class Aws::SageMaker::Client
An API client for SageMaker
. To construct a client, you need to configure a `:region` and `:credentials`.
client = Aws::SageMaker::Client.new( region: region_name, credentials: credentials, # ... )
For details on configuring region and credentials see the [developer guide](/sdk-for-ruby/v3/developer-guide/setup-config.html).
See {#initialize} for a full list of supported configuration options.
Attributes
@api private
Public Class Methods
@overload initialize(options)
@param [Hash] options @option options [required, Aws::CredentialProvider] :credentials Your AWS credentials. This can be an instance of any one of the following classes: * `Aws::Credentials` - Used for configuring static, non-refreshing credentials. * `Aws::SharedCredentials` - Used for loading static credentials from a shared file, such as `~/.aws/config`. * `Aws::AssumeRoleCredentials` - Used when you need to assume a role. * `Aws::AssumeRoleWebIdentityCredentials` - Used when you need to assume a role after providing credentials via the web. * `Aws::SSOCredentials` - Used for loading credentials from AWS SSO using an access token generated from `aws login`. * `Aws::ProcessCredentials` - Used for loading credentials from a process that outputs to stdout. * `Aws::InstanceProfileCredentials` - Used for loading credentials from an EC2 IMDS on an EC2 instance. * `Aws::ECSCredentials` - Used for loading credentials from instances running in ECS. * `Aws::CognitoIdentityCredentials` - Used for loading credentials from the Cognito Identity service. When `:credentials` are not configured directly, the following locations will be searched for credentials: * `Aws.config[:credentials]` * The `:access_key_id`, `:secret_access_key`, and `:session_token` options. * ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY'] * `~/.aws/credentials` * `~/.aws/config` * EC2/ECS IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of `Aws::InstanceProfileCredentails` or `Aws::ECSCredentials` to enable retries and extended timeouts. @option options [required, String] :region The AWS region to connect to. The configured `:region` is used to determine the service `:endpoint`. When not passed, a default `:region` is searched for in the following locations: * `Aws.config[:region]` * `ENV['AWS_REGION']` * `ENV['AMAZON_REGION']` * `ENV['AWS_DEFAULT_REGION']` * `~/.aws/credentials` * `~/.aws/config` @option options [String] :access_key_id @option options [Boolean] :active_endpoint_cache (false) When set to `true`, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to `false`. @option options [Boolean] :adaptive_retry_wait_to_fill (true) Used only in `adaptive` retry mode. When true, the request will sleep until there is sufficent client side capacity to retry the request. When false, the request will raise a `RetryCapacityNotAvailableError` and will not retry instead of sleeping. @option options [Boolean] :client_side_monitoring (false) When `true`, client-side metrics will be collected for all API requests from this client. @option options [String] :client_side_monitoring_client_id ("") Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string. @option options [String] :client_side_monitoring_host ("127.0.0.1") Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP. @option options [Integer] :client_side_monitoring_port (31000) Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP. @option options [Aws::ClientSideMonitoring::Publisher] :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher. @option options [Boolean] :convert_params (true) When `true`, an attempt is made to coerce request parameters into the required types. @option options [Boolean] :correct_clock_skew (true) Used only in `standard` and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks. @option options [Boolean] :disable_host_prefix_injection (false) Set to true to disable SDK automatically adding host prefix to default service endpoint when available. @option options [String] :endpoint The client endpoint is normally constructed from the `:region` option. You should only configure an `:endpoint` when connecting to test or custom endpoints. This should be a valid HTTP(S) URI. @option options [Integer] :endpoint_cache_max_entries (1000) Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000. @option options [Integer] :endpoint_cache_max_threads (10) Used for the maximum threads in use for polling endpoints to be cached, defaults to 10. @option options [Integer] :endpoint_cache_poll_interval (60) When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec. @option options [Boolean] :endpoint_discovery (false) When set to `true`, endpoint discovery will be enabled for operations when available. @option options [Aws::Log::Formatter] :log_formatter (Aws::Log::Formatter.default) The log formatter. @option options [Symbol] :log_level (:info) The log level to send messages to the `:logger` at. @option options [Logger] :logger The Logger instance to send log messages to. If this option is not set, logging will be disabled. @option options [Integer] :max_attempts (3) An integer representing the maximum number attempts that will be made for a single request, including the initial attempt. For example, setting this value to 5 will result in a request being retried up to 4 times. Used in `standard` and `adaptive` retry modes. @option options [String] :profile ("default") Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used. @option options [Proc] :retry_backoff A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the `legacy` retry mode. @option options [Float] :retry_base_delay (0.3) The base delay in seconds used by the default backoff function. This option is only used in the `legacy` retry mode. @option options [Symbol] :retry_jitter (:none) A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. This option is only used in the `legacy` retry mode. @see https://www.awsarchitectureblog.com/2015/03/backoff.html @option options [Integer] :retry_limit (3) The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors, auth errors, endpoint discovery, and errors from expired credentials. This option is only used in the `legacy` retry mode. @option options [Integer] :retry_max_delay (0) The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. This option is only used in the `legacy` retry mode. @option options [String] :retry_mode ("legacy") Specifies which retry algorithm to use. Values are: * `legacy` - The pre-existing retry behavior. This is default value if no retry mode is provided. * `standard` - A standardized set of retry rules across the AWS SDKs. This includes support for retry quotas, which limit the number of unsuccessful retries a client can make. * `adaptive` - An experimental retry mode that includes all the functionality of `standard` mode along with automatic client side throttling. This is a provisional mode that may change behavior in the future. @option options [String] :secret_access_key @option options [String] :session_token @option options [Boolean] :simple_json (false) Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures. When `:simple_json` is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects. @option options [Boolean] :stub_responses (false) Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling {ClientStubs#stub_responses}. See {ClientStubs} for more information. ** Please note ** When response stubbing is enabled, no HTTP requests are made, and retries are disabled. @option options [Boolean] :validate_params (true) When `true`, request parameters are validated before sending the request. @option options [URI::HTTP,String] :http_proxy A proxy to send requests through. Formatted like 'http://proxy.com:123'. @option options [Float] :http_open_timeout (15) The number of seconds to wait when opening a HTTP session before raising a `Timeout::Error`. @option options [Integer] :http_read_timeout (60) The default number of seconds to wait for response data. This value can safely be set per-request on the session. @option options [Float] :http_idle_timeout (5) The number of seconds a connection is allowed to sit idle before it is considered stale. Stale connections are closed and removed from the pool before making a request. @option options [Float] :http_continue_timeout (1) The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has "Expect" header set to "100-continue". Defaults to `nil` which disables this behaviour. This value can safely be set per request on the session. @option options [Boolean] :http_wire_trace (false) When `true`, HTTP debug output will be sent to the `:logger`. @option options [Boolean] :ssl_verify_peer (true) When `true`, SSL peer certificates are verified when establishing a connection. @option options [String] :ssl_ca_bundle Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass `:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available. @option options [String] :ssl_ca_directory Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass `:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available.
# File lib/aws-sdk-sagemaker/client.rb, line 334 def initialize(*args) super end
Private Class Methods
@api private
# File lib/aws-sdk-sagemaker/client.rb, line 18132 def errors_module Errors end
Public Instance Methods
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see [Amazon SageMaker
ML Lineage Tracking].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html
@option params [required, String] :source_arn
The ARN of the source.
@option params [required, String] :destination_arn
The Amazon Resource Name (ARN) of the destination.
@option params [String] :association_type
The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use. * ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job. * AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment. * DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs. * Produced - The source generated the destination. For example, a training job produced a model artifact.
@return [Types::AddAssociationResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::AddAssociationResponse#source_arn #source_arn} => String * {Types::AddAssociationResponse#destination_arn #destination_arn} => String
@example Request syntax with placeholder values
resp = client.add_association({ source_arn: "AssociationEntityArn", # required destination_arn: "AssociationEntityArn", # required association_type: "ContributedTo", # accepts ContributedTo, AssociatedWith, DerivedFrom, Produced })
@example Response structure
resp.source_arn #=> String resp.destination_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/AddAssociation AWS API Documentation
@overload add_association
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 396 def add_association(params = {}, options = {}) req = build_request(:add_association, params) req.send_request(options) end
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
@option params [required, String] :trial_component_name
The name of the component to associated with the trial.
@option params [required, String] :trial_name
The name of the trial to associate with.
@return [Types::AssociateTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::AssociateTrialComponentResponse#trial_component_arn #trial_component_arn} => String * {Types::AssociateTrialComponentResponse#trial_arn #trial_arn} => String
@example Request syntax with placeholder values
resp = client.associate_trial_component({ trial_component_name: "ExperimentEntityName", # required trial_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_component_arn #=> String resp.trial_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/AssociateTrialComponent AWS API Documentation
@overload associate_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 512 def associate_trial_component(params = {}, options = {}) req = build_request(:associate_trial_component, params) req.send_request(options) end
@param params ({}) @api private
# File lib/aws-sdk-sagemaker/client.rb, line 17970 def build_request(operation_name, params = {}) handlers = @handlers.for(operation_name) context = Seahorse::Client::RequestContext.new( operation_name: operation_name, operation: config.api.operation(operation_name), client: self, params: params, config: config) context[:gem_name] = 'aws-sdk-sagemaker' context[:gem_version] = '1.99.0' Seahorse::Client::Request.new(handlers, context) end
Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see [Amazon SageMaker
ML Lineage Tracking].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html
@option params [required, String] :action_name
The name of the action. Must be unique to your account in an Amazon Web Services Region.
@option params [required, Types::ActionSource] :source
The source type, ID, and URI.
@option params [required, String] :action_type
The action type.
@option params [String] :description
The description of the action.
@option params [String] :status
The status of the action.
@option params [Hash<String,String>] :properties
A list of properties to add to the action.
@option params [Types::MetadataProperties] :metadata_properties
Metadata properties of the tracking entity, trial, or trial component.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the action.
@return [Types::CreateActionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateActionResponse#action_arn #action_arn} => String
@example Request syntax with placeholder values
resp = client.create_action({ action_name: "ExperimentEntityName", # required source: { # required source_uri: "String2048", # required source_type: "String256", source_id: "String256", }, action_type: "String256", # required description: "ExperimentDescription", status: "Unknown", # accepts Unknown, InProgress, Completed, Failed, Stopping, Stopped properties: { "StringParameterValue" => "StringParameterValue", }, metadata_properties: { commit_id: "MetadataPropertyValue", repository: "MetadataPropertyValue", generated_by: "MetadataPropertyValue", project_id: "MetadataPropertyValue", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.action_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateAction AWS API Documentation
@overload create_action
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 593 def create_action(params = {}, options = {}) req = build_request(:create_action, params) req.send_request(options) end
Create a machine learning algorithm that you can use in Amazon SageMaker
and list in the Amazon Web Services Marketplace.
@option params [required, String] :algorithm_name
The name of the algorithm.
@option params [String] :algorithm_description
A description of the algorithm.
@option params [required, Types::TrainingSpecification] :training_specification
Specifies details about training jobs run by this algorithm, including the following: * The Amazon ECR path of the container and the version digest of the algorithm. * The hyperparameters that the algorithm supports. * The instance types that the algorithm supports for training. * Whether the algorithm supports distributed training. * The metrics that the algorithm emits to Amazon CloudWatch. * Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs. * The input channels that the algorithm supports for training data. For example, an algorithm might support `train`, `validation`, and `test` channels.
@option params [Types::InferenceSpecification] :inference_specification
Specifies details about inference jobs that the algorithm runs, including the following: * The Amazon ECR paths of containers that contain the inference code and model artifacts. * The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference. * The input and output content formats that the algorithm supports for inference.
@option params [Types::AlgorithmValidationSpecification] :validation_specification
Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
@option params [Boolean] :certify_for_marketplace
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateAlgorithmOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateAlgorithmOutput#algorithm_arn #algorithm_arn} => String
@example Request syntax with placeholder values
resp = client.create_algorithm({ algorithm_name: "EntityName", # required algorithm_description: "EntityDescription", training_specification: { # required training_image: "ContainerImage", # required training_image_digest: "ImageDigest", supported_hyper_parameters: [ { name: "ParameterName", # required description: "EntityDescription", type: "Integer", # required, accepts Integer, Continuous, Categorical, FreeText range: { integer_parameter_range_specification: { min_value: "ParameterValue", # required max_value: "ParameterValue", # required }, continuous_parameter_range_specification: { min_value: "ParameterValue", # required max_value: "ParameterValue", # required }, categorical_parameter_range_specification: { values: ["ParameterValue"], # required }, }, is_tunable: false, is_required: false, default_value: "HyperParameterValue", }, ], supported_training_instance_types: ["ml.m4.xlarge"], # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge supports_distributed_training: false, metric_definitions: [ { name: "MetricName", # required regex: "MetricRegex", # required }, ], training_channels: [ # required { name: "ChannelName", # required description: "EntityDescription", is_required: false, supported_content_types: ["ContentType"], # required supported_compression_types: ["None"], # accepts None, Gzip supported_input_modes: ["Pipe"], # required, accepts Pipe, File }, ], supported_tuning_job_objective_metrics: [ { type: "Maximize", # required, accepts Maximize, Minimize metric_name: "MetricName", # required }, ], }, inference_specification: { containers: [ # required { container_hostname: "ContainerHostname", image: "ContainerImage", # required image_digest: "ImageDigest", model_data_url: "Url", product_id: "ProductId", environment: { "EnvironmentKey" => "EnvironmentValue", }, }, ], supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge supported_content_types: ["ContentType"], # required supported_response_mime_types: ["ResponseMIMEType"], # required }, validation_specification: { validation_role: "RoleArn", # required validation_profiles: [ # required { profile_name: "EntityName", # required training_job_definition: { # required training_input_mode: "Pipe", # required, accepts Pipe, File hyper_parameters: { "HyperParameterKey" => "HyperParameterValue", }, input_data_config: [ # required { channel_name: "ChannelName", # required data_source: { # required s3_data_source: { s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key attribute_names: ["AttributeName"], }, file_system_data_source: { file_system_id: "FileSystemId", # required file_system_access_mode: "rw", # required, accepts rw, ro file_system_type: "EFS", # required, accepts EFS, FSxLustre directory_path: "DirectoryPath", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip record_wrapper_type: "None", # accepts None, RecordIO input_mode: "Pipe", # accepts Pipe, File shuffle_config: { seed: 1, # required }, }, ], output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, resource_config: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge instance_count: 1, # required volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, }, transform_job_definition: { max_concurrent_transforms: 1, max_payload_in_mb: 1, batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord environment: { "TransformEnvironmentKey" => "TransformEnvironmentValue", }, transform_input: { # required data_source: { # required s3_data_source: { # required s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip split_type: "None", # accepts None, Line, RecordIO, TFRecord }, transform_output: { # required s3_output_path: "S3Uri", # required accept: "Accept", assemble_with: "None", # accepts None, Line kms_key_id: "KmsKeyId", }, transform_resources: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge instance_count: 1, # required volume_kms_key_id: "KmsKeyId", }, }, }, ], }, certify_for_marketplace: false, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.algorithm_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateAlgorithm AWS API Documentation
@overload create_algorithm
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 841 def create_algorithm(params = {}, options = {}) req = build_request(:create_algorithm, params) req.send_request(options) end
Creates a running app for the specified UserProfile. Supported apps are `JupyterServer` and `KernelGateway`. This operation is automatically invoked by Amazon SageMaker
Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name.
@option params [required, String] :app_type
The type of app. Supported apps are `JupyterServer` and `KernelGateway`. `TensorBoard` is not supported.
@option params [required, String] :app_name
The name of the app.
@option params [Array<Types::Tag>] :tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
@option params [Types::ResourceSpec] :resource_spec
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
@return [Types::CreateAppResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateAppResponse#app_arn #app_arn} => String
@example Request syntax with placeholder values
resp = client.create_app({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard app_name: "AppName", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, })
@example Response structure
resp.app_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateApp AWS API Documentation
@overload create_app
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 906 def create_app(params = {}, options = {}) req = build_request(:create_app, params) req.send_request(options) end
Creates a configuration for running a SageMaker
image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
@option params [required, String] :app_image_config_name
The name of the AppImageConfig. Must be unique to your account.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the AppImageConfig.
@option params [Types::KernelGatewayImageConfig] :kernel_gateway_image_config
The KernelGatewayImageConfig.
@return [Types::CreateAppImageConfigResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateAppImageConfigResponse#app_image_config_arn #app_image_config_arn} => String
@example Request syntax with placeholder values
resp = client.create_app_image_config({ app_image_config_name: "AppImageConfigName", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], kernel_gateway_image_config: { kernel_specs: [ # required { name: "KernelName", # required display_name: "KernelDisplayName", }, ], file_system_config: { mount_path: "MountPath", default_uid: 1, default_gid: 1, }, }, })
@example Response structure
resp.app_image_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateAppImageConfig AWS API Documentation
@overload create_app_image_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 962 def create_app_image_config(params = {}, options = {}) req = build_request(:create_app_image_config, params) req.send_request(options) end
Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see [Amazon SageMaker
ML Lineage Tracking].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html
@option params [String] :artifact_name
The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
@option params [required, Types::ArtifactSource] :source
The ID, ID type, and URI of the source.
@option params [required, String] :artifact_type
The artifact type.
@option params [Hash<String,String>] :properties
A list of properties to add to the artifact.
@option params [Types::MetadataProperties] :metadata_properties
Metadata properties of the tracking entity, trial, or trial component.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the artifact.
@return [Types::CreateArtifactResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateArtifactResponse#artifact_arn #artifact_arn} => String
@example Request syntax with placeholder values
resp = client.create_artifact({ artifact_name: "ExperimentEntityName", source: { # required source_uri: "String2048", # required source_types: [ { source_id_type: "MD5Hash", # required, accepts MD5Hash, S3ETag, S3Version, Custom value: "String256", # required }, ], }, artifact_type: "String256", # required properties: { "StringParameterValue" => "StringParameterValue", }, metadata_properties: { commit_id: "MetadataPropertyValue", repository: "MetadataPropertyValue", generated_by: "MetadataPropertyValue", project_id: "MetadataPropertyValue", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.artifact_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateArtifact AWS API Documentation
@overload create_artifact
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1038 def create_artifact(params = {}, options = {}) req = build_request(:create_artifact, params) req.send_request(options) end
Creates an Autopilot job.
Find the best-performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see [Automate Model Development with Amazon SageMaker
Autopilot].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html
@option params [required, String] :auto_ml_job_name
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
@option params [required, Array<Types::AutoMLChannel>] :input_data_config
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to `InputDataConfig` supported by . Format(s) supported: CSV. Minimum of 500 rows.
@option params [required, Types::AutoMLOutputDataConfig] :output_data_config
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
@option params [String] :problem_type
Defines the type of supervised learning available for the candidates. Options include: `BinaryClassification`, `MulticlassClassification`, and `Regression`. For more information, see [ Amazon SageMaker Autopilot problem types and algorithm support][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html
@option params [Types::AutoMLJobObjective] :auto_ml_job_objective
Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.
@option params [Types::AutoMLJobConfig] :auto_ml_job_config
Contains `CompletionCriteria` and `SecurityConfig` settings for the AutoML job.
@option params [required, String] :role_arn
The ARN of the role that is used to access the data.
@option params [Boolean] :generate_candidate_definitions_only
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
@option params [Array<Types::Tag>] :tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
@option params [Types::ModelDeployConfig] :model_deploy_config
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
@return [Types::CreateAutoMLJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateAutoMLJobResponse#auto_ml_job_arn #auto_ml_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_auto_ml_job({ auto_ml_job_name: "AutoMLJobName", # required input_data_config: [ # required { data_source: { # required s3_data_source: { # required s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix s3_uri: "S3Uri", # required }, }, compression_type: "None", # accepts None, Gzip target_attribute_name: "TargetAttributeName", # required }, ], output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression auto_ml_job_objective: { metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC }, auto_ml_job_config: { completion_criteria: { max_candidates: 1, max_runtime_per_training_job_in_seconds: 1, max_auto_ml_job_runtime_in_seconds: 1, }, security_config: { volume_kms_key_id: "KmsKeyId", enable_inter_container_traffic_encryption: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, }, role_arn: "RoleArn", # required generate_candidate_definitions_only: false, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], model_deploy_config: { auto_generate_endpoint_name: false, endpoint_name: "EndpointName", }, })
@example Response structure
resp.auto_ml_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateAutoMLJob AWS API Documentation
@overload create_auto_ml_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1170 def create_auto_ml_job(params = {}, options = {}) req = build_request(:create_auto_ml_job, params) req.send_request(options) end
Creates a Git repository as a resource in your Amazon SageMaker
account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker
account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in [Amazon Web Services CodeCommit] or in any other Git repository.
[1]: docs.aws.amazon.com/codecommit/latest/userguide/welcome.html
@option params [required, String] :code_repository_name
The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
@option params [required, Types::GitConfig] :git_config
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateCodeRepositoryOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateCodeRepositoryOutput#code_repository_arn #code_repository_arn} => String
@example Request syntax with placeholder values
resp = client.create_code_repository({ code_repository_name: "EntityName", # required git_config: { # required repository_url: "GitConfigUrl", # required branch: "Branch", secret_arn: "SecretArn", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.code_repository_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateCodeRepository AWS API Documentation
@overload create_code_repository
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1238 def create_code_repository(params = {}, options = {}) req = build_request(:create_code_repository, params) req.send_request(options) end
Starts a model compilation job. After the model has been compiled, Amazon SageMaker
saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker
hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
-
A name for the compilation job
-
Information about the input model artifacts
-
The output location for the compiled model and the device (target) that the model runs on
-
The Amazon
Resource
Name (ARN) of the IAM role that AmazonSageMaker
assumes to perform the model compilation job.
You can also provide a `Tag` to track the model compilation job's resource use and costs. The response body contains the `CompilationJobArn` for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
@option params [required, String] :compilation_job_name
A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf. During model compilation, Amazon SageMaker needs your permission to: * Read input data from an S3 bucket * Write model artifacts to an S3 bucket * Write logs to Amazon CloudWatch Logs * Publish metrics to Amazon CloudWatch You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the `iam:PassRole` permission. For more information, see [Amazon SageMaker Roles.][1] [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html
@option params [required, Types::InputConfig] :input_config
Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
@option params [required, Types::OutputConfig] :output_config
Provides information about the output location for the compiled model and the target device the model runs on.
@option params [Types::NeoVpcConfig] :vpc_config
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see [Protect Compilation Jobs by Using an Amazon Virtual Private Cloud][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/neo-vpc.html
@option params [required, Types::StoppingCondition] :stopping_condition
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateCompilationJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateCompilationJobResponse#compilation_job_arn #compilation_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_compilation_job({ compilation_job_name: "EntityName", # required role_arn: "RoleArn", # required input_config: { # required s3_uri: "S3Uri", # required data_input_config: "DataInputConfig", # required framework: "TENSORFLOW", # required, accepts TENSORFLOW, KERAS, MXNET, ONNX, PYTORCH, XGBOOST, TFLITE, DARKNET, SKLEARN framework_version: "FrameworkVersion", }, output_config: { # required s3_output_location: "S3Uri", # required target_device: "lambda", # accepts lambda, ml_m4, ml_m5, ml_c4, ml_c5, ml_p2, ml_p3, ml_g4dn, ml_inf1, ml_eia2, jetson_tx1, jetson_tx2, jetson_nano, jetson_xavier, rasp3b, imx8qm, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603, sitara_am57x, amba_cv22, amba_cv25, x86_win32, x86_win64, coreml, jacinto_tda4vm, imx8mplus target_platform: { os: "ANDROID", # required, accepts ANDROID, LINUX arch: "X86_64", # required, accepts X86_64, X86, ARM64, ARM_EABI, ARM_EABIHF accelerator: "INTEL_GRAPHICS", # accepts INTEL_GRAPHICS, MALI, NVIDIA }, compiler_options: "CompilerOptions", kms_key_id: "KmsKeyId", }, vpc_config: { security_group_ids: ["NeoVpcSecurityGroupId"], # required subnets: ["NeoVpcSubnetId"], # required }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.compilation_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateCompilationJob AWS API Documentation
@overload create_compilation_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1385 def create_compilation_job(params = {}, options = {}) req = build_request(:create_compilation_job, params) req.send_request(options) end
Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see [Amazon SageMaker
ML Lineage Tracking].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html
@option params [required, String] :context_name
The name of the context. Must be unique to your account in an Amazon Web Services Region.
@option params [required, Types::ContextSource] :source
The source type, ID, and URI.
@option params [required, String] :context_type
The context type.
@option params [String] :description
The description of the context.
@option params [Hash<String,String>] :properties
A list of properties to add to the context.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the context.
@return [Types::CreateContextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateContextResponse#context_arn #context_arn} => String
@example Request syntax with placeholder values
resp = client.create_context({ context_name: "ExperimentEntityName", # required source: { # required source_uri: "String2048", # required source_type: "String256", source_id: "String256", }, context_type: "String256", # required description: "ExperimentDescription", properties: { "StringParameterValue" => "StringParameterValue", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.context_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateContext AWS API Documentation
@overload create_context
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1452 def create_context(params = {}, options = {}) req = build_request(:create_context, params) req.send_request(options) end
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see [Amazon SageMaker
Model Monitor].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html
@option params [required, String] :job_definition_name
The name for the monitoring job definition.
@option params [Types::DataQualityBaselineConfig] :data_quality_baseline_config
Configures the constraints and baselines for the monitoring job.
@option params [required, Types::DataQualityAppSpecification] :data_quality_app_specification
Specifies the container that runs the monitoring job.
@option params [required, Types::DataQualityJobInput] :data_quality_job_input
A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
@option params [required, Types::MonitoringOutputConfig] :data_quality_job_output_config
The output configuration for monitoring jobs.
@option params [required, Types::MonitoringResources] :job_resources
Identifies the resources to deploy for a monitoring job.
@option params [Types::MonitoringNetworkConfig] :network_config
Specifies networking configuration for the monitoring job.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
@option params [Types::MonitoringStoppingCondition] :stopping_condition
A time limit for how long the monitoring job is allowed to run before stopping.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL
@return [Types::CreateDataQualityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateDataQualityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String
@example Request syntax with placeholder values
resp = client.create_data_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required data_quality_baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, statistics_resource: { s3_uri: "S3Uri", }, }, data_quality_app_specification: { # required image_uri: "ImageUri", # required container_entrypoint: ["ContainerEntrypointString"], container_arguments: ["ContainerArgument"], record_preprocessor_source_uri: "S3Uri", post_analytics_processor_source_uri: "S3Uri", environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, }, data_quality_job_input: { # required endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, }, data_quality_job_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, job_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required stopping_condition: { max_runtime_in_seconds: 1, # required }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.job_definition_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateDataQualityJobDefinition AWS API Documentation
@overload create_data_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1593 def create_data_quality_job_definition(params = {}, options = {}) req = build_request(:create_data_quality_job_definition, params) req.send_request(options) end
Creates a device fleet.
@option params [required, String] :device_fleet_name
The name of the fleet that the device belongs to.
@option params [String] :role_arn
The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
@option params [String] :description
A description of the fleet.
@option params [required, Types::EdgeOutputConfig] :output_config
The output configuration for storing sample data collected by the fleet.
@option params [Array<Types::Tag>] :tags
Creates tags for the specified fleet.
@option params [Boolean] :enable_iot_role_alias
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-\\\{DeviceFleetName\\}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.create_device_fleet({ device_fleet_name: "EntityName", # required role_arn: "RoleArn", description: "DeviceFleetDescription", output_config: { # required s3_output_location: "S3Uri", # required kms_key_id: "KmsKeyId", preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component preset_deployment_config: "String", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], enable_iot_role_alias: false, })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateDeviceFleet AWS API Documentation
@overload create_device_fleet
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1652 def create_device_fleet(params = {}, options = {}) req = build_request(:create_device_fleet, params) req.send_request(options) end
Creates a `Domain` used by Amazon SageMaker
Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
**EFS storage**
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker
uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see [Protect Data at Rest Using Encryption].
**VPC configuration**
All SageMaker
Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the `AppNetworkAccessType` parameter. `AppNetworkAccessType` corresponds to the network access type that you choose when you onboard to Studio. The following options are available:
-
`PublicInternetOnly` - Non-EFS traffic goes through a VPC managed by Amazon
SageMaker
, which allows internet access. This is the default value. -
`VpcOnly` - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.
When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the
SageMaker
API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker
Studio app successfully.
For more information, see [Connect SageMaker
Studio Notebooks to Resources in a VPC].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/studio-notebooks-and-internet-access.html
@option params [required, String] :domain_name
A name for the domain.
@option params [required, String] :auth_mode
The mode of authentication that members use to access the domain.
@option params [required, Types::UserSettings] :default_user_settings
The default settings to use to create a user profile when `UserSettings` isn't specified in the call to the `CreateUserProfile` API. `SecurityGroups` is aggregated when specified in both calls. For all other settings in `UserSettings`, the values specified in `CreateUserProfile` take precedence over those specified in `CreateDomain`.
@option params [required, Array<String>] :subnet_ids
The VPC subnets that Studio uses for communication.
@option params [required, String] :vpc_id
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
@option params [Array<Types::Tag>] :tags
Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the `Search` API. Tags that you specify for the Domain are also added to all Apps that the Domain launches.
@option params [String] :app_network_access_type
Specifies the VPC used for non-EFS traffic. The default value is `PublicInternetOnly`. * `PublicInternetOnly` - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access * `VpcOnly` - All Studio traffic is through the specified VPC and subnets
@option params [String] :home_efs_file_system_kms_key_id
This member is deprecated and replaced with `KmsKeyId`.
@option params [String] :kms_key_id
SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed customer master key (CMK) by default. For more control, specify a customer managed CMK.
@return [Types::CreateDomainResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateDomainResponse#domain_arn #domain_arn} => String * {Types::CreateDomainResponse#url #url} => String
@example Request syntax with placeholder values
resp = client.create_domain({ domain_name: "DomainName", # required auth_mode: "SSO", # required, accepts SSO, IAM default_user_settings: { # required execution_role: "RoleArn", security_groups: ["SecurityGroupId"], sharing_settings: { notebook_output_option: "Allowed", # accepts Allowed, Disabled s3_output_path: "S3Uri", s3_kms_key_id: "KmsKeyId", }, jupyter_server_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, kernel_gateway_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, custom_images: [ { image_name: "ImageName", # required image_version_number: 1, app_image_config_name: "AppImageConfigName", # required }, ], lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, tensor_board_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, }, }, subnet_ids: ["SubnetId"], # required vpc_id: "VpcId", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], app_network_access_type: "PublicInternetOnly", # accepts PublicInternetOnly, VpcOnly home_efs_file_system_kms_key_id: "KmsKeyId", kms_key_id: "KmsKeyId", })
@example Response structure
resp.domain_arn #=> String resp.url #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateDomain AWS API Documentation
@overload create_domain
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1835 def create_domain(params = {}, options = {}) req = build_request(:create_domain, params) req.send_request(options) end
Starts a SageMaker
Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker
saves the resulting artifacts to an S3 bucket that you specify.
@option params [required, String] :edge_packaging_job_name
The name of the edge packaging job.
@option params [required, String] :compilation_job_name
The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
@option params [required, String] :model_name
The name of the model.
@option params [required, String] :model_version
The version of the model.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
@option params [required, Types::EdgeOutputConfig] :output_config
Provides information about the output location for the packaged model.
@option params [String] :resource_key
The CMK to use when encrypting the EBS volume the edge packaging job runs on.
@option params [Array<Types::Tag>] :tags
Creates tags for the packaging job.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.create_edge_packaging_job({ edge_packaging_job_name: "EntityName", # required compilation_job_name: "EntityName", # required model_name: "EntityName", # required model_version: "EdgeVersion", # required role_arn: "RoleArn", # required output_config: { # required s3_output_location: "S3Uri", # required kms_key_id: "KmsKeyId", preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component preset_deployment_config: "String", }, resource_key: "KmsKeyId", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateEdgePackagingJob AWS API Documentation
@overload create_edge_packaging_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 1902 def create_edge_packaging_job(params = {}, options = {}) req = build_request(:create_edge_packaging_job, params) req.send_request(options) end
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker
uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using Amazon SageMaker
hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker
hosting services, see the [Create Endpoint example notebook.]
<note markdown=“1”> You must not delete an `EndpointConfig` that is in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. To update an endpoint, you must create a new `EndpointConfig`.
</note>
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, Amazon SageMaker
creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
<note markdown=“1”> When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting [ `Eventually Consistent Reads` ][2], the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
</note>
When Amazon SageMaker
receives the request, it sets the endpoint status to `Creating`. After it creates the endpoint, it sets the status to `InService`. Amazon SageMaker
can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker
uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see [Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region] in the *Amazon Web Services Identity and Access Management User Guide*.
<note markdown=“1”> To add the IAM role policies for using this API operation, go to the [IAM console], and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
* Option 1: For a full Amazon SageMaker access, search and attach the `AmazonSageMakerFullAccess` policy.
-
Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
`“Action”: [“sagemaker:CreateEndpoint”, “sagemaker:CreateEndpointConfig”]`
`“Resource”: [`
`“arn:aws:sagemaker:region:account-id:endpoint/endpointName”`
`“arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName”`
`]`
For more information, see [Amazon
SageMaker
API Permissions: Actions, Permissions, and Resources Reference].
</note>
[1]: github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-fundamentals/create-endpoint/create_endpoint.ipynb [2]: docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html [3]: docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html [4]: console.aws.amazon.com/iam/ [5]: docs.aws.amazon.com/sagemaker/latest/dg/api-permissions-reference.html
@option params [required, String] :endpoint_name
The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in `CreateEndpoint`, but the case is preserved and must be matched in .
@option params [required, String] :endpoint_config_name
The name of an endpoint configuration. For more information, see CreateEndpointConfig.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateEndpointOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateEndpointOutput#endpoint_arn #endpoint_arn} => String
@example Request syntax with placeholder values
resp = client.create_endpoint({ endpoint_name: "EndpointName", # required endpoint_config_name: "EndpointConfigName", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.endpoint_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateEndpoint AWS API Documentation
@overload create_endpoint
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2044 def create_endpoint(params = {}, options = {}) req = build_request(:create_endpoint, params) req.send_request(options) end
Creates an endpoint configuration that Amazon SageMaker
hosting services uses to deploy models. In the configuration, you identify one or more models, created using the `CreateModel` API, to deploy and the resources that you want Amazon SageMaker
to provision. Then you call the CreateEndpoint API.
<note markdown=“1”> Use this API if you want to use Amazon SageMaker
hosting services to deploy models into production.
</note>
In the request, you define a `ProductionVariant`, for each model that you want to deploy. Each `ProductionVariant` parameter also describes the resources that you want Amazon SageMaker
to provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a `VariantWeight` to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker
distributes two-thirds of the traffic to Model A, and one-third to model B.
<note markdown=“1”> When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting [ `Eventually Consistent Reads` ][1], the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
</note>
[1]: docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html
@option params [required, String] :endpoint_config_name
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
@option params [required, Array<Types::ProductionVariant>] :production_variants
An list of `ProductionVariant` objects, one for each model that you want to host at this endpoint.
@option params [Types::DataCaptureConfig] :data_capture_config
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@option params [String] :kms_key_id
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKeyId can be any of the following formats: * Key ID: `1234abcd-12ab-34cd-56ef-1234567890ab` * Key ARN: `arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab` * Alias name: `alias/ExampleAlias` * Alias name ARN: `arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias` The KMS key policy must grant permission to the IAM role that you specify in your `CreateEndpoint`, `UpdateEndpoint` requests. For more information, refer to the Amazon Web Services Key Management Service section[ Using Key Policies in Amazon Web Services KMS ][1] <note markdown="1"> Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a `KmsKeyId` when using an instance type with local storage. If any of the models that you specify in the `ProductionVariants` parameter use nitro-based instances with local storage, do not specify a value for the `KmsKeyId` parameter. If you specify a value for `KmsKeyId` when using any nitro-based instances with local storage, the call to `CreateEndpointConfig` fails. For a list of instance types that support local instance storage, see [Instance Store Volumes][2]. For more information about local instance storage encryption, see [SSD Instance Store Volumes][3]. </note> [1]: https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html [2]: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes [3]: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html
@option params [Types::AsyncInferenceConfig] :async_inference_config
Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using [ `InvokeEndpointAsync` ][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html
@return [Types::CreateEndpointConfigOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateEndpointConfigOutput#endpoint_config_arn #endpoint_config_arn} => String
@example Request syntax with placeholder values
resp = client.create_endpoint_config({ endpoint_config_name: "EndpointConfigName", # required production_variants: [ # required { variant_name: "VariantName", # required model_name: "ModelName", # required initial_instance_count: 1, # required instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge initial_variant_weight: 1.0, accelerator_type: "ml.eia1.medium", # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge core_dump_config: { destination_s3_uri: "DestinationS3Uri", # required kms_key_id: "KmsKeyId", }, }, ], data_capture_config: { enable_capture: false, initial_sampling_percentage: 1, # required destination_s3_uri: "DestinationS3Uri", # required kms_key_id: "KmsKeyId", capture_options: [ # required { capture_mode: "Input", # required, accepts Input, Output }, ], capture_content_type_header: { csv_content_types: ["CsvContentType"], json_content_types: ["JsonContentType"], }, }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], kms_key_id: "KmsKeyId", async_inference_config: { client_config: { max_concurrent_invocations_per_instance: 1, }, output_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "DestinationS3Uri", # required notification_config: { success_topic: "SnsTopicArn", error_topic: "SnsTopicArn", }, }, }, })
@example Response structure
resp.endpoint_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateEndpointConfig AWS API Documentation
@overload create_endpoint_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2232 def create_endpoint_config(params = {}, options = {}) req = build_request(:create_endpoint_config, params) req.send_request(options) end
Creates an SageMaker
experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called *trial components*, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker
Studio or the SageMaker
Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional `Description` parameter. To add a description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
@option params [required, String] :experiment_name
The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
@option params [String] :display_name
The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify `DisplayName`, the value in `ExperimentName` is displayed.
@option params [String] :description
The description of the experiment.
@option params [Array<Types::Tag>] :tags
A list of tags to associate with the experiment. You can use Search API to search on the tags.
@return [Types::CreateExperimentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateExperimentResponse#experiment_arn #experiment_arn} => String
@example Request syntax with placeholder values
resp = client.create_experiment({ experiment_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", description: "ExperimentDescription", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.experiment_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateExperiment AWS API Documentation
@overload create_experiment
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2306 def create_experiment(params = {}, options = {}) req = build_request(:create_experiment, params) req.send_request(options) end
Create a new `FeatureGroup`. A `FeatureGroup` is a group of `Features` defined in the `FeatureStore` to describe a `Record`.
The `FeatureGroup` defines the schema and features contained in the FeatureGroup. A `FeatureGroup` definition is composed of a list of `Features`, a `RecordIdentifierFeatureName`, an `EventTimeFeatureName` and configurations for its `OnlineStore` and `OfflineStore`. Check
- Amazon Web Services service quotas][1
-
to see the `FeatureGroup`s
quota for your Amazon Web Services account.
You must include at least one of `OnlineStoreConfig` and `OfflineStoreConfig` to create a `FeatureGroup`.
[1]: docs.aws.amazon.com/general/latest/gr/aws_service_limits.html
@option params [required, String] :feature_group_name
The name of the `FeatureGroup`. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name: * Must start and end with an alphanumeric character. * Can only contain alphanumeric character and hyphens. Spaces are not allowed.
@option params [required, String] :record_identifier_feature_name
The name of the `Feature` whose value uniquely identifies a `Record` defined in the `FeatureStore`. Only the latest record per identifier value will be stored in the `OnlineStore`. `RecordIdentifierFeatureName` must be one of feature definitions' names. You use the `RecordIdentifierFeatureName` to access data in a `FeatureStore`. This name: * Must start and end with an alphanumeric character. * Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
@option params [required, String] :event_time_feature_name
The name of the feature that stores the `EventTime` of a `Record` in a `FeatureGroup`. An `EventTime` is a point in time when a new event occurs that corresponds to the creation or update of a `Record` in a `FeatureGroup`. All `Records` in the `FeatureGroup` must have a corresponding `EventTime`. An `EventTime` can be a `String` or `Fractional`. * `Fractional`\: `EventTime` feature values must be a Unix timestamp in seconds. * `String`\: `EventTime` feature values must be an ISO-8601 string in the format. The following formats are supported `yyyy-MM-dd'T'HH:mm:ssZ` and `yyyy-MM-dd'T'HH:mm:ss.SSSZ` where `yyyy`, `MM`, and `dd` represent the year, month, and day respectively and `HH`, `mm`, `ss`, and if applicable, `SSS` represent the hour, month, second and milliseconds respsectively. `'T'` and `Z` are constants.
@option params [required, Array<Types::FeatureDefinition>] :feature_definitions
A list of `Feature` names and types. `Name` and `Type` is compulsory per `Feature`. Valid feature `FeatureType`s are `Integral`, `Fractional` and `String`. `FeatureName`s cannot be any of the following: `is_deleted`, `write_time`, `api_invocation_time` You can create up to 2,500 `FeatureDefinition`s per `FeatureGroup`.
@option params [Types::OnlineStoreConfig] :online_store_config
You can turn the `OnlineStore` on or off by specifying `True` for the `EnableOnlineStore` flag in `OnlineStoreConfig`; the default value is `False`. You can also include an Amazon Web Services KMS key ID (`KMSKeyId`) for at-rest encryption of the `OnlineStore`.
@option params [Types::OfflineStoreConfig] :offline_store_config
Use this to configure an `OfflineFeatureStore`. This parameter allows you to specify: * The Amazon Simple Storage Service (Amazon S3) location of an `OfflineStore`. * A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data cataolgue. * An KMS encryption key to encrypt the Amazon S3 location used for `OfflineStore`. To learn more about this parameter, see OfflineStoreConfig.
@option params [String] :role_arn
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the `OfflineStore` if an `OfflineStoreConfig` is provided.
@option params [String] :description
A free-form description of a `FeatureGroup`.
@option params [Array<Types::Tag>] :tags
Tags used to identify `Features` in each `FeatureGroup`.
@return [Types::CreateFeatureGroupResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateFeatureGroupResponse#feature_group_arn #feature_group_arn} => String
@example Request syntax with placeholder values
resp = client.create_feature_group({ feature_group_name: "FeatureGroupName", # required record_identifier_feature_name: "FeatureName", # required event_time_feature_name: "FeatureName", # required feature_definitions: [ # required { feature_name: "FeatureName", feature_type: "Integral", # accepts Integral, Fractional, String }, ], online_store_config: { security_config: { kms_key_id: "KmsKeyId", }, enable_online_store: false, }, offline_store_config: { s3_storage_config: { # required s3_uri: "S3Uri", # required kms_key_id: "KmsKeyId", resolved_output_s3_uri: "S3Uri", }, disable_glue_table_creation: false, data_catalog_config: { table_name: "TableName", # required catalog: "Catalog", # required database: "Database", # required }, }, role_arn: "RoleArn", description: "Description", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.feature_group_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateFeatureGroup AWS API Documentation
@overload create_feature_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2476 def create_feature_group(params = {}, options = {}) req = build_request(:create_feature_group, params) req.send_request(options) end
Creates a flow definition.
@option params [required, String] :flow_definition_name
The name of your flow definition.
@option params [Types::HumanLoopRequestSource] :human_loop_request_source
Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
@option params [Types::HumanLoopActivationConfig] :human_loop_activation_config
An object containing information about the events that trigger a human workflow.
@option params [required, Types::HumanLoopConfig] :human_loop_config
An object containing information about the tasks the human reviewers will perform.
@option params [required, Types::FlowDefinitionOutputConfig] :output_config
An object containing information about where the human review results will be uploaded.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, `arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298`.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
@return [Types::CreateFlowDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateFlowDefinitionResponse#flow_definition_arn #flow_definition_arn} => String
@example Request syntax with placeholder values
resp = client.create_flow_definition({ flow_definition_name: "FlowDefinitionName", # required human_loop_request_source: { aws_managed_human_loop_request_source: "AWS/Rekognition/DetectModerationLabels/Image/V3", # required, accepts AWS/Rekognition/DetectModerationLabels/Image/V3, AWS/Textract/AnalyzeDocument/Forms/V1 }, human_loop_activation_config: { human_loop_activation_conditions_config: { # required human_loop_activation_conditions: "HumanLoopActivationConditions", # required }, }, human_loop_config: { # required workteam_arn: "WorkteamArn", # required human_task_ui_arn: "HumanTaskUiArn", # required task_title: "FlowDefinitionTaskTitle", # required task_description: "FlowDefinitionTaskDescription", # required task_count: 1, # required task_availability_lifetime_in_seconds: 1, task_time_limit_in_seconds: 1, task_keywords: ["FlowDefinitionTaskKeyword"], public_workforce_task_price: { amount_in_usd: { dollars: 1, cents: 1, tenth_fractions_of_a_cent: 1, }, }, }, output_config: { # required s3_output_path: "S3Uri", # required kms_key_id: "KmsKeyId", }, role_arn: "RoleArn", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.flow_definition_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateFlowDefinition AWS API Documentation
@overload create_flow_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2567 def create_flow_definition(params = {}, options = {}) req = build_request(:create_flow_definition, params) req.send_request(options) end
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
@option params [required, String] :human_task_ui_name
The name of the user interface you are creating.
@option params [required, Types::UiTemplate] :ui_template
The Liquid template for the worker user interface.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
@return [Types::CreateHumanTaskUiResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateHumanTaskUiResponse#human_task_ui_arn #human_task_ui_arn} => String
@example Request syntax with placeholder values
resp = client.create_human_task_ui({ human_task_ui_name: "HumanTaskUiName", # required ui_template: { # required content: "TemplateContent", # required }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.human_task_ui_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateHumanTaskUi AWS API Documentation
@overload create_human_task_ui
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2614 def create_human_task_ui(params = {}, options = {}) req = build_request(:create_human_task_ui, params) req.send_request(options) end
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
@option params [required, String] :hyper_parameter_tuning_job_name
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ \_ % - (hyphen). The name is not case sensitive.
@option params [required, Types::HyperParameterTuningJobConfig] :hyper_parameter_tuning_job_config
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see [How Hyperparameter Tuning Works][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html
@option params [Types::HyperParameterTrainingJobDefinition] :training_job_definition
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
@option params [Array<Types::HyperParameterTrainingJobDefinition>] :training_job_definitions
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
@option params [Types::HyperParameterTuningJobWarmStartConfig] :warm_start_config
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify `IDENTICAL_DATA_AND_ALGORITHM` as the `WarmStartType` value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job. <note markdown="1"> All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job. </note>
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateHyperParameterTuningJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateHyperParameterTuningJobResponse#hyper_parameter_tuning_job_arn #hyper_parameter_tuning_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_hyper_parameter_tuning_job({ hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required hyper_parameter_tuning_job_config: { # required strategy: "Bayesian", # required, accepts Bayesian, Random hyper_parameter_tuning_job_objective: { type: "Maximize", # required, accepts Maximize, Minimize metric_name: "MetricName", # required }, resource_limits: { # required max_number_of_training_jobs: 1, # required max_parallel_training_jobs: 1, # required }, parameter_ranges: { integer_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], continuous_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], categorical_parameter_ranges: [ { name: "ParameterKey", # required values: ["ParameterValue"], # required }, ], }, training_job_early_stopping_type: "Off", # accepts Off, Auto tuning_job_completion_criteria: { target_objective_metric_value: 1.0, # required }, }, training_job_definition: { definition_name: "HyperParameterTrainingJobDefinitionName", tuning_objective: { type: "Maximize", # required, accepts Maximize, Minimize metric_name: "MetricName", # required }, hyper_parameter_ranges: { integer_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], continuous_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], categorical_parameter_ranges: [ { name: "ParameterKey", # required values: ["ParameterValue"], # required }, ], }, static_hyper_parameters: { "HyperParameterKey" => "HyperParameterValue", }, algorithm_specification: { # required training_image: "AlgorithmImage", training_input_mode: "Pipe", # required, accepts Pipe, File algorithm_name: "ArnOrName", metric_definitions: [ { name: "MetricName", # required regex: "MetricRegex", # required }, ], }, role_arn: "RoleArn", # required input_data_config: [ { channel_name: "ChannelName", # required data_source: { # required s3_data_source: { s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key attribute_names: ["AttributeName"], }, file_system_data_source: { file_system_id: "FileSystemId", # required file_system_access_mode: "rw", # required, accepts rw, ro file_system_type: "EFS", # required, accepts EFS, FSxLustre directory_path: "DirectoryPath", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip record_wrapper_type: "None", # accepts None, RecordIO input_mode: "Pipe", # accepts Pipe, File shuffle_config: { seed: 1, # required }, }, ], vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, resource_config: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge instance_count: 1, # required volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, enable_network_isolation: false, enable_inter_container_traffic_encryption: false, enable_managed_spot_training: false, checkpoint_config: { s3_uri: "S3Uri", # required local_path: "DirectoryPath", }, retry_strategy: { maximum_retry_attempts: 1, # required }, }, training_job_definitions: [ { definition_name: "HyperParameterTrainingJobDefinitionName", tuning_objective: { type: "Maximize", # required, accepts Maximize, Minimize metric_name: "MetricName", # required }, hyper_parameter_ranges: { integer_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], continuous_parameter_ranges: [ { name: "ParameterKey", # required min_value: "ParameterValue", # required max_value: "ParameterValue", # required scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic }, ], categorical_parameter_ranges: [ { name: "ParameterKey", # required values: ["ParameterValue"], # required }, ], }, static_hyper_parameters: { "HyperParameterKey" => "HyperParameterValue", }, algorithm_specification: { # required training_image: "AlgorithmImage", training_input_mode: "Pipe", # required, accepts Pipe, File algorithm_name: "ArnOrName", metric_definitions: [ { name: "MetricName", # required regex: "MetricRegex", # required }, ], }, role_arn: "RoleArn", # required input_data_config: [ { channel_name: "ChannelName", # required data_source: { # required s3_data_source: { s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key attribute_names: ["AttributeName"], }, file_system_data_source: { file_system_id: "FileSystemId", # required file_system_access_mode: "rw", # required, accepts rw, ro file_system_type: "EFS", # required, accepts EFS, FSxLustre directory_path: "DirectoryPath", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip record_wrapper_type: "None", # accepts None, RecordIO input_mode: "Pipe", # accepts Pipe, File shuffle_config: { seed: 1, # required }, }, ], vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, resource_config: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge instance_count: 1, # required volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, enable_network_isolation: false, enable_inter_container_traffic_encryption: false, enable_managed_spot_training: false, checkpoint_config: { s3_uri: "S3Uri", # required local_path: "DirectoryPath", }, retry_strategy: { maximum_retry_attempts: 1, # required }, }, ], warm_start_config: { parent_hyper_parameter_tuning_jobs: [ # required { hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", }, ], warm_start_type: "IdenticalDataAndAlgorithm", # required, accepts IdenticalDataAndAlgorithm, TransferLearning }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.hyper_parameter_tuning_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateHyperParameterTuningJob AWS API Documentation
@overload create_hyper_parameter_tuning_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 2962 def create_hyper_parameter_tuning_job(params = {}, options = {}) req = build_request(:create_hyper_parameter_tuning_job, params) req.send_request(options) end
Creates a custom SageMaker
image. A SageMaker
image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see [Bring your own SageMaker
image].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html
@option params [String] :description
The description of the image.
@option params [String] :display_name
The display name of the image. If not provided, `ImageName` is displayed.
@option params [required, String] :image_name
The name of the image. Must be unique to your account.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the image.
@return [Types::CreateImageResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateImageResponse#image_arn #image_arn} => String
@example Request syntax with placeholder values
resp = client.create_image({ description: "ImageDescription", display_name: "ImageDisplayName", image_name: "ImageName", # required role_arn: "RoleArn", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.image_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateImage AWS API Documentation
@overload create_image
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3020 def create_image(params = {}, options = {}) req = build_request(:create_image, params) req.send_request(options) end
Creates a version of the SageMaker
image specified by `ImageName`. The version represents the Amazon Container Registry (ECR) container image specified by `BaseImage`.
@option params [required, String] :base_image
The registry path of the container image to use as the starting point for this version. The path is an Amazon Container Registry (ECR) URI in the following format: `<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>`
@option params [required, String] :client_token
A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@option params [required, String] :image_name
The `ImageName` of the `Image` to create a version of.
@return [Types::CreateImageVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateImageVersionResponse#image_version_arn #image_version_arn} => String
@example Request syntax with placeholder values
resp = client.create_image_version({ base_image: "ImageBaseImage", # required client_token: "ClientToken", # required image_name: "ImageName", # required })
@example Response structure
resp.image_version_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateImageVersion AWS API Documentation
@overload create_image_version
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3068 def create_image_version(params = {}, options = {}) req = build_request(:create_image_version, params) req.send_request(options) end
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
-
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
-
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
-
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use *automated data labeling* to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses *active learning* to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see [Using Automated Data Labeling].
The data objects to be labeled are contained in an Amazon S3 bucket. You create a *manifest file* that describes the location of each object. For more information, see [Using Input and Output Data].
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in `ManifestS3Uri` have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (`InProgress`) streaming labeling job in real time. To learn how to create a static labeling job, see [Create a Labeling Job (API) ][3] in the Amazon SageMaker
Developer Guide. To learn how to create a streaming labeling job, see [Create a Streaming Labeling Job].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-data.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/sms-create-labeling-job-api.html [4]: docs.aws.amazon.com/sagemaker/latest/dg/sms-streaming-create-job.html
@option params [required, String] :labeling_job_name
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. `LabelingJobName` is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
@option params [required, String] :label_attribute_name
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The `LabelAttributeName` must meet the following requirements. * The name can't end with "-metadata". * If you are using one of the following [built-in task types][1], the attribute name *must* end with "-ref". If the task type you are using is not listed below, the attribute name *must not* end with "-ref". * Image semantic segmentation (`SemanticSegmentation)`, and adjustment (`AdjustmentSemanticSegmentation`) and verification (`VerificationSemanticSegmentation`) labeling jobs for this task type. * Video frame object detection (`VideoObjectDetection`), and adjustment and verification (`AdjustmentVideoObjectDetection`) labeling jobs for this task type. * Video frame object tracking (`VideoObjectTracking`), and adjustment and verification (`AdjustmentVideoObjectTracking`) labeling jobs for this task type. * 3D point cloud semantic segmentation (`3DPointCloudSemanticSegmentation`), and adjustment and verification (`Adjustment3DPointCloudSemanticSegmentation`) labeling jobs for this task type. * 3D point cloud object tracking (`3DPointCloudObjectTracking`), and adjustment and verification (`Adjustment3DPointCloudObjectTracking`) labeling jobs for this task type. If you are creating an adjustment or verification labeling job, you must use a *different* `LabelAttributeName` than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see [Verify and Adjust Labels][2]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html
@option params [required, Types::LabelingJobInputConfig] :input_config
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects. You must specify at least one of the following: `S3DataSource` or `SnsDataSource`. * Use `SnsDataSource` to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled. * Use `S3DataSource` to specify an input manifest file for both streaming and one-time labeling jobs. Adding an `S3DataSource` is optional if you use `SnsDataSource` to create a streaming labeling job. If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use `ContentClassifiers` to specify that your data is free of personally identifiable information and adult content.
@option params [required, Types::LabelingJobOutputConfig] :output_config
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
@option params [required, String] :role_arn
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
@option params [String] :label_category_config_s3_uri
The S3 URI of the file, referred to as a *label category configuration file*, that defines the categories used to label the data objects. For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see [Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs][1]. For named entity recognition jobs, in addition to `"labels"`, you must provide worker instructions in the label category configuration file using the `"instructions"` parameter: `"instructions": \{"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"\}`. For details and an example, see [Create a Named Entity Recognition Labeling Job (API) ][2]. For all other [built-in task types][3] and [custom tasks][4], your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing `label_1`, `label_2`,`...`,`label_n` with your label categories. `\{ ` `"document-version": "2018-11-28",` `"labels": [\{"label": "label_1"\},\{"label": "label_2"\},...\{"label": "label_n"\}]` `\}` Note the following about the label category configuration file: * For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one. * Each label category must be unique, you cannot specify duplicate label categories. * If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include `auditLabelAttributeName` in the label category configuration. Use this parameter to enter the [ `LabelAttributeName` ][5] of the labeling job you want to adjust or verify annotations of. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api [3]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html [4]: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html [5]: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName
@option params [Types::LabelingJobStoppingConditions] :stopping_conditions
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
@option params [Types::LabelingJobAlgorithmsConfig] :labeling_job_algorithms_config
Configures the information required to perform automated data labeling.
@option params [required, Types::HumanTaskConfig] :human_task_config
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
@option params [Array<Types::Tag>] :tags
An array of key/value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what
@return [Types::CreateLabelingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateLabelingJobResponse#labeling_job_arn #labeling_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_labeling_job({ labeling_job_name: "LabelingJobName", # required label_attribute_name: "LabelAttributeName", # required input_config: { # required data_source: { # required s3_data_source: { manifest_s3_uri: "S3Uri", # required }, sns_data_source: { sns_topic_arn: "SnsTopicArn", # required }, }, data_attributes: { content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent }, }, output_config: { # required s3_output_path: "S3Uri", # required kms_key_id: "KmsKeyId", sns_topic_arn: "SnsTopicArn", }, role_arn: "RoleArn", # required label_category_config_s3_uri: "S3Uri", stopping_conditions: { max_human_labeled_object_count: 1, max_percentage_of_input_dataset_labeled: 1, }, labeling_job_algorithms_config: { labeling_job_algorithm_specification_arn: "LabelingJobAlgorithmSpecificationArn", # required initial_active_learning_model_arn: "ModelArn", labeling_job_resource_config: { volume_kms_key_id: "KmsKeyId", }, }, human_task_config: { # required workteam_arn: "WorkteamArn", # required ui_config: { # required ui_template_s3_uri: "S3Uri", human_task_ui_arn: "HumanTaskUiArn", }, pre_human_task_lambda_arn: "LambdaFunctionArn", # required task_keywords: ["TaskKeyword"], task_title: "TaskTitle", # required task_description: "TaskDescription", # required number_of_human_workers_per_data_object: 1, # required task_time_limit_in_seconds: 1, # required task_availability_lifetime_in_seconds: 1, max_concurrent_task_count: 1, annotation_consolidation_config: { # required annotation_consolidation_lambda_arn: "LambdaFunctionArn", # required }, public_workforce_task_price: { amount_in_usd: { dollars: 1, cents: 1, tenth_fractions_of_a_cent: 1, }, }, }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.labeling_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateLabelingJob AWS API Documentation
@overload create_labeling_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3373 def create_labeling_job(params = {}, options = {}) req = build_request(:create_labeling_job, params) req.send_request(options) end
Creates a model in Amazon SageMaker
. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker
hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the `CreateEndpointConfig` API, and then create an endpoint with the `CreateEndpoint` API. Amazon SageMaker
then deploys all of the containers that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker
hosting services, see [Deploy the Model to Amazon SageMaker
Hosting Services (Amazon Web Services SDK for Python (Boto 3)).]
To run a batch transform using your model, you start a job with the `CreateTransformJob` API. Amazon SageMaker
uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the `CreateModel` request, you must define a container with the `PrimaryContainer` parameter.
In the request, you also provide an IAM role that Amazon SageMaker
can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
[1]: docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto
@option params [required, String] :model_name
The name of the new model.
@option params [Types::ContainerDefinition] :primary_container
The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
@option params [Array<Types::ContainerDefinition>] :containers
Specifies the containers in the inference pipeline.
@option params [Types::InferenceExecutionConfig] :inference_execution_config
Specifies details of how containers in a multi-container endpoint are called.
@option params [required, String] :execution_role_arn
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see [Amazon SageMaker Roles][1]. <note markdown="1"> To be able to pass this role to Amazon SageMaker, the caller of this API must have the `iam:PassRole` permission. </note> [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@option params [Types::VpcConfig] :vpc_config
A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. `VpcConfig` is used in hosting services and in batch transform. For more information, see [Protect Endpoints by Using an Amazon Virtual Private Cloud][1] and [Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud][2]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/host-vpc.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/batch-vpc.html
@option params [Boolean] :enable_network_isolation
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
@return [Types::CreateModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelOutput#model_arn #model_arn} => String
@example Request syntax with placeholder values
resp = client.create_model({ model_name: "ModelName", # required primary_container: { container_hostname: "ContainerHostname", image: "ContainerImage", image_config: { repository_access_mode: "Platform", # required, accepts Platform, Vpc repository_auth_config: { repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required }, }, mode: "SingleModel", # accepts SingleModel, MultiModel model_data_url: "Url", environment: { "EnvironmentKey" => "EnvironmentValue", }, model_package_name: "VersionedArnOrName", multi_model_config: { model_cache_setting: "Enabled", # accepts Enabled, Disabled }, }, containers: [ { container_hostname: "ContainerHostname", image: "ContainerImage", image_config: { repository_access_mode: "Platform", # required, accepts Platform, Vpc repository_auth_config: { repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required }, }, mode: "SingleModel", # accepts SingleModel, MultiModel model_data_url: "Url", environment: { "EnvironmentKey" => "EnvironmentValue", }, model_package_name: "VersionedArnOrName", multi_model_config: { model_cache_setting: "Enabled", # accepts Enabled, Disabled }, }, ], inference_execution_config: { mode: "Serial", # required, accepts Serial, Direct }, execution_role_arn: "RoleArn", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, enable_network_isolation: false, })
@example Response structure
resp.model_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModel AWS API Documentation
@overload create_model
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3546 def create_model(params = {}, options = {}) req = build_request(:create_model, params) req.send_request(options) end
Creates the definition for a model bias job.
@option params [required, String] :job_definition_name
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@option params [Types::ModelBiasBaselineConfig] :model_bias_baseline_config
The baseline configuration for a model bias job.
@option params [required, Types::ModelBiasAppSpecification] :model_bias_app_specification
Configures the model bias job to run a specified Docker container image.
@option params [required, Types::ModelBiasJobInput] :model_bias_job_input
Inputs for the model bias job.
@option params [required, Types::MonitoringOutputConfig] :model_bias_job_output_config
The output configuration for monitoring jobs.
@option params [required, Types::MonitoringResources] :job_resources
Identifies the resources to deploy for a monitoring job.
@option params [Types::MonitoringNetworkConfig] :network_config
Networking options for a model bias job.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
@option params [Types::MonitoringStoppingCondition] :stopping_condition
A time limit for how long the monitoring job is allowed to run before stopping.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL
@return [Types::CreateModelBiasJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelBiasJobDefinitionResponse#job_definition_arn #job_definition_arn} => String
@example Request syntax with placeholder values
resp = client.create_model_bias_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required model_bias_baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, }, model_bias_app_specification: { # required image_uri: "ImageUri", # required config_uri: "S3Uri", # required environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, }, model_bias_job_input: { # required endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, ground_truth_s3_input: { # required s3_uri: "MonitoringS3Uri", }, }, model_bias_job_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, job_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required stopping_condition: { max_runtime_in_seconds: 1, # required }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.job_definition_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModelBiasJobDefinition AWS API Documentation
@overload create_model_bias_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3679 def create_model_bias_job_definition(params = {}, options = {}) req = build_request(:create_model_bias_job_definition, params) req.send_request(options) end
Creates the definition for a model explainability job.
@option params [required, String] :job_definition_name
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@option params [Types::ModelExplainabilityBaselineConfig] :model_explainability_baseline_config
The baseline configuration for a model explainability job.
@option params [required, Types::ModelExplainabilityAppSpecification] :model_explainability_app_specification
Configures the model explainability job to run a specified Docker container image.
@option params [required, Types::ModelExplainabilityJobInput] :model_explainability_job_input
Inputs for the model explainability job.
@option params [required, Types::MonitoringOutputConfig] :model_explainability_job_output_config
The output configuration for monitoring jobs.
@option params [required, Types::MonitoringResources] :job_resources
Identifies the resources to deploy for a monitoring job.
@option params [Types::MonitoringNetworkConfig] :network_config
Networking options for a model explainability job.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
@option params [Types::MonitoringStoppingCondition] :stopping_condition
A time limit for how long the monitoring job is allowed to run before stopping.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL
@return [Types::CreateModelExplainabilityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelExplainabilityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String
@example Request syntax with placeholder values
resp = client.create_model_explainability_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required model_explainability_baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, }, model_explainability_app_specification: { # required image_uri: "ImageUri", # required config_uri: "S3Uri", # required environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, }, model_explainability_job_input: { # required endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, }, model_explainability_job_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, job_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required stopping_condition: { max_runtime_in_seconds: 1, # required }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.job_definition_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModelExplainabilityJobDefinition AWS API Documentation
@overload create_model_explainability_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 3810 def create_model_explainability_job_definition(params = {}, options = {}) req = build_request(:create_model_explainability_job_definition, params) req.send_request(options) end
Creates a model package that you can use to create Amazon SageMaker
models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker
.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for `InferenceSpecification`. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for `SourceAlgorithmSpecification`.
<note markdown=“1”> There are two types of model packages:
* Versioned - a model that is part of a model group in the model registry.
-
Unversioned - a model package that is not part of a model group.
</note>
@option params [String] :model_package_name
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models.
@option params [String] :model_package_group_name
The name of the model group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models.
@option params [String] :model_package_description
A description of the model package.
@option params [Types::InferenceSpecification] :inference_specification
Specifies details about inference jobs that can be run with models based on this model package, including the following: * The Amazon ECR paths of containers that contain the inference code and model artifacts. * The instance types that the model package supports for transform jobs and real-time endpoints used for inference. * The input and output content formats that the model package supports for inference.
@option params [Types::ModelPackageValidationSpecification] :validation_specification
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
@option params [Types::SourceAlgorithmSpecification] :source_algorithm_specification
Details about the algorithm that was used to create the model package.
@option params [Boolean] :certify_for_marketplace
Whether to certify the model package for listing on Amazon Web Services Marketplace. This parameter is optional for unversioned models, and does not apply to versioned models.
@option params [Array<Types::Tag>] :tags
A list of key value pairs associated with the model. For more information, see [Tagging Amazon Web Services resources][1] in the *Amazon Web Services General Reference Guide*. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@option params [String] :model_approval_status
Whether the model is approved for deployment. This parameter is optional for versioned models, and does not apply to unversioned models. For versioned models, the value of this parameter must be set to `Approved` to deploy the model.
@option params [Types::MetadataProperties] :metadata_properties
Metadata properties of the tracking entity, trial, or trial component.
@option params [Types::ModelMetrics] :model_metrics
A structure that contains model metrics reports.
@option params [String] :client_token
A unique token that guarantees that the call to this API is idempotent. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::CreateModelPackageOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelPackageOutput#model_package_arn #model_package_arn} => String
@example Request syntax with placeholder values
resp = client.create_model_package({ model_package_name: "EntityName", model_package_group_name: "EntityName", model_package_description: "EntityDescription", inference_specification: { containers: [ # required { container_hostname: "ContainerHostname", image: "ContainerImage", # required image_digest: "ImageDigest", model_data_url: "Url", product_id: "ProductId", environment: { "EnvironmentKey" => "EnvironmentValue", }, }, ], supported_transform_instance_types: ["ml.m4.xlarge"], # accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge supported_realtime_inference_instance_types: ["ml.t2.medium"], # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge supported_content_types: ["ContentType"], # required supported_response_mime_types: ["ResponseMIMEType"], # required }, validation_specification: { validation_role: "RoleArn", # required validation_profiles: [ # required { profile_name: "EntityName", # required transform_job_definition: { # required max_concurrent_transforms: 1, max_payload_in_mb: 1, batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord environment: { "TransformEnvironmentKey" => "TransformEnvironmentValue", }, transform_input: { # required data_source: { # required s3_data_source: { # required s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip split_type: "None", # accepts None, Line, RecordIO, TFRecord }, transform_output: { # required s3_output_path: "S3Uri", # required accept: "Accept", assemble_with: "None", # accepts None, Line kms_key_id: "KmsKeyId", }, transform_resources: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge instance_count: 1, # required volume_kms_key_id: "KmsKeyId", }, }, }, ], }, source_algorithm_specification: { source_algorithms: [ # required { model_data_url: "Url", algorithm_name: "ArnOrName", # required }, ], }, certify_for_marketplace: false, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], model_approval_status: "Approved", # accepts Approved, Rejected, PendingManualApproval metadata_properties: { commit_id: "MetadataPropertyValue", repository: "MetadataPropertyValue", generated_by: "MetadataPropertyValue", project_id: "MetadataPropertyValue", }, model_metrics: { model_quality: { statistics: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, constraints: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, }, model_data_quality: { statistics: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, constraints: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, }, bias: { report: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, }, explainability: { report: { content_type: "ContentType", # required content_digest: "ContentDigest", s3_uri: "S3Uri", # required }, }, }, client_token: "ClientToken", })
@example Response structure
resp.model_package_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModelPackage AWS API Documentation
@overload create_model_package
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4050 def create_model_package(params = {}, options = {}) req = build_request(:create_model_package, params) req.send_request(options) end
Creates a model group. A model group contains a group of model versions.
@option params [required, String] :model_package_group_name
The name of the model group.
@option params [String] :model_package_group_description
A description for the model group.
@option params [Array<Types::Tag>] :tags
A list of key value pairs associated with the model group. For more information, see [Tagging Amazon Web Services resources][1] in the *Amazon Web Services General Reference Guide*. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateModelPackageGroupOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelPackageGroupOutput#model_package_group_arn #model_package_group_arn} => String
@example Request syntax with placeholder values
resp = client.create_model_package_group({ model_package_group_name: "EntityName", # required model_package_group_description: "EntityDescription", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.model_package_group_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModelPackageGroup AWS API Documentation
@overload create_model_package_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4098 def create_model_package_group(params = {}, options = {}) req = build_request(:create_model_package_group, params) req.send_request(options) end
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see [Amazon SageMaker
Model Monitor].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html
@option params [required, String] :job_definition_name
The name of the monitoring job definition.
@option params [Types::ModelQualityBaselineConfig] :model_quality_baseline_config
Specifies the constraints and baselines for the monitoring job.
@option params [required, Types::ModelQualityAppSpecification] :model_quality_app_specification
The container that runs the monitoring job.
@option params [required, Types::ModelQualityJobInput] :model_quality_job_input
A list of the inputs that are monitored. Currently endpoints are supported.
@option params [required, Types::MonitoringOutputConfig] :model_quality_job_output_config
The output configuration for monitoring jobs.
@option params [required, Types::MonitoringResources] :job_resources
Identifies the resources to deploy for a monitoring job.
@option params [Types::MonitoringNetworkConfig] :network_config
Specifies the network configuration for the monitoring job.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
@option params [Types::MonitoringStoppingCondition] :stopping_condition
A time limit for how long the monitoring job is allowed to run before stopping.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL
@return [Types::CreateModelQualityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateModelQualityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String
@example Request syntax with placeholder values
resp = client.create_model_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required model_quality_baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, }, model_quality_app_specification: { # required image_uri: "ImageUri", # required container_entrypoint: ["ContainerEntrypointString"], container_arguments: ["ContainerArgument"], record_preprocessor_source_uri: "S3Uri", post_analytics_processor_source_uri: "S3Uri", problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, }, model_quality_job_input: { # required endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, ground_truth_s3_input: { # required s3_uri: "MonitoringS3Uri", }, }, model_quality_job_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, job_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required stopping_condition: { max_runtime_in_seconds: 1, # required }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.job_definition_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateModelQualityJobDefinition AWS API Documentation
@overload create_model_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4240 def create_model_quality_job_definition(params = {}, options = {}) req = build_request(:create_model_quality_job_definition, params) req.send_request(options) end
Creates a schedule that regularly starts Amazon SageMaker
Processing Jobs to monitor the data captured for an Amazon SageMaker
Endoint.
@option params [required, String] :monitoring_schedule_name
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
@option params [required, Types::MonitoringScheduleConfig] :monitoring_schedule_config
The configuration object that specifies the monitoring schedule and defines the monitoring job.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags]( https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL) in the *Amazon Web Services Billing and Cost Management User Guide*.
@return [Types::CreateMonitoringScheduleResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateMonitoringScheduleResponse#monitoring_schedule_arn #monitoring_schedule_arn} => String
@example Request syntax with placeholder values
resp = client.create_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required monitoring_schedule_config: { # required schedule_config: { schedule_expression: "ScheduleExpression", # required }, monitoring_job_definition: { baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, statistics_resource: { s3_uri: "S3Uri", }, }, monitoring_inputs: [ # required { endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, }, ], monitoring_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, monitoring_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, monitoring_app_specification: { # required image_uri: "ImageUri", # required container_entrypoint: ["ContainerEntrypointString"], container_arguments: ["ContainerArgument"], record_preprocessor_source_uri: "S3Uri", post_analytics_processor_source_uri: "S3Uri", }, stopping_condition: { max_runtime_in_seconds: 1, # required }, environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required }, monitoring_job_definition_name: "MonitoringJobDefinitionName", monitoring_type: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.monitoring_schedule_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateMonitoringSchedule AWS API Documentation
@overload create_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4362 def create_monitoring_schedule(params = {}, options = {}) req = build_request(:create_monitoring_schedule, params) req.send_request(options) end
Creates an Amazon SageMaker
notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a `CreateNotebookInstance` request, specify the type of ML compute instance that you want to run. Amazon SageMaker
launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker
also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker
with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker
does the following:
-
Creates a network interface in the Amazon
SageMaker
VPC. -
(Option) If you specified `SubnetId`, Amazon
SageMaker
creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, AmazonSageMaker
attaches the security group that you specified in the request to the network interface that it creates in your VPC. -
Launches an EC2 instance of the type specified in the request in the Amazon
SageMaker
VPC. If you specified `SubnetId` of your VPC, AmazonSageMaker
specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, Amazon SageMaker
returns its Amazon Resource
Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker
creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker
endpoints, and validate hosted models.
For more information, see [How It Works].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html
@option params [required, String] :notebook_instance_name
The name of the new notebook instance.
@option params [required, String] :instance_type
The type of ML compute instance to launch for the notebook instance.
@option params [String] :subnet_id
The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
@option params [Array<String>] :security_group_ids
The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
@option params [required, String] :role_arn
When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see [Amazon SageMaker Roles][1]. <note markdown="1"> To be able to pass this role to Amazon SageMaker, the caller of this API must have the `iam:PassRole` permission. </note> [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html
@option params [String] :kms_key_id
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see [Enabling and Disabling Keys][1] in the *Amazon Web Services Key Management Service Developer Guide*. [1]: https://docs.aws.amazon.com/kms/latest/developerguide/enabling-keys.html
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@option params [String] :lifecycle_config_name
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html
@option params [String] :direct_internet_access
Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to `Disabled` this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC. For more information, see [Notebook Instances Are Internet-Enabled by Default][1]. You can set the value of this parameter to `Disabled` only if you set a value for the `SubnetId` parameter. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/appendix-additional-considerations.html#appendix-notebook-and-internet-access
@option params [Integer] :volume_size_in_gb
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
@option params [Array<String>] :accelerator_types
A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see [Using Elastic Inference in Amazon SageMaker][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
@option params [String] :default_code_repository
A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in [Amazon Web Services CodeCommit][1] or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see [Associating Git Repositories with Amazon SageMaker Notebook Instances][2]. [1]: https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html
@option params [Array<String>] :additional_code_repositories
An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in [Amazon Web Services CodeCommit][1] or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see [Associating Git Repositories with Amazon SageMaker Notebook Instances][2]. [1]: https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html
@option params [String] :root_access
Whether root access is enabled or disabled for users of the notebook instance. The default value is `Enabled`. <note markdown="1"> Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users. </note>
@option params [String] :platform_identifier
The platform identifier of the notebook instance runtime environment.
@return [Types::CreateNotebookInstanceOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateNotebookInstanceOutput#notebook_instance_arn #notebook_instance_arn} => String
@example Request syntax with placeholder values
resp = client.create_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge subnet_id: "SubnetId", security_group_ids: ["SecurityGroupId"], role_arn: "RoleArn", # required kms_key_id: "KmsKeyId", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], lifecycle_config_name: "NotebookInstanceLifecycleConfigName", direct_internet_access: "Enabled", # accepts Enabled, Disabled volume_size_in_gb: 1, accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge default_code_repository: "CodeRepositoryNameOrUrl", additional_code_repositories: ["CodeRepositoryNameOrUrl"], root_access: "Enabled", # accepts Enabled, Disabled platform_identifier: "PlatformIdentifier", })
@example Response structure
resp.notebook_instance_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateNotebookInstance AWS API Documentation
@overload create_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4586 def create_notebook_instance(params = {}, options = {}) req = build_request(:create_notebook_instance, params) req.send_request(options) end
Creates a lifecycle configuration that you can associate with a notebook instance. A *lifecycle configuration* is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the `$PATH` environment variable that is available to both scripts is `/sbin:bin:/usr/sbin:/usr/bin`.
View CloudWatch Logs for notebook instance lifecycle configurations in log group `/aws/sagemaker/NotebookInstances` in log stream `[notebook-instance-name]/`.
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html
@option params [required, String] :notebook_instance_lifecycle_config_name
The name of the lifecycle configuration.
@option params [Array<Types::NotebookInstanceLifecycleHook>] :on_create
A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
@option params [Array<Types::NotebookInstanceLifecycleHook>] :on_start
A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
@return [Types::CreateNotebookInstanceLifecycleConfigOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateNotebookInstanceLifecycleConfigOutput#notebook_instance_lifecycle_config_arn #notebook_instance_lifecycle_config_arn} => String
@example Request syntax with placeholder values
resp = client.create_notebook_instance_lifecycle_config({ notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required on_create: [ { content: "NotebookInstanceLifecycleConfigContent", }, ], on_start: [ { content: "NotebookInstanceLifecycleConfigContent", }, ], })
@example Response structure
resp.notebook_instance_lifecycle_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateNotebookInstanceLifecycleConfig AWS API Documentation
@overload create_notebook_instance_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4655 def create_notebook_instance_lifecycle_config(params = {}, options = {}) req = build_request(:create_notebook_instance_lifecycle_config, params) req.send_request(options) end
Creates a pipeline using a JSON pipeline definition.
@option params [required, String] :pipeline_name
The name of the pipeline.
@option params [String] :pipeline_display_name
The display name of the pipeline.
@option params [required, String] :pipeline_definition
The JSON pipeline definition of the pipeline.
@option params [String] :pipeline_description
A description of the pipeline.
@option params [required, String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
@option params [Array<Types::Tag>] :tags
A list of tags to apply to the created pipeline.
@return [Types::CreatePipelineResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreatePipelineResponse#pipeline_arn #pipeline_arn} => String
@example Request syntax with placeholder values
resp = client.create_pipeline({ pipeline_name: "PipelineName", # required pipeline_display_name: "PipelineName", pipeline_definition: "PipelineDefinition", # required pipeline_description: "PipelineDescription", client_request_token: "IdempotencyToken", # required role_arn: "RoleArn", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.pipeline_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreatePipeline AWS API Documentation
@overload create_pipeline
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4718 def create_pipeline(params = {}, options = {}) req = build_request(:create_pipeline, params) req.send_request(options) end
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker
Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user used to call this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see [Connect to SageMaker
Studio Through an Interface VPC Endpoint] .
<note markdown=“1”> The URL that you get from a call to `CreatePresignedDomainUrl` has a default timeout of 5 minutes. You can configure this value using `ExpiresInSeconds`. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
</note>
[1]: docs.aws.amazon.com/sagemaker/latest/dg/studio-interface-endpoint.html
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The name of the UserProfile to sign-in as.
@option params [Integer] :session_expiration_duration_in_seconds
The session expiration duration in seconds. This value defaults to 43200.
@option params [Integer] :expires_in_seconds
The number of seconds until the pre-signed URL expires. This value defaults to 300.
@return [Types::CreatePresignedDomainUrlResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreatePresignedDomainUrlResponse#authorized_url #authorized_url} => String
@example Request syntax with placeholder values
resp = client.create_presigned_domain_url({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required session_expiration_duration_in_seconds: 1, expires_in_seconds: 1, })
@example Response structure
resp.authorized_url #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreatePresignedDomainUrl AWS API Documentation
@overload create_presigned_domain_url
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4788 def create_presigned_domain_url(params = {}, options = {}) req = build_request(:create_presigned_domain_url, params) req.send_request(options) end
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
console, when you choose `Open` next to a notebook instance, Amazon SageMaker
opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the `NotIpAddress` condition operator and the `aws:SourceIP` condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see [Limit Access to a Notebook Instance by IP Address].
<note markdown=“1”> The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
</note>
[1]: docs.aws.amazon.com/sagemaker/latest/dg/security_iam_id-based-policy-examples.html#nbi-ip-filter
@option params [required, String] :notebook_instance_name
The name of the notebook instance.
@option params [Integer] :session_expiration_duration_in_seconds
The duration of the session, in seconds. The default is 12 hours.
@return [Types::CreatePresignedNotebookInstanceUrlOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreatePresignedNotebookInstanceUrlOutput#authorized_url #authorized_url} => String
@example Request syntax with placeholder values
resp = client.create_presigned_notebook_instance_url({ notebook_instance_name: "NotebookInstanceName", # required session_expiration_duration_in_seconds: 1, })
@example Response structure
resp.authorized_url #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreatePresignedNotebookInstanceUrl AWS API Documentation
@overload create_presigned_notebook_instance_url
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 4849 def create_presigned_notebook_instance_url(params = {}, options = {}) req = build_request(:create_presigned_notebook_instance_url, params) req.send_request(options) end
Creates a processing job.
@option params [Array<Types::ProcessingInput>] :processing_inputs
An array of inputs configuring the data to download into the processing container.
@option params [Types::ProcessingOutputConfig] :processing_output_config
Output configuration for the processing job.
@option params [required, String] :processing_job_name
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@option params [required, Types::ProcessingResources] :processing_resources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
@option params [Types::ProcessingStoppingCondition] :stopping_condition
The time limit for how long the processing job is allowed to run.
@option params [required, Types::AppSpecification] :app_specification
Configures the processing job to run a specified Docker container image.
@option params [Hash<String,String>] :environment
The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
@option params [Types::NetworkConfig] :network_config
Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL
@option params [Types::ExperimentConfig] :experiment_config
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: * CreateProcessingJob * CreateTrainingJob * CreateTransformJob
@return [Types::CreateProcessingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateProcessingJobResponse#processing_job_arn #processing_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_processing_job({ processing_inputs: [ { input_name: "String", # required app_managed: false, s3_input: { s3_uri: "S3Uri", # required local_path: "ProcessingLocalPath", s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key s3_compression_type: "None", # accepts None, Gzip }, dataset_definition: { athena_dataset_definition: { catalog: "AthenaCatalog", # required database: "AthenaDatabase", # required query_string: "AthenaQueryString", # required work_group: "AthenaWorkGroup", output_s3_uri: "S3Uri", # required kms_key_id: "KmsKeyId", output_format: "PARQUET", # required, accepts PARQUET, ORC, AVRO, JSON, TEXTFILE output_compression: "GZIP", # accepts GZIP, SNAPPY, ZLIB }, redshift_dataset_definition: { cluster_id: "RedshiftClusterId", # required database: "RedshiftDatabase", # required db_user: "RedshiftUserName", # required query_string: "RedshiftQueryString", # required cluster_role_arn: "RoleArn", # required output_s3_uri: "S3Uri", # required kms_key_id: "KmsKeyId", output_format: "PARQUET", # required, accepts PARQUET, CSV output_compression: "None", # accepts None, GZIP, BZIP2, ZSTD, SNAPPY }, local_path: "ProcessingLocalPath", data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key input_mode: "Pipe", # accepts Pipe, File }, }, ], processing_output_config: { outputs: [ # required { output_name: "String", # required s3_output: { s3_uri: "S3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # required, accepts Continuous, EndOfJob }, feature_store_output: { feature_group_name: "FeatureGroupName", # required }, app_managed: false, }, ], kms_key_id: "KmsKeyId", }, processing_job_name: "ProcessingJobName", # required processing_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, stopping_condition: { max_runtime_in_seconds: 1, # required }, app_specification: { # required image_uri: "ImageUri", # required container_entrypoint: ["ContainerEntrypointString"], container_arguments: ["ContainerArgument"], }, environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], experiment_config: { experiment_name: "ExperimentEntityName", trial_name: "ExperimentEntityName", trial_component_display_name: "ExperimentEntityName", }, })
@example Response structure
resp.processing_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateProcessingJob AWS API Documentation
@overload create_processing_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5026 def create_processing_job(params = {}, options = {}) req = build_request(:create_processing_job, params) req.send_request(options) end
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
@option params [required, String] :project_name
The name of the project.
@option params [String] :project_description
A description for the project.
@option params [required, Types::ServiceCatalogProvisioningDetails] :service_catalog_provisioning_details
The product ID and provisioning artifact ID to provision a service catalog. For information, see [What is Amazon Web Services Service Catalog][1]. [1]: https://docs.aws.amazon.com/servicecatalog/latest/adminguide/introduction.html
@option params [Array<Types::Tag>] :tags
An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see [Tagging Amazon Web Services resources][1] in the *Amazon Web Services General Reference Guide*. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@return [Types::CreateProjectOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateProjectOutput#project_arn #project_arn} => String * {Types::CreateProjectOutput#project_id #project_id} => String
@example Request syntax with placeholder values
resp = client.create_project({ project_name: "ProjectEntityName", # required project_description: "EntityDescription", service_catalog_provisioning_details: { # required product_id: "ServiceCatalogEntityId", # required provisioning_artifact_id: "ServiceCatalogEntityId", # required path_id: "ServiceCatalogEntityId", provisioning_parameters: [ { key: "ProvisioningParameterKey", value: "ProvisioningParameterValue", }, ], }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.project_arn #=> String resp.project_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateProject AWS API Documentation
@overload create_project
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5098 def create_project(params = {}, options = {}) req = build_request(:create_project, params) req.send_request(options) end
Creates a new Studio Lifecycle Configuration.
@option params [required, String] :studio_lifecycle_config_name
The name of the Studio Lifecycle Configuration to create.
@option params [required, String] :studio_lifecycle_config_content
The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
@option params [required, String] :studio_lifecycle_config_app_type
The App type that the Lifecycle Configuration is attached to.
@option params [Array<Types::Tag>] :tags
Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
@return [Types::CreateStudioLifecycleConfigResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateStudioLifecycleConfigResponse#studio_lifecycle_config_arn #studio_lifecycle_config_arn} => String
@example Request syntax with placeholder values
resp = client.create_studio_lifecycle_config({ studio_lifecycle_config_name: "StudioLifecycleConfigName", # required studio_lifecycle_config_content: "StudioLifecycleConfigContent", # required studio_lifecycle_config_app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.studio_lifecycle_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateStudioLifecycleConfig AWS API Documentation
@overload create_studio_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5146 def create_studio_lifecycle_config(params = {}, options = {}) req = build_request(:create_studio_lifecycle_config, params) req.send_request(options) end
Starts a model training job. After training completes, Amazon SageMaker
saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker
hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker
, provided that you know how to use them for inference.
In the request body, you provide the following:
-
`AlgorithmSpecification` - Identifies the training algorithm to use.
-
`HyperParameters` - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon
SageMaker
, see [Algorithms]. -
`InputDataConfig` - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
-
`OutputDataConfig` - Identifies the Amazon S3 bucket where you want Amazon
SageMaker
to save the results of model training. -
`ResourceConfig` - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
-
`EnableManagedSpotTraining` - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see [Managed Spot Training].
-
`RoleArn` - The Amazon
Resource
Name (ARN) that AmazonSageMaker
assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that AmazonSageMaker
can successfully complete model training. -
`StoppingCondition` - To help cap training costs, use `MaxRuntimeInSeconds` to set a time limit for training. Use `MaxWaitTimeInSeconds` to specify how long a managed spot training job has to complete.
-
`Environment` - The environment variables to set in the Docker container.
-
`RetryStrategy` - The number of times to retry the job when the job fails due to an `InternalServerError`.
For more information about Amazon SageMaker
, see [How It Works].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html [3]: docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html
@option params [required, String] :training_job_name
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
@option params [Hash<String,String>] :hyper_parameters
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see [Algorithms][1]. You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the `Length Constraint`. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
@option params [required, Types::AlgorithmSpecification] :algorithm_specification
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see [Algorithms][1]. For information about providing your own algorithms, see [Using Your Own Algorithms with Amazon SageMaker][2]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see [Amazon SageMaker Roles][1]. <note markdown="1"> To be able to pass this role to Amazon SageMaker, the caller of this API must have the `iam:PassRole` permission. </note> [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html
@option params [Array<Types::Channel>] :input_data_config
An array of `Channel` objects. Each channel is a named input source. `InputDataConfig` describes the input data and its location. Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, `training_data` and `validation_data`. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
@option params [required, Types::OutputDataConfig] :output_data_config
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
@option params [required, Types::ResourceConfig] :resource_config
The resources, including the ML compute instances and ML storage volumes, to use for model training. ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose `File` as the `TrainingInputMode` in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
@option params [Types::VpcConfig] :vpc_config
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see [Protect Training Jobs by Using an Amazon Virtual Private Cloud][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html
@option params [required, Types::StoppingCondition] :stopping_condition
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM` signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see [Tagging Amazon Web Services Resources][1]. [1]: https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html
@option params [Boolean] :enable_network_isolation
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
@option params [Boolean] :enable_inter_container_traffic_encryption
To encrypt all communications between ML compute instances in distributed training, choose `True`. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see [Protect Communications Between ML Compute Instances in a Distributed Training Job][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html
@option params [Boolean] :enable_managed_spot_training
To train models using managed spot training, choose `True`. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run. The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
@option params [Types::CheckpointConfig] :checkpoint_config
Contains information about the output location for managed spot training checkpoint data.
@option params [Types::DebugHookConfig] :debug_hook_config
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the `DebugHookConfig` parameter, see [Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html
@option params [Array<Types::DebugRuleConfiguration>] :debug_rule_configurations
Configuration information for Debugger rules for debugging output tensors.
@option params [Types::TensorBoardOutputConfig] :tensor_board_output_config
Configuration of storage locations for the Debugger TensorBoard output data.
@option params [Types::ExperimentConfig] :experiment_config
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: * CreateProcessingJob * CreateTrainingJob * CreateTransformJob
@option params [Types::ProfilerConfig] :profiler_config
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
@option params [Array<Types::ProfilerRuleConfiguration>] :profiler_rule_configurations
Configuration information for Debugger rules for profiling system and framework metrics.
@option params [Hash<String,String>] :environment
The environment variables to set in the Docker container.
@option params [Types::RetryStrategy] :retry_strategy
The number of times to retry the job when the job fails due to an `InternalServerError`.
@return [Types::CreateTrainingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateTrainingJobResponse#training_job_arn #training_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_training_job({ training_job_name: "TrainingJobName", # required hyper_parameters: { "HyperParameterKey" => "HyperParameterValue", }, algorithm_specification: { # required training_image: "AlgorithmImage", algorithm_name: "ArnOrName", training_input_mode: "Pipe", # required, accepts Pipe, File metric_definitions: [ { name: "MetricName", # required regex: "MetricRegex", # required }, ], enable_sage_maker_metrics_time_series: false, }, role_arn: "RoleArn", # required input_data_config: [ { channel_name: "ChannelName", # required data_source: { # required s3_data_source: { s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key attribute_names: ["AttributeName"], }, file_system_data_source: { file_system_id: "FileSystemId", # required file_system_access_mode: "rw", # required, accepts rw, ro file_system_type: "EFS", # required, accepts EFS, FSxLustre directory_path: "DirectoryPath", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip record_wrapper_type: "None", # accepts None, RecordIO input_mode: "Pipe", # accepts Pipe, File shuffle_config: { seed: 1, # required }, }, ], output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, resource_config: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge instance_count: 1, # required volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, stopping_condition: { # required max_runtime_in_seconds: 1, max_wait_time_in_seconds: 1, }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], enable_network_isolation: false, enable_inter_container_traffic_encryption: false, enable_managed_spot_training: false, checkpoint_config: { s3_uri: "S3Uri", # required local_path: "DirectoryPath", }, debug_hook_config: { local_path: "DirectoryPath", s3_output_path: "S3Uri", # required hook_parameters: { "ConfigKey" => "ConfigValue", }, collection_configurations: [ { collection_name: "CollectionName", collection_parameters: { "ConfigKey" => "ConfigValue", }, }, ], }, debug_rule_configurations: [ { rule_configuration_name: "RuleConfigurationName", # required local_path: "DirectoryPath", s3_output_path: "S3Uri", rule_evaluator_image: "AlgorithmImage", # required instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, rule_parameters: { "ConfigKey" => "ConfigValue", }, }, ], tensor_board_output_config: { local_path: "DirectoryPath", s3_output_path: "S3Uri", # required }, experiment_config: { experiment_name: "ExperimentEntityName", trial_name: "ExperimentEntityName", trial_component_display_name: "ExperimentEntityName", }, profiler_config: { s3_output_path: "S3Uri", # required profiling_interval_in_milliseconds: 1, profiling_parameters: { "ConfigKey" => "ConfigValue", }, }, profiler_rule_configurations: [ { rule_configuration_name: "RuleConfigurationName", # required local_path: "DirectoryPath", s3_output_path: "S3Uri", rule_evaluator_image: "AlgorithmImage", # required instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, rule_parameters: { "ConfigKey" => "ConfigValue", }, }, ], environment: { "TrainingEnvironmentKey" => "TrainingEnvironmentValue", }, retry_strategy: { maximum_retry_attempts: 1, # required }, })
@example Response structure
resp.training_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTrainingJob AWS API Documentation
@overload create_training_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5561 def create_training_job(params = {}, options = {}) req = build_request(:create_training_job, params) req.send_request(options) end
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
-
`TransformJobName` - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
-
`ModelName` - Identifies the model to use. `ModelName` must be the name of an existing Amazon
SageMaker
model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. -
`TransformInput` - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
-
`TransformOutput` - Identifies the Amazon S3 location where you want Amazon
SageMaker
to save the results from the transform job. -
`TransformResources` - Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see [Batch Transform].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html
@option params [required, String] :transform_job_name
The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
@option params [required, String] :model_name
The name of the model that you want to use for the transform job. `ModelName` must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
@option params [Integer] :max_concurrent_transforms
The maximum number of parallel requests that can be sent to each instance in a transform job. If `MaxConcurrentTransforms` is set to `0` or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is `1`. For more information on execution-parameters, see [How Containers Serve Requests][1]. For built-in algorithms, you don't need to set a value for `MaxConcurrentTransforms`. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests
@option params [Types::ModelClientConfig] :model_client_config
Configures the timeout and maximum number of retries for processing a transform job invocation.
@option params [Integer] :max_payload_in_mb
The maximum allowed size of the payload, in MB. A *payload* is the data portion of a record (without metadata). The value in `MaxPayloadInMB` must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is `6` MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to `0`. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
@option params [String] :batch_strategy
Specifies the number of records to include in a mini-batch for an HTTP inference request. A *record* ** is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set the `SplitType` property to `Line`, `RecordIO`, or `TFRecord`. To use only one record when making an HTTP invocation request to a container, set `BatchStrategy` to `SingleRecord` and `SplitType` to `Line`. To fit as many records in a mini-batch as can fit within the `MaxPayloadInMB` limit, set `BatchStrategy` to `MultiRecord` and `SplitType` to `Line`.
@option params [Hash<String,String>] :environment
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
@option params [required, Types::TransformInput] :transform_input
Describes the input source and the way the transform job consumes it.
@option params [required, Types::TransformOutput] :transform_output
Describes the results of the transform job.
@option params [required, Types::TransformResources] :transform_resources
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
@option params [Types::DataProcessing] :data_processing
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see [Associate Prediction Results with their Corresponding Input Records][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html
@option params [Array<Types::Tag>] :tags
(Optional) An array of key-value pairs. For more information, see [Using Cost Allocation Tags][1] in the *Amazon Web Services Billing and Cost Management User Guide*. [1]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what
@option params [Types::ExperimentConfig] :experiment_config
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: * CreateProcessingJob * CreateTrainingJob * CreateTransformJob
@return [Types::CreateTransformJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateTransformJobResponse#transform_job_arn #transform_job_arn} => String
@example Request syntax with placeholder values
resp = client.create_transform_job({ transform_job_name: "TransformJobName", # required model_name: "ModelName", # required max_concurrent_transforms: 1, model_client_config: { invocations_timeout_in_seconds: 1, invocations_max_retries: 1, }, max_payload_in_mb: 1, batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord environment: { "TransformEnvironmentKey" => "TransformEnvironmentValue", }, transform_input: { # required data_source: { # required s3_data_source: { # required s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile s3_uri: "S3Uri", # required }, }, content_type: "ContentType", compression_type: "None", # accepts None, Gzip split_type: "None", # accepts None, Line, RecordIO, TFRecord }, transform_output: { # required s3_output_path: "S3Uri", # required accept: "Accept", assemble_with: "None", # accepts None, Line kms_key_id: "KmsKeyId", }, transform_resources: { # required instance_type: "ml.m4.xlarge", # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge instance_count: 1, # required volume_kms_key_id: "KmsKeyId", }, data_processing: { input_filter: "JsonPath", output_filter: "JsonPath", join_source: "Input", # accepts Input, None }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], experiment_config: { experiment_name: "ExperimentEntityName", trial_name: "ExperimentEntityName", trial_component_display_name: "ExperimentEntityName", }, })
@example Response structure
resp.transform_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTransformJob AWS API Documentation
@overload create_transform_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5773 def create_transform_job(params = {}, options = {}) req = build_request(:create_transform_job, params) req.send_request(options) end
Creates an SageMaker
trial. A trial is a set of steps called *trial components* that produce a machine learning model. A trial is part of a single SageMaker
experiment.
When you use SageMaker
Studio or the SageMaker
Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
@option params [required, String] :trial_name
The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
@option params [String] :display_name
The name of the trial as displayed. The name doesn't need to be unique. If `DisplayName` isn't specified, `TrialName` is displayed.
@option params [required, String] :experiment_name
The name of the experiment to associate the trial with.
@option params [Types::MetadataProperties] :metadata_properties
Metadata properties of the tracking entity, trial, or trial component.
@option params [Array<Types::Tag>] :tags
A list of tags to associate with the trial. You can use Search API to search on the tags.
@return [Types::CreateTrialResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateTrialResponse#trial_arn #trial_arn} => String
@example Request syntax with placeholder values
resp = client.create_trial({ trial_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", experiment_name: "ExperimentEntityName", # required metadata_properties: { commit_id: "MetadataPropertyValue", repository: "MetadataPropertyValue", generated_by: "MetadataPropertyValue", project_id: "MetadataPropertyValue", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.trial_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTrial AWS API Documentation
@overload create_trial
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5844 def create_trial(params = {}, options = {}) req = build_request(:create_trial, params) req.send_request(options) end
Creates a *trial component*, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker
Studio or the SageMaker
Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
@option params [required, String] :trial_component_name
The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
@option params [String] :display_name
The name of the component as displayed. The name doesn't need to be unique. If `DisplayName` isn't specified, `TrialComponentName` is displayed.
@option params [Types::TrialComponentStatus] :status
The status of the component. States include: * InProgress * Completed * Failed
@option params [Time,DateTime,Date,Integer,String] :start_time
When the component started.
@option params [Time,DateTime,Date,Integer,String] :end_time
When the component ended.
@option params [Hash<String,Types::TrialComponentParameterValue>] :parameters
The hyperparameters for the component.
@option params [Hash<String,Types::TrialComponentArtifact>] :input_artifacts
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
@option params [Hash<String,Types::TrialComponentArtifact>] :output_artifacts
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
@option params [Types::MetadataProperties] :metadata_properties
Metadata properties of the tracking entity, trial, or trial component.
@option params [Array<Types::Tag>] :tags
A list of tags to associate with the component. You can use Search API to search on the tags.
@return [Types::CreateTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateTrialComponentResponse#trial_component_arn #trial_component_arn} => String
@example Request syntax with placeholder values
resp = client.create_trial_component({ trial_component_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", status: { primary_status: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped message: "TrialComponentStatusMessage", }, start_time: Time.now, end_time: Time.now, parameters: { "TrialComponentKey256" => { string_value: "StringParameterValue", number_value: 1.0, }, }, input_artifacts: { "TrialComponentKey64" => { media_type: "MediaType", value: "TrialComponentArtifactValue", # required }, }, output_artifacts: { "TrialComponentKey64" => { media_type: "MediaType", value: "TrialComponentArtifactValue", # required }, }, metadata_properties: { commit_id: "MetadataPropertyValue", repository: "MetadataPropertyValue", generated_by: "MetadataPropertyValue", project_id: "MetadataPropertyValue", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.trial_component_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTrialComponent AWS API Documentation
@overload create_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 5962 def create_trial_component(params = {}, options = {}) req = build_request(:create_trial_component, params) req.send_request(options) end
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a “person” for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker
Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
@option params [required, String] :domain_id
The ID of the associated Domain.
@option params [required, String] :user_profile_name
A name for the UserProfile. This value is not case sensitive.
@option params [String] :single_sign_on_user_identifier
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is SSO, this field is required. If the Domain's AuthMode is not SSO, this field cannot be specified.
@option params [String] :single_sign_on_user_value
The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not SSO, this field cannot be specified.
@option params [Array<Types::Tag>] :tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
@option params [Types::UserSettings] :user_settings
A collection of settings.
@return [Types::CreateUserProfileResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateUserProfileResponse#user_profile_arn #user_profile_arn} => String
@example Request syntax with placeholder values
resp = client.create_user_profile({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required single_sign_on_user_identifier: "SingleSignOnUserIdentifier", single_sign_on_user_value: "String256", tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], user_settings: { execution_role: "RoleArn", security_groups: ["SecurityGroupId"], sharing_settings: { notebook_output_option: "Allowed", # accepts Allowed, Disabled s3_output_path: "S3Uri", s3_kms_key_id: "KmsKeyId", }, jupyter_server_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, kernel_gateway_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, custom_images: [ { image_name: "ImageName", # required image_version_number: 1, app_image_config_name: "AppImageConfigName", # required }, ], lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, tensor_board_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, }, }, })
@example Response structure
resp.user_profile_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateUserProfile AWS API Documentation
@overload create_user_profile
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6073 def create_user_profile(params = {}, options = {}) req = build_request(:create_user_profile, params) req.send_request(options) end
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use `CreateWorkforce` to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in `CognitoConfig`. You can also create an Amazon Cognito workforce using the Amazon SageMaker
console. For more information, see [ Create a Private Workforce (Amazon Cognito)].
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in `OidcConfig`. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see [ Create a Private Workforce (OIDC IdP)].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-create-private-oidc.html
@option params [Types::CognitoConfig] :cognito_config
Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single [ Amazon Cognito user pool][1]. Do not use `OidcConfig` if you specify values for `CognitoConfig`. [1]: https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html
@option params [Types::OidcConfig] :oidc_config
Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use `CognitoConfig` if you specify values for `OidcConfig`.
@option params [Types::SourceIpConfig] :source_ip_config
A list of IP address ranges ([CIDRs][1]). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses. [1]: https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html
@option params [required, String] :workforce_name
The name of the private workforce.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
@return [Types::CreateWorkforceResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateWorkforceResponse#workforce_arn #workforce_arn} => String
@example Request syntax with placeholder values
resp = client.create_workforce({ cognito_config: { user_pool: "CognitoUserPool", # required client_id: "ClientId", # required }, oidc_config: { client_id: "ClientId", # required client_secret: "ClientSecret", # required issuer: "OidcEndpoint", # required authorization_endpoint: "OidcEndpoint", # required token_endpoint: "OidcEndpoint", # required user_info_endpoint: "OidcEndpoint", # required logout_endpoint: "OidcEndpoint", # required jwks_uri: "OidcEndpoint", # required }, source_ip_config: { cidrs: ["Cidr"], # required }, workforce_name: "WorkforceName", # required tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.workforce_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateWorkforce AWS API Documentation
@overload create_workforce
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6180 def create_workforce(params = {}, options = {}) req = build_request(:create_workforce, params) req.send_request(options) end
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
@option params [required, String] :workteam_name
The name of the work team. Use this name to identify the work team.
@option params [String] :workforce_name
The name of the workforce.
@option params [required, Array<Types::MemberDefinition>] :member_definitions
A list of `MemberDefinition` objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use `CognitoMemberDefinition`. For workforces created using your own OIDC identity provider (IdP) use `OidcMemberDefinition`. Do not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito *user groups* within the user pool used to create a workforce. All of the `CognitoMemberDefinition` objects that make up the member definition must have the same `ClientId` and `UserPool` values. To add a Amazon Cognito user group to an existing worker pool, see [Adding groups to a User Pool](). For more information about user pools, see [Amazon Cognito User Pools][1]. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in `OidcMemberDefinition` by listing those groups in `Groups`. [1]: https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html
@option params [required, String] :description
A description of the work team.
@option params [Types::NotificationConfiguration] :notification_configuration
Configures notification of workers regarding available or expiring work items.
@option params [Array<Types::Tag>] :tags
An array of key-value pairs. For more information, see [Resource Tag][1] and [Using Cost Allocation Tags][2] in the <i> Amazon Web Services Billing and Cost Management User Guide</i>. [1]: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-resource-tags.html [2]: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what
@return [Types::CreateWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::CreateWorkteamResponse#workteam_arn #workteam_arn} => String
@example Request syntax with placeholder values
resp = client.create_workteam({ workteam_name: "WorkteamName", # required workforce_name: "WorkforceName", member_definitions: [ # required { cognito_member_definition: { user_pool: "CognitoUserPool", # required user_group: "CognitoUserGroup", # required client_id: "ClientId", # required }, oidc_member_definition: { groups: ["Group"], # required }, }, ], description: "String200", # required notification_configuration: { notification_topic_arn: "NotificationTopicArn", }, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@example Response structure
resp.workteam_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateWorkteam AWS API Documentation
@overload create_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6283 def create_workteam(params = {}, options = {}) req = build_request(:create_workteam, params) req.send_request(options) end
Deletes an action.
@option params [required, String] :action_name
The name of the action to delete.
@return [Types::DeleteActionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteActionResponse#action_arn #action_arn} => String
@example Request syntax with placeholder values
resp = client.delete_action({ action_name: "ExperimentEntityName", # required })
@example Response structure
resp.action_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteAction AWS API Documentation
@overload delete_action
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6311 def delete_action(params = {}, options = {}) req = build_request(:delete_action, params) req.send_request(options) end
Removes the specified algorithm from your account.
@option params [required, String] :algorithm_name
The name of the algorithm to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_algorithm({ algorithm_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteAlgorithm AWS API Documentation
@overload delete_algorithm
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6333 def delete_algorithm(params = {}, options = {}) req = build_request(:delete_algorithm, params) req.send_request(options) end
Used to stop and delete an app.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name.
@option params [required, String] :app_type
The type of app.
@option params [required, String] :app_name
The name of the app.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_app({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard app_name: "AppName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteApp AWS API Documentation
@overload delete_app
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6367 def delete_app(params = {}, options = {}) req = build_request(:delete_app, params) req.send_request(options) end
Deletes an AppImageConfig.
@option params [required, String] :app_image_config_name
The name of the AppImageConfig to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_app_image_config({ app_image_config_name: "AppImageConfigName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteAppImageConfig AWS API Documentation
@overload delete_app_image_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6389 def delete_app_image_config(params = {}, options = {}) req = build_request(:delete_app_image_config, params) req.send_request(options) end
Deletes an artifact. Either `ArtifactArn` or `Source` must be specified.
@option params [String] :artifact_arn
The Amazon Resource Name (ARN) of the artifact to delete.
@option params [Types::ArtifactSource] :source
The URI of the source.
@return [Types::DeleteArtifactResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteArtifactResponse#artifact_arn #artifact_arn} => String
@example Request syntax with placeholder values
resp = client.delete_artifact({ artifact_arn: "ArtifactArn", source: { source_uri: "String2048", # required source_types: [ { source_id_type: "MD5Hash", # required, accepts MD5Hash, S3ETag, S3Version, Custom value: "String256", # required }, ], }, })
@example Response structure
resp.artifact_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteArtifact AWS API Documentation
@overload delete_artifact
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6430 def delete_artifact(params = {}, options = {}) req = build_request(:delete_artifact, params) req.send_request(options) end
Deletes an association.
@option params [required, String] :source_arn
The ARN of the source.
@option params [required, String] :destination_arn
The Amazon Resource Name (ARN) of the destination.
@return [Types::DeleteAssociationResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteAssociationResponse#source_arn #source_arn} => String * {Types::DeleteAssociationResponse#destination_arn #destination_arn} => String
@example Request syntax with placeholder values
resp = client.delete_association({ source_arn: "AssociationEntityArn", # required destination_arn: "AssociationEntityArn", # required })
@example Response structure
resp.source_arn #=> String resp.destination_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteAssociation AWS API Documentation
@overload delete_association
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6464 def delete_association(params = {}, options = {}) req = build_request(:delete_association, params) req.send_request(options) end
Deletes the specified Git repository from your account.
@option params [required, String] :code_repository_name
The name of the Git repository to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_code_repository({ code_repository_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteCodeRepository AWS API Documentation
@overload delete_code_repository
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6486 def delete_code_repository(params = {}, options = {}) req = build_request(:delete_code_repository, params) req.send_request(options) end
Deletes an context.
@option params [required, String] :context_name
The name of the context to delete.
@return [Types::DeleteContextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteContextResponse#context_arn #context_arn} => String
@example Request syntax with placeholder values
resp = client.delete_context({ context_name: "ExperimentEntityName", # required })
@example Response structure
resp.context_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteContext AWS API Documentation
@overload delete_context
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6514 def delete_context(params = {}, options = {}) req = build_request(:delete_context, params) req.send_request(options) end
Deletes a data quality monitoring job definition.
@option params [required, String] :job_definition_name
The name of the data quality monitoring job definition to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_data_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteDataQualityJobDefinition AWS API Documentation
@overload delete_data_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6536 def delete_data_quality_job_definition(params = {}, options = {}) req = build_request(:delete_data_quality_job_definition, params) req.send_request(options) end
Deletes a fleet.
@option params [required, String] :device_fleet_name
The name of the fleet to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_device_fleet({ device_fleet_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteDeviceFleet AWS API Documentation
@overload delete_device_fleet
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6558 def delete_device_fleet(params = {}, options = {}) req = build_request(:delete_device_fleet, params) req.send_request(options) end
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
@option params [required, String] :domain_id
The domain ID.
@option params [Types::RetentionPolicy] :retention_policy
The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_domain({ domain_id: "DomainId", # required retention_policy: { home_efs_file_system: "Retain", # accepts Retain, Delete }, })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteDomain AWS API Documentation
@overload delete_domain
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6591 def delete_domain(params = {}, options = {}) req = build_request(:delete_domain, params) req.send_request(options) end
Deletes an endpoint. Amazon SageMaker
frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker
retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the [RevokeGrant] API call.
[1]: docs.aws.amazon.com/kms/latest/APIReference/API_RevokeGrant.html
@option params [required, String] :endpoint_name
The name of the endpoint that you want to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_endpoint({ endpoint_name: "EndpointName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteEndpoint AWS API Documentation
@overload delete_endpoint
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6622 def delete_endpoint(params = {}, options = {}) req = build_request(:delete_endpoint, params) req.send_request(options) end
Deletes an endpoint configuration. The `DeleteEndpointConfig` API deletes only the specified configuration. It does not delete endpoints created using the configuration.
You must not delete an `EndpointConfig` in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. If you delete the `EndpointConfig` of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
@option params [required, String] :endpoint_config_name
The name of the endpoint configuration that you want to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_endpoint_config({ endpoint_config_name: "EndpointConfigName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteEndpointConfig AWS API Documentation
@overload delete_endpoint_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6653 def delete_endpoint_config(params = {}, options = {}) req = build_request(:delete_endpoint_config, params) req.send_request(options) end
Deletes an SageMaker
experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
@option params [required, String] :experiment_name
The name of the experiment to delete.
@return [Types::DeleteExperimentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteExperimentResponse#experiment_arn #experiment_arn} => String
@example Request syntax with placeholder values
resp = client.delete_experiment({ experiment_name: "ExperimentEntityName", # required })
@example Response structure
resp.experiment_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteExperiment AWS API Documentation
@overload delete_experiment
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6683 def delete_experiment(params = {}, options = {}) req = build_request(:delete_experiment, params) req.send_request(options) end
Delete the `FeatureGroup` and any data that was written to the `OnlineStore` of the `FeatureGroup`. Data cannot be accessed from the `OnlineStore` immediately after `DeleteFeatureGroup` is called.
Data written into the `OfflineStore` will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your `OfflineStore` are not deleted.
@option params [required, String] :feature_group_name
The name of the `FeatureGroup` you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_feature_group({ feature_group_name: "FeatureGroupName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteFeatureGroup AWS API Documentation
@overload delete_feature_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6713 def delete_feature_group(params = {}, options = {}) req = build_request(:delete_feature_group, params) req.send_request(options) end
Deletes the specified flow definition.
@option params [required, String] :flow_definition_name
The name of the flow definition you are deleting.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_flow_definition({ flow_definition_name: "FlowDefinitionName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteFlowDefinition AWS API Documentation
@overload delete_flow_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6735 def delete_flow_definition(params = {}, options = {}) req = build_request(:delete_flow_definition, params) req.send_request(options) end
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker task template, it no longer appears when you call `ListHumanTaskUis`.
@option params [required, String] :human_task_ui_name
The name of the human task user interface (work task template) you want to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_human_task_ui({ human_task_ui_name: "HumanTaskUiName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteHumanTaskUi AWS API Documentation
@overload delete_human_task_ui
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6763 def delete_human_task_ui(params = {}, options = {}) req = build_request(:delete_human_task_ui, params) req.send_request(options) end
Deletes a SageMaker
image and all versions of the image. The container images aren't deleted.
@option params [required, String] :image_name
The name of the image to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_image({ image_name: "ImageName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteImage AWS API Documentation
@overload delete_image
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6786 def delete_image(params = {}, options = {}) req = build_request(:delete_image, params) req.send_request(options) end
Deletes a version of a SageMaker
image. The container image the version represents isn't deleted.
@option params [required, String] :image_name
The name of the image.
@option params [required, Integer] :version
The version to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_image_version({ image_name: "ImageName", # required version: 1, # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteImageVersion AWS API Documentation
@overload delete_image_version
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6813 def delete_image_version(params = {}, options = {}) req = build_request(:delete_image_version, params) req.send_request(options) end
Deletes a model. The `DeleteModel` API deletes only the model entry that was created in Amazon SageMaker
when you called the `CreateModel` API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
@option params [required, String] :model_name
The name of the model to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model({ model_name: "ModelName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModel AWS API Documentation
@overload delete_model
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6838 def delete_model(params = {}, options = {}) req = build_request(:delete_model, params) req.send_request(options) end
Deletes an Amazon SageMaker
model bias job definition.
@option params [required, String] :job_definition_name
The name of the model bias job definition to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_bias_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelBiasJobDefinition AWS API Documentation
@overload delete_model_bias_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6860 def delete_model_bias_job_definition(params = {}, options = {}) req = build_request(:delete_model_bias_job_definition, params) req.send_request(options) end
Deletes an Amazon SageMaker
model explainability job definition.
@option params [required, String] :job_definition_name
The name of the model explainability job definition to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_explainability_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelExplainabilityJobDefinition AWS API Documentation
@overload delete_model_explainability_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6882 def delete_model_explainability_job_definition(params = {}, options = {}) req = build_request(:delete_model_explainability_job_definition, params) req.send_request(options) end
Deletes a model package.
A model package is used to create Amazon SageMaker
models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker
.
@option params [required, String] :model_package_name
The name or Amazon Resource Name (ARN) of the model package to delete. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_package({ model_package_name: "VersionedArnOrName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelPackage AWS API Documentation
@overload delete_model_package
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6912 def delete_model_package(params = {}, options = {}) req = build_request(:delete_model_package, params) req.send_request(options) end
Deletes the specified model group.
@option params [required, String] :model_package_group_name
The name of the model group to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_package_group({ model_package_group_name: "ArnOrName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelPackageGroup AWS API Documentation
@overload delete_model_package_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6934 def delete_model_package_group(params = {}, options = {}) req = build_request(:delete_model_package_group, params) req.send_request(options) end
Deletes a model group resource policy.
@option params [required, String] :model_package_group_name
The name of the model group for which to delete the policy.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_package_group_policy({ model_package_group_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelPackageGroupPolicy AWS API Documentation
@overload delete_model_package_group_policy
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6956 def delete_model_package_group_policy(params = {}, options = {}) req = build_request(:delete_model_package_group_policy, params) req.send_request(options) end
Deletes the secified model quality monitoring job definition.
@option params [required, String] :job_definition_name
The name of the model quality monitoring job definition to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_model_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteModelQualityJobDefinition AWS API Documentation
@overload delete_model_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 6978 def delete_model_quality_job_definition(params = {}, options = {}) req = build_request(:delete_model_quality_job_definition, params) req.send_request(options) end
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
@option params [required, String] :monitoring_schedule_name
The name of the monitoring schedule to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteMonitoringSchedule AWS API Documentation
@overload delete_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7002 def delete_monitoring_schedule(params = {}, options = {}) req = build_request(:delete_monitoring_schedule, params) req.send_request(options) end
Deletes an Amazon SageMaker
notebook instance. Before you can delete a notebook instance, you must call the `StopNotebookInstance` API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker
removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
@option params [required, String] :notebook_instance_name
The name of the Amazon SageMaker notebook instance to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteNotebookInstance AWS API Documentation
@overload delete_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7030 def delete_notebook_instance(params = {}, options = {}) req = build_request(:delete_notebook_instance, params) req.send_request(options) end
Deletes a notebook instance lifecycle configuration.
@option params [required, String] :notebook_instance_lifecycle_config_name
The name of the lifecycle configuration to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_notebook_instance_lifecycle_config({ notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteNotebookInstanceLifecycleConfig AWS API Documentation
@overload delete_notebook_instance_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7052 def delete_notebook_instance_lifecycle_config(params = {}, options = {}) req = build_request(:delete_notebook_instance_lifecycle_config, params) req.send_request(options) end
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the `StopPipelineExecution` API. When you delete a pipeline, all instances of the pipeline are deleted.
@option params [required, String] :pipeline_name
The name of the pipeline to delete.
@option params [required, String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::DeletePipelineResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeletePipelineResponse#pipeline_arn #pipeline_arn} => String
@example Request syntax with placeholder values
resp = client.delete_pipeline({ pipeline_name: "PipelineName", # required client_request_token: "IdempotencyToken", # required })
@example Response structure
resp.pipeline_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeletePipeline AWS API Documentation
@overload delete_pipeline
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7092 def delete_pipeline(params = {}, options = {}) req = build_request(:delete_pipeline, params) req.send_request(options) end
Delete the specified project.
@option params [required, String] :project_name
The name of the project to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_project({ project_name: "ProjectEntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteProject AWS API Documentation
@overload delete_project
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7114 def delete_project(params = {}, options = {}) req = build_request(:delete_project, params) req.send_request(options) end
Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
@option params [required, String] :studio_lifecycle_config_name
The name of the Studio Lifecycle Configuration to delete.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_studio_lifecycle_config({ studio_lifecycle_config_name: "StudioLifecycleConfigName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteStudioLifecycleConfig AWS API Documentation
@overload delete_studio_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7139 def delete_studio_lifecycle_config(params = {}, options = {}) req = build_request(:delete_studio_lifecycle_config, params) req.send_request(options) end
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
@option params [required, String] :trial_name
The name of the trial to delete.
@return [Types::DeleteTrialResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteTrialResponse#trial_arn #trial_arn} => String
@example Request syntax with placeholder values
resp = client.delete_trial({ trial_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteTrial AWS API Documentation
@overload delete_trial
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7211 def delete_trial(params = {}, options = {}) req = build_request(:delete_trial, params) req.send_request(options) end
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
@option params [required, String] :trial_component_name
The name of the component to delete.
@return [Types::DeleteTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteTrialComponentResponse#trial_component_arn #trial_component_arn} => String
@example Request syntax with placeholder values
resp = client.delete_trial_component({ trial_component_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_component_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteTrialComponent AWS API Documentation
@overload delete_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7242 def delete_trial_component(params = {}, options = {}) req = build_request(:delete_trial_component, params) req.send_request(options) end
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_user_profile({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteUserProfile AWS API Documentation
@overload delete_user_profile
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7270 def delete_user_profile(params = {}, options = {}) req = build_request(:delete_user_profile, params) req.send_request(options) end
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a `ResourceInUse` error.
@option params [required, String] :workforce_name
The name of the workforce.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.delete_workforce({ workforce_name: "WorkforceName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteWorkforce AWS API Documentation
@overload delete_workforce
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7301 def delete_workforce(params = {}, options = {}) req = build_request(:delete_workforce, params) req.send_request(options) end
Deletes an existing work team. This operation can't be undone.
@option params [required, String] :workteam_name
The name of the work team to delete.
@return [Types::DeleteWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DeleteWorkteamResponse#success #success} => Boolean
@example Request syntax with placeholder values
resp = client.delete_workteam({ workteam_name: "WorkteamName", # required })
@example Response structure
resp.success #=> Boolean
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeleteWorkteam AWS API Documentation
@overload delete_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7329 def delete_workteam(params = {}, options = {}) req = build_request(:delete_workteam, params) req.send_request(options) end
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
@option params [required, String] :device_fleet_name
The name of the fleet the devices belong to.
@option params [required, Array<String>] :device_names
The unique IDs of the devices.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.deregister_devices({ device_fleet_name: "EntityName", # required device_names: ["DeviceName"], # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DeregisterDevices AWS API Documentation
@overload deregister_devices
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7356 def deregister_devices(params = {}, options = {}) req = build_request(:deregister_devices, params) req.send_request(options) end
Describes an action.
@option params [required, String] :action_name
The name of the action to describe.
@return [Types::DescribeActionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeActionResponse#action_name #action_name} => String * {Types::DescribeActionResponse#action_arn #action_arn} => String * {Types::DescribeActionResponse#source #source} => Types::ActionSource * {Types::DescribeActionResponse#action_type #action_type} => String * {Types::DescribeActionResponse#description #description} => String * {Types::DescribeActionResponse#status #status} => String * {Types::DescribeActionResponse#properties #properties} => Hash<String,String> * {Types::DescribeActionResponse#creation_time #creation_time} => Time * {Types::DescribeActionResponse#created_by #created_by} => Types::UserContext * {Types::DescribeActionResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeActionResponse#last_modified_by #last_modified_by} => Types::UserContext * {Types::DescribeActionResponse#metadata_properties #metadata_properties} => Types::MetadataProperties
@example Request syntax with placeholder values
resp = client.describe_action({ action_name: "ExperimentEntityName", # required })
@example Response structure
resp.action_name #=> String resp.action_arn #=> String resp.source.source_uri #=> String resp.source.source_type #=> String resp.source.source_id #=> String resp.action_type #=> String resp.description #=> String resp.status #=> String, one of "Unknown", "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.properties #=> Hash resp.properties["StringParameterValue"] #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String resp.metadata_properties.commit_id #=> String resp.metadata_properties.repository #=> String resp.metadata_properties.generated_by #=> String resp.metadata_properties.project_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeAction AWS API Documentation
@overload describe_action
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7416 def describe_action(params = {}, options = {}) req = build_request(:describe_action, params) req.send_request(options) end
Returns a description of the specified algorithm that is in your account.
@option params [required, String] :algorithm_name
The name of the algorithm to describe.
@return [Types::DescribeAlgorithmOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeAlgorithmOutput#algorithm_name #algorithm_name} => String * {Types::DescribeAlgorithmOutput#algorithm_arn #algorithm_arn} => String * {Types::DescribeAlgorithmOutput#algorithm_description #algorithm_description} => String * {Types::DescribeAlgorithmOutput#creation_time #creation_time} => Time * {Types::DescribeAlgorithmOutput#training_specification #training_specification} => Types::TrainingSpecification * {Types::DescribeAlgorithmOutput#inference_specification #inference_specification} => Types::InferenceSpecification * {Types::DescribeAlgorithmOutput#validation_specification #validation_specification} => Types::AlgorithmValidationSpecification * {Types::DescribeAlgorithmOutput#algorithm_status #algorithm_status} => String * {Types::DescribeAlgorithmOutput#algorithm_status_details #algorithm_status_details} => Types::AlgorithmStatusDetails * {Types::DescribeAlgorithmOutput#product_id #product_id} => String * {Types::DescribeAlgorithmOutput#certify_for_marketplace #certify_for_marketplace} => Boolean
@example Request syntax with placeholder values
resp = client.describe_algorithm({ algorithm_name: "ArnOrName", # required })
@example Response structure
resp.algorithm_name #=> String resp.algorithm_arn #=> String resp.algorithm_description #=> String resp.creation_time #=> Time resp.training_specification.training_image #=> String resp.training_specification.training_image_digest #=> String resp.training_specification.supported_hyper_parameters #=> Array resp.training_specification.supported_hyper_parameters[0].name #=> String resp.training_specification.supported_hyper_parameters[0].description #=> String resp.training_specification.supported_hyper_parameters[0].type #=> String, one of "Integer", "Continuous", "Categorical", "FreeText" resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.min_value #=> String resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.max_value #=> String resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.min_value #=> String resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.max_value #=> String resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values #=> Array resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values[0] #=> String resp.training_specification.supported_hyper_parameters[0].is_tunable #=> Boolean resp.training_specification.supported_hyper_parameters[0].is_required #=> Boolean resp.training_specification.supported_hyper_parameters[0].default_value #=> String resp.training_specification.supported_training_instance_types #=> Array resp.training_specification.supported_training_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.training_specification.supports_distributed_training #=> Boolean resp.training_specification.metric_definitions #=> Array resp.training_specification.metric_definitions[0].name #=> String resp.training_specification.metric_definitions[0].regex #=> String resp.training_specification.training_channels #=> Array resp.training_specification.training_channels[0].name #=> String resp.training_specification.training_channels[0].description #=> String resp.training_specification.training_channels[0].is_required #=> Boolean resp.training_specification.training_channels[0].supported_content_types #=> Array resp.training_specification.training_channels[0].supported_content_types[0] #=> String resp.training_specification.training_channels[0].supported_compression_types #=> Array resp.training_specification.training_channels[0].supported_compression_types[0] #=> String, one of "None", "Gzip" resp.training_specification.training_channels[0].supported_input_modes #=> Array resp.training_specification.training_channels[0].supported_input_modes[0] #=> String, one of "Pipe", "File" resp.training_specification.supported_tuning_job_objective_metrics #=> Array resp.training_specification.supported_tuning_job_objective_metrics[0].type #=> String, one of "Maximize", "Minimize" resp.training_specification.supported_tuning_job_objective_metrics[0].metric_name #=> String resp.inference_specification.containers #=> Array resp.inference_specification.containers[0].container_hostname #=> String resp.inference_specification.containers[0].image #=> String resp.inference_specification.containers[0].image_digest #=> String resp.inference_specification.containers[0].model_data_url #=> String resp.inference_specification.containers[0].product_id #=> String resp.inference_specification.containers[0].environment #=> Hash resp.inference_specification.containers[0].environment["EnvironmentKey"] #=> String resp.inference_specification.supported_transform_instance_types #=> Array resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.inference_specification.supported_realtime_inference_instance_types #=> Array resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge" resp.inference_specification.supported_content_types #=> Array resp.inference_specification.supported_content_types[0] #=> String resp.inference_specification.supported_response_mime_types #=> Array resp.inference_specification.supported_response_mime_types[0] #=> String resp.validation_specification.validation_role #=> String resp.validation_specification.validation_profiles #=> Array resp.validation_specification.validation_profiles[0].profile_name #=> String resp.validation_specification.validation_profiles[0].training_job_definition.training_input_mode #=> String, one of "Pipe", "File" resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters #=> Hash resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters["HyperParameterKey"] #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config #=> Array resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].channel_name #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].content_type #=> String resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.kms_key_id #=> String resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.s3_output_path #=> String resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_count #=> Integer resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_size_in_gb #=> Integer resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_kms_key_id #=> String resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord" resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String resp.algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting" resp.algorithm_status_details.validation_statuses #=> Array resp.algorithm_status_details.validation_statuses[0].name #=> String resp.algorithm_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.algorithm_status_details.validation_statuses[0].failure_reason #=> String resp.algorithm_status_details.image_scan_statuses #=> Array resp.algorithm_status_details.image_scan_statuses[0].name #=> String resp.algorithm_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.algorithm_status_details.image_scan_statuses[0].failure_reason #=> String resp.product_id #=> String resp.certify_for_marketplace #=> Boolean
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeAlgorithm AWS API Documentation
@overload describe_algorithm
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7566 def describe_algorithm(params = {}, options = {}) req = build_request(:describe_algorithm, params) req.send_request(options) end
Describes the app.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name.
@option params [required, String] :app_type
The type of app.
@option params [required, String] :app_name
The name of the app.
@return [Types::DescribeAppResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeAppResponse#app_arn #app_arn} => String * {Types::DescribeAppResponse#app_type #app_type} => String * {Types::DescribeAppResponse#app_name #app_name} => String * {Types::DescribeAppResponse#domain_id #domain_id} => String * {Types::DescribeAppResponse#user_profile_name #user_profile_name} => String * {Types::DescribeAppResponse#status #status} => String * {Types::DescribeAppResponse#last_health_check_timestamp #last_health_check_timestamp} => Time * {Types::DescribeAppResponse#last_user_activity_timestamp #last_user_activity_timestamp} => Time * {Types::DescribeAppResponse#creation_time #creation_time} => Time * {Types::DescribeAppResponse#failure_reason #failure_reason} => String * {Types::DescribeAppResponse#resource_spec #resource_spec} => Types::ResourceSpec
@example Request syntax with placeholder values
resp = client.describe_app({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard app_name: "AppName", # required })
@example Response structure
resp.app_arn #=> String resp.app_type #=> String, one of "JupyterServer", "KernelGateway", "TensorBoard" resp.app_name #=> String resp.domain_id #=> String resp.user_profile_name #=> String resp.status #=> String, one of "Deleted", "Deleting", "Failed", "InService", "Pending" resp.last_health_check_timestamp #=> Time resp.last_user_activity_timestamp #=> Time resp.creation_time #=> Time resp.failure_reason #=> String resp.resource_spec.sage_maker_image_arn #=> String resp.resource_spec.sage_maker_image_version_arn #=> String resp.resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.resource_spec.lifecycle_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeApp AWS API Documentation
@overload describe_app
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7629 def describe_app(params = {}, options = {}) req = build_request(:describe_app, params) req.send_request(options) end
Describes an AppImageConfig.
@option params [required, String] :app_image_config_name
The name of the AppImageConfig to describe.
@return [Types::DescribeAppImageConfigResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeAppImageConfigResponse#app_image_config_arn #app_image_config_arn} => String * {Types::DescribeAppImageConfigResponse#app_image_config_name #app_image_config_name} => String * {Types::DescribeAppImageConfigResponse#creation_time #creation_time} => Time * {Types::DescribeAppImageConfigResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeAppImageConfigResponse#kernel_gateway_image_config #kernel_gateway_image_config} => Types::KernelGatewayImageConfig
@example Request syntax with placeholder values
resp = client.describe_app_image_config({ app_image_config_name: "AppImageConfigName", # required })
@example Response structure
resp.app_image_config_arn #=> String resp.app_image_config_name #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.kernel_gateway_image_config.kernel_specs #=> Array resp.kernel_gateway_image_config.kernel_specs[0].name #=> String resp.kernel_gateway_image_config.kernel_specs[0].display_name #=> String resp.kernel_gateway_image_config.file_system_config.mount_path #=> String resp.kernel_gateway_image_config.file_system_config.default_uid #=> Integer resp.kernel_gateway_image_config.file_system_config.default_gid #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeAppImageConfig AWS API Documentation
@overload describe_app_image_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7670 def describe_app_image_config(params = {}, options = {}) req = build_request(:describe_app_image_config, params) req.send_request(options) end
Describes an artifact.
@option params [required, String] :artifact_arn
The Amazon Resource Name (ARN) of the artifact to describe.
@return [Types::DescribeArtifactResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeArtifactResponse#artifact_name #artifact_name} => String * {Types::DescribeArtifactResponse#artifact_arn #artifact_arn} => String * {Types::DescribeArtifactResponse#source #source} => Types::ArtifactSource * {Types::DescribeArtifactResponse#artifact_type #artifact_type} => String * {Types::DescribeArtifactResponse#properties #properties} => Hash<String,String> * {Types::DescribeArtifactResponse#creation_time #creation_time} => Time * {Types::DescribeArtifactResponse#created_by #created_by} => Types::UserContext * {Types::DescribeArtifactResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeArtifactResponse#last_modified_by #last_modified_by} => Types::UserContext * {Types::DescribeArtifactResponse#metadata_properties #metadata_properties} => Types::MetadataProperties
@example Request syntax with placeholder values
resp = client.describe_artifact({ artifact_arn: "ArtifactArn", # required })
@example Response structure
resp.artifact_name #=> String resp.artifact_arn #=> String resp.source.source_uri #=> String resp.source.source_types #=> Array resp.source.source_types[0].source_id_type #=> String, one of "MD5Hash", "S3ETag", "S3Version", "Custom" resp.source.source_types[0].value #=> String resp.artifact_type #=> String resp.properties #=> Hash resp.properties["StringParameterValue"] #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String resp.metadata_properties.commit_id #=> String resp.metadata_properties.repository #=> String resp.metadata_properties.generated_by #=> String resp.metadata_properties.project_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeArtifact AWS API Documentation
@overload describe_artifact
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7727 def describe_artifact(params = {}, options = {}) req = build_request(:describe_artifact, params) req.send_request(options) end
Returns information about an Amazon SageMaker
AutoML job.
@option params [required, String] :auto_ml_job_name
Requests information about an AutoML job using its unique name.
@return [Types::DescribeAutoMLJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeAutoMLJobResponse#auto_ml_job_name #auto_ml_job_name} => String * {Types::DescribeAutoMLJobResponse#auto_ml_job_arn #auto_ml_job_arn} => String * {Types::DescribeAutoMLJobResponse#input_data_config #input_data_config} => Array<Types::AutoMLChannel> * {Types::DescribeAutoMLJobResponse#output_data_config #output_data_config} => Types::AutoMLOutputDataConfig * {Types::DescribeAutoMLJobResponse#role_arn #role_arn} => String * {Types::DescribeAutoMLJobResponse#auto_ml_job_objective #auto_ml_job_objective} => Types::AutoMLJobObjective * {Types::DescribeAutoMLJobResponse#problem_type #problem_type} => String * {Types::DescribeAutoMLJobResponse#auto_ml_job_config #auto_ml_job_config} => Types::AutoMLJobConfig * {Types::DescribeAutoMLJobResponse#creation_time #creation_time} => Time * {Types::DescribeAutoMLJobResponse#end_time #end_time} => Time * {Types::DescribeAutoMLJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeAutoMLJobResponse#failure_reason #failure_reason} => String * {Types::DescribeAutoMLJobResponse#partial_failure_reasons #partial_failure_reasons} => Array<Types::AutoMLPartialFailureReason> * {Types::DescribeAutoMLJobResponse#best_candidate #best_candidate} => Types::AutoMLCandidate * {Types::DescribeAutoMLJobResponse#auto_ml_job_status #auto_ml_job_status} => String * {Types::DescribeAutoMLJobResponse#auto_ml_job_secondary_status #auto_ml_job_secondary_status} => String * {Types::DescribeAutoMLJobResponse#generate_candidate_definitions_only #generate_candidate_definitions_only} => Boolean * {Types::DescribeAutoMLJobResponse#auto_ml_job_artifacts #auto_ml_job_artifacts} => Types::AutoMLJobArtifacts * {Types::DescribeAutoMLJobResponse#resolved_attributes #resolved_attributes} => Types::ResolvedAttributes * {Types::DescribeAutoMLJobResponse#model_deploy_config #model_deploy_config} => Types::ModelDeployConfig * {Types::DescribeAutoMLJobResponse#model_deploy_result #model_deploy_result} => Types::ModelDeployResult
@example Request syntax with placeholder values
resp = client.describe_auto_ml_job({ auto_ml_job_name: "AutoMLJobName", # required })
@example Response structure
resp.auto_ml_job_name #=> String resp.auto_ml_job_arn #=> String resp.input_data_config #=> Array resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix" resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.input_data_config[0].target_attribute_name #=> String resp.output_data_config.kms_key_id #=> String resp.output_data_config.s3_output_path #=> String resp.role_arn #=> String resp.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression" resp.auto_ml_job_config.completion_criteria.max_candidates #=> Integer resp.auto_ml_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer resp.auto_ml_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer resp.auto_ml_job_config.security_config.volume_kms_key_id #=> String resp.auto_ml_job_config.security_config.enable_inter_container_traffic_encryption #=> Boolean resp.auto_ml_job_config.security_config.vpc_config.security_group_ids #=> Array resp.auto_ml_job_config.security_config.vpc_config.security_group_ids[0] #=> String resp.auto_ml_job_config.security_config.vpc_config.subnets #=> Array resp.auto_ml_job_config.security_config.vpc_config.subnets[0] #=> String resp.creation_time #=> Time resp.end_time #=> Time resp.last_modified_time #=> Time resp.failure_reason #=> String resp.partial_failure_reasons #=> Array resp.partial_failure_reasons[0].partial_failure_message #=> String resp.best_candidate.candidate_name #=> String resp.best_candidate.final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize" resp.best_candidate.final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.best_candidate.final_auto_ml_job_objective_metric.value #=> Float resp.best_candidate.objective_status #=> String, one of "Succeeded", "Pending", "Failed" resp.best_candidate.candidate_steps #=> Array resp.best_candidate.candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob" resp.best_candidate.candidate_steps[0].candidate_step_arn #=> String resp.best_candidate.candidate_steps[0].candidate_step_name #=> String resp.best_candidate.candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.best_candidate.inference_containers #=> Array resp.best_candidate.inference_containers[0].image #=> String resp.best_candidate.inference_containers[0].model_data_url #=> String resp.best_candidate.inference_containers[0].environment #=> Hash resp.best_candidate.inference_containers[0].environment["EnvironmentKey"] #=> String resp.best_candidate.creation_time #=> Time resp.best_candidate.end_time #=> Time resp.best_candidate.last_modified_time #=> Time resp.best_candidate.failure_reason #=> String resp.best_candidate.candidate_properties.candidate_artifact_locations.explainability #=> String resp.best_candidate.candidate_properties.candidate_metrics #=> Array resp.best_candidate.candidate_properties.candidate_metrics[0].metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.best_candidate.candidate_properties.candidate_metrics[0].value #=> Float resp.best_candidate.candidate_properties.candidate_metrics[0].set #=> String, one of "Train", "Validation", "Test" resp.auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.auto_ml_job_secondary_status #=> String, one of "Starting", "AnalyzingData", "FeatureEngineering", "ModelTuning", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated", "GeneratingExplainabilityReport", "Completed", "ExplainabilityError", "DeployingModel", "ModelDeploymentError" resp.generate_candidate_definitions_only #=> Boolean resp.auto_ml_job_artifacts.candidate_definition_notebook_location #=> String resp.auto_ml_job_artifacts.data_exploration_notebook_location #=> String resp.resolved_attributes.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.resolved_attributes.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression" resp.resolved_attributes.completion_criteria.max_candidates #=> Integer resp.resolved_attributes.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer resp.resolved_attributes.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer resp.model_deploy_config.auto_generate_endpoint_name #=> Boolean resp.model_deploy_config.endpoint_name #=> String resp.model_deploy_result.endpoint_name #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeAutoMLJob AWS API Documentation
@overload describe_auto_ml_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7838 def describe_auto_ml_job(params = {}, options = {}) req = build_request(:describe_auto_ml_job, params) req.send_request(options) end
Gets details about the specified Git repository.
@option params [required, String] :code_repository_name
The name of the Git repository to describe.
@return [Types::DescribeCodeRepositoryOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeCodeRepositoryOutput#code_repository_name #code_repository_name} => String * {Types::DescribeCodeRepositoryOutput#code_repository_arn #code_repository_arn} => String * {Types::DescribeCodeRepositoryOutput#creation_time #creation_time} => Time * {Types::DescribeCodeRepositoryOutput#last_modified_time #last_modified_time} => Time * {Types::DescribeCodeRepositoryOutput#git_config #git_config} => Types::GitConfig
@example Request syntax with placeholder values
resp = client.describe_code_repository({ code_repository_name: "EntityName", # required })
@example Response structure
resp.code_repository_name #=> String resp.code_repository_arn #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.git_config.repository_url #=> String resp.git_config.branch #=> String resp.git_config.secret_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeCodeRepository AWS API Documentation
@overload describe_code_repository
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7876 def describe_code_repository(params = {}, options = {}) req = build_request(:describe_code_repository, params) req.send_request(options) end
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
@option params [required, String] :compilation_job_name
The name of the model compilation job that you want information about.
@return [Types::DescribeCompilationJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeCompilationJobResponse#compilation_job_name #compilation_job_name} => String * {Types::DescribeCompilationJobResponse#compilation_job_arn #compilation_job_arn} => String * {Types::DescribeCompilationJobResponse#compilation_job_status #compilation_job_status} => String * {Types::DescribeCompilationJobResponse#compilation_start_time #compilation_start_time} => Time * {Types::DescribeCompilationJobResponse#compilation_end_time #compilation_end_time} => Time * {Types::DescribeCompilationJobResponse#stopping_condition #stopping_condition} => Types::StoppingCondition * {Types::DescribeCompilationJobResponse#inference_image #inference_image} => String * {Types::DescribeCompilationJobResponse#creation_time #creation_time} => Time * {Types::DescribeCompilationJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeCompilationJobResponse#failure_reason #failure_reason} => String * {Types::DescribeCompilationJobResponse#model_artifacts #model_artifacts} => Types::ModelArtifacts * {Types::DescribeCompilationJobResponse#model_digests #model_digests} => Types::ModelDigests * {Types::DescribeCompilationJobResponse#role_arn #role_arn} => String * {Types::DescribeCompilationJobResponse#input_config #input_config} => Types::InputConfig * {Types::DescribeCompilationJobResponse#output_config #output_config} => Types::OutputConfig * {Types::DescribeCompilationJobResponse#vpc_config #vpc_config} => Types::NeoVpcConfig
@example Request syntax with placeholder values
resp = client.describe_compilation_job({ compilation_job_name: "EntityName", # required })
@example Response structure
resp.compilation_job_name #=> String resp.compilation_job_arn #=> String resp.compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED" resp.compilation_start_time #=> Time resp.compilation_end_time #=> Time resp.stopping_condition.max_runtime_in_seconds #=> Integer resp.stopping_condition.max_wait_time_in_seconds #=> Integer resp.inference_image #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.failure_reason #=> String resp.model_artifacts.s3_model_artifacts #=> String resp.model_digests.artifact_digest #=> String resp.role_arn #=> String resp.input_config.s3_uri #=> String resp.input_config.data_input_config #=> String resp.input_config.framework #=> String, one of "TENSORFLOW", "KERAS", "MXNET", "ONNX", "PYTORCH", "XGBOOST", "TFLITE", "DARKNET", "SKLEARN" resp.input_config.framework_version #=> String resp.output_config.s3_output_location #=> String resp.output_config.target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_c4", "ml_c5", "ml_p2", "ml_p3", "ml_g4dn", "ml_inf1", "ml_eia2", "jetson_tx1", "jetson_tx2", "jetson_nano", "jetson_xavier", "rasp3b", "imx8qm", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603", "sitara_am57x", "amba_cv22", "amba_cv25", "x86_win32", "x86_win64", "coreml", "jacinto_tda4vm", "imx8mplus" resp.output_config.target_platform.os #=> String, one of "ANDROID", "LINUX" resp.output_config.target_platform.arch #=> String, one of "X86_64", "X86", "ARM64", "ARM_EABI", "ARM_EABIHF" resp.output_config.target_platform.accelerator #=> String, one of "INTEL_GRAPHICS", "MALI", "NVIDIA" resp.output_config.compiler_options #=> String resp.output_config.kms_key_id #=> String resp.vpc_config.security_group_ids #=> Array resp.vpc_config.security_group_ids[0] #=> String resp.vpc_config.subnets #=> Array resp.vpc_config.subnets[0] #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeCompilationJob AWS API Documentation
@overload describe_compilation_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 7951 def describe_compilation_job(params = {}, options = {}) req = build_request(:describe_compilation_job, params) req.send_request(options) end
Describes a context.
@option params [required, String] :context_name
The name of the context to describe.
@return [Types::DescribeContextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeContextResponse#context_name #context_name} => String * {Types::DescribeContextResponse#context_arn #context_arn} => String * {Types::DescribeContextResponse#source #source} => Types::ContextSource * {Types::DescribeContextResponse#context_type #context_type} => String * {Types::DescribeContextResponse#description #description} => String * {Types::DescribeContextResponse#properties #properties} => Hash<String,String> * {Types::DescribeContextResponse#creation_time #creation_time} => Time * {Types::DescribeContextResponse#created_by #created_by} => Types::UserContext * {Types::DescribeContextResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeContextResponse#last_modified_by #last_modified_by} => Types::UserContext
@example Request syntax with placeholder values
resp = client.describe_context({ context_name: "ExperimentEntityName", # required })
@example Response structure
resp.context_name #=> String resp.context_arn #=> String resp.source.source_uri #=> String resp.source.source_type #=> String resp.source.source_id #=> String resp.context_type #=> String resp.description #=> String resp.properties #=> Hash resp.properties["StringParameterValue"] #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeContext AWS API Documentation
@overload describe_context
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8004 def describe_context(params = {}, options = {}) req = build_request(:describe_context, params) req.send_request(options) end
Gets the details of a data quality monitoring job definition.
@option params [required, String] :job_definition_name
The name of the data quality monitoring job definition to describe.
@return [Types::DescribeDataQualityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeDataQualityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String * {Types::DescribeDataQualityJobDefinitionResponse#job_definition_name #job_definition_name} => String * {Types::DescribeDataQualityJobDefinitionResponse#creation_time #creation_time} => Time * {Types::DescribeDataQualityJobDefinitionResponse#data_quality_baseline_config #data_quality_baseline_config} => Types::DataQualityBaselineConfig * {Types::DescribeDataQualityJobDefinitionResponse#data_quality_app_specification #data_quality_app_specification} => Types::DataQualityAppSpecification * {Types::DescribeDataQualityJobDefinitionResponse#data_quality_job_input #data_quality_job_input} => Types::DataQualityJobInput * {Types::DescribeDataQualityJobDefinitionResponse#data_quality_job_output_config #data_quality_job_output_config} => Types::MonitoringOutputConfig * {Types::DescribeDataQualityJobDefinitionResponse#job_resources #job_resources} => Types::MonitoringResources * {Types::DescribeDataQualityJobDefinitionResponse#network_config #network_config} => Types::MonitoringNetworkConfig * {Types::DescribeDataQualityJobDefinitionResponse#role_arn #role_arn} => String * {Types::DescribeDataQualityJobDefinitionResponse#stopping_condition #stopping_condition} => Types::MonitoringStoppingCondition
@example Request syntax with placeholder values
resp = client.describe_data_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@example Response structure
resp.job_definition_arn #=> String resp.job_definition_name #=> String resp.creation_time #=> Time resp.data_quality_baseline_config.baselining_job_name #=> String resp.data_quality_baseline_config.constraints_resource.s3_uri #=> String resp.data_quality_baseline_config.statistics_resource.s3_uri #=> String resp.data_quality_app_specification.image_uri #=> String resp.data_quality_app_specification.container_entrypoint #=> Array resp.data_quality_app_specification.container_entrypoint[0] #=> String resp.data_quality_app_specification.container_arguments #=> Array resp.data_quality_app_specification.container_arguments[0] #=> String resp.data_quality_app_specification.record_preprocessor_source_uri #=> String resp.data_quality_app_specification.post_analytics_processor_source_uri #=> String resp.data_quality_app_specification.environment #=> Hash resp.data_quality_app_specification.environment["ProcessingEnvironmentKey"] #=> String resp.data_quality_job_input.endpoint_input.endpoint_name #=> String resp.data_quality_job_input.endpoint_input.local_path #=> String resp.data_quality_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.data_quality_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.data_quality_job_input.endpoint_input.features_attribute #=> String resp.data_quality_job_input.endpoint_input.inference_attribute #=> String resp.data_quality_job_input.endpoint_input.probability_attribute #=> String resp.data_quality_job_input.endpoint_input.probability_threshold_attribute #=> Float resp.data_quality_job_input.endpoint_input.start_time_offset #=> String resp.data_quality_job_input.endpoint_input.end_time_offset #=> String resp.data_quality_job_output_config.monitoring_outputs #=> Array resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.data_quality_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.data_quality_job_output_config.kms_key_id #=> String resp.job_resources.cluster_config.instance_count #=> Integer resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.job_resources.cluster_config.volume_size_in_gb #=> Integer resp.job_resources.cluster_config.volume_kms_key_id #=> String resp.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.network_config.enable_network_isolation #=> Boolean resp.network_config.vpc_config.security_group_ids #=> Array resp.network_config.vpc_config.security_group_ids[0] #=> String resp.network_config.vpc_config.subnets #=> Array resp.network_config.vpc_config.subnets[0] #=> String resp.role_arn #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeDataQualityJobDefinition AWS API Documentation
@overload describe_data_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8083 def describe_data_quality_job_definition(params = {}, options = {}) req = build_request(:describe_data_quality_job_definition, params) req.send_request(options) end
Describes the device.
@option params [String] :next_token
Next token of device description.
@option params [required, String] :device_name
The unique ID of the device.
@option params [required, String] :device_fleet_name
The name of the fleet the devices belong to.
@return [Types::DescribeDeviceResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeDeviceResponse#device_arn #device_arn} => String * {Types::DescribeDeviceResponse#device_name #device_name} => String * {Types::DescribeDeviceResponse#description #description} => String * {Types::DescribeDeviceResponse#device_fleet_name #device_fleet_name} => String * {Types::DescribeDeviceResponse#iot_thing_name #iot_thing_name} => String * {Types::DescribeDeviceResponse#registration_time #registration_time} => Time * {Types::DescribeDeviceResponse#latest_heartbeat #latest_heartbeat} => Time * {Types::DescribeDeviceResponse#models #models} => Array<Types::EdgeModel> * {Types::DescribeDeviceResponse#max_models #max_models} => Integer * {Types::DescribeDeviceResponse#next_token #next_token} => String
@example Request syntax with placeholder values
resp = client.describe_device({ next_token: "NextToken", device_name: "EntityName", # required device_fleet_name: "EntityName", # required })
@example Response structure
resp.device_arn #=> String resp.device_name #=> String resp.description #=> String resp.device_fleet_name #=> String resp.iot_thing_name #=> String resp.registration_time #=> Time resp.latest_heartbeat #=> Time resp.models #=> Array resp.models[0].model_name #=> String resp.models[0].model_version #=> String resp.models[0].latest_sample_time #=> Time resp.models[0].latest_inference #=> Time resp.max_models #=> Integer resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeDevice AWS API Documentation
@overload describe_device
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8141 def describe_device(params = {}, options = {}) req = build_request(:describe_device, params) req.send_request(options) end
A description of the fleet the device belongs to.
@option params [required, String] :device_fleet_name
The name of the fleet.
@return [Types::DescribeDeviceFleetResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeDeviceFleetResponse#device_fleet_name #device_fleet_name} => String * {Types::DescribeDeviceFleetResponse#device_fleet_arn #device_fleet_arn} => String * {Types::DescribeDeviceFleetResponse#output_config #output_config} => Types::EdgeOutputConfig * {Types::DescribeDeviceFleetResponse#description #description} => String * {Types::DescribeDeviceFleetResponse#creation_time #creation_time} => Time * {Types::DescribeDeviceFleetResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeDeviceFleetResponse#role_arn #role_arn} => String * {Types::DescribeDeviceFleetResponse#iot_role_alias #iot_role_alias} => String
@example Request syntax with placeholder values
resp = client.describe_device_fleet({ device_fleet_name: "EntityName", # required })
@example Response structure
resp.device_fleet_name #=> String resp.device_fleet_arn #=> String resp.output_config.s3_output_location #=> String resp.output_config.kms_key_id #=> String resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component" resp.output_config.preset_deployment_config #=> String resp.description #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.role_arn #=> String resp.iot_role_alias #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeDeviceFleet AWS API Documentation
@overload describe_device_fleet
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8186 def describe_device_fleet(params = {}, options = {}) req = build_request(:describe_device_fleet, params) req.send_request(options) end
The description of the domain.
@option params [required, String] :domain_id
The domain ID.
@return [Types::DescribeDomainResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeDomainResponse#domain_arn #domain_arn} => String * {Types::DescribeDomainResponse#domain_id #domain_id} => String * {Types::DescribeDomainResponse#domain_name #domain_name} => String * {Types::DescribeDomainResponse#home_efs_file_system_id #home_efs_file_system_id} => String * {Types::DescribeDomainResponse#single_sign_on_managed_application_instance_id #single_sign_on_managed_application_instance_id} => String * {Types::DescribeDomainResponse#status #status} => String * {Types::DescribeDomainResponse#creation_time #creation_time} => Time * {Types::DescribeDomainResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeDomainResponse#failure_reason #failure_reason} => String * {Types::DescribeDomainResponse#auth_mode #auth_mode} => String * {Types::DescribeDomainResponse#default_user_settings #default_user_settings} => Types::UserSettings * {Types::DescribeDomainResponse#app_network_access_type #app_network_access_type} => String * {Types::DescribeDomainResponse#home_efs_file_system_kms_key_id #home_efs_file_system_kms_key_id} => String * {Types::DescribeDomainResponse#subnet_ids #subnet_ids} => Array<String> * {Types::DescribeDomainResponse#url #url} => String * {Types::DescribeDomainResponse#vpc_id #vpc_id} => String * {Types::DescribeDomainResponse#kms_key_id #kms_key_id} => String
@example Request syntax with placeholder values
resp = client.describe_domain({ domain_id: "DomainId", # required })
@example Response structure
resp.domain_arn #=> String resp.domain_id #=> String resp.domain_name #=> String resp.home_efs_file_system_id #=> String resp.single_sign_on_managed_application_instance_id #=> String resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed" resp.creation_time #=> Time resp.last_modified_time #=> Time resp.failure_reason #=> String resp.auth_mode #=> String, one of "SSO", "IAM" resp.default_user_settings.execution_role #=> String resp.default_user_settings.security_groups #=> Array resp.default_user_settings.security_groups[0] #=> String resp.default_user_settings.sharing_settings.notebook_output_option #=> String, one of "Allowed", "Disabled" resp.default_user_settings.sharing_settings.s3_output_path #=> String resp.default_user_settings.sharing_settings.s3_kms_key_id #=> String resp.default_user_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.default_user_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.default_user_settings.jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.default_user_settings.jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String resp.default_user_settings.jupyter_server_app_settings.lifecycle_config_arns #=> Array resp.default_user_settings.jupyter_server_app_settings.lifecycle_config_arns[0] #=> String resp.default_user_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.default_user_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.default_user_settings.kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.default_user_settings.kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String resp.default_user_settings.kernel_gateway_app_settings.custom_images #=> Array resp.default_user_settings.kernel_gateway_app_settings.custom_images[0].image_name #=> String resp.default_user_settings.kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer resp.default_user_settings.kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String resp.default_user_settings.kernel_gateway_app_settings.lifecycle_config_arns #=> Array resp.default_user_settings.kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String resp.default_user_settings.tensor_board_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.default_user_settings.tensor_board_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.default_user_settings.tensor_board_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.default_user_settings.tensor_board_app_settings.default_resource_spec.lifecycle_config_arn #=> String resp.app_network_access_type #=> String, one of "PublicInternetOnly", "VpcOnly" resp.home_efs_file_system_kms_key_id #=> String resp.subnet_ids #=> Array resp.subnet_ids[0] #=> String resp.url #=> String resp.vpc_id #=> String resp.kms_key_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeDomain AWS API Documentation
@overload describe_domain
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8272 def describe_domain(params = {}, options = {}) req = build_request(:describe_domain, params) req.send_request(options) end
A description of edge packaging jobs.
@option params [required, String] :edge_packaging_job_name
The name of the edge packaging job.
@return [Types::DescribeEdgePackagingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeEdgePackagingJobResponse#edge_packaging_job_arn #edge_packaging_job_arn} => String * {Types::DescribeEdgePackagingJobResponse#edge_packaging_job_name #edge_packaging_job_name} => String * {Types::DescribeEdgePackagingJobResponse#compilation_job_name #compilation_job_name} => String * {Types::DescribeEdgePackagingJobResponse#model_name #model_name} => String * {Types::DescribeEdgePackagingJobResponse#model_version #model_version} => String * {Types::DescribeEdgePackagingJobResponse#role_arn #role_arn} => String * {Types::DescribeEdgePackagingJobResponse#output_config #output_config} => Types::EdgeOutputConfig * {Types::DescribeEdgePackagingJobResponse#resource_key #resource_key} => String * {Types::DescribeEdgePackagingJobResponse#edge_packaging_job_status #edge_packaging_job_status} => String * {Types::DescribeEdgePackagingJobResponse#edge_packaging_job_status_message #edge_packaging_job_status_message} => String * {Types::DescribeEdgePackagingJobResponse#creation_time #creation_time} => Time * {Types::DescribeEdgePackagingJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeEdgePackagingJobResponse#model_artifact #model_artifact} => String * {Types::DescribeEdgePackagingJobResponse#model_signature #model_signature} => String * {Types::DescribeEdgePackagingJobResponse#preset_deployment_output #preset_deployment_output} => Types::EdgePresetDeploymentOutput
@example Request syntax with placeholder values
resp = client.describe_edge_packaging_job({ edge_packaging_job_name: "EntityName", # required })
@example Response structure
resp.edge_packaging_job_arn #=> String resp.edge_packaging_job_name #=> String resp.compilation_job_name #=> String resp.model_name #=> String resp.model_version #=> String resp.role_arn #=> String resp.output_config.s3_output_location #=> String resp.output_config.kms_key_id #=> String resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component" resp.output_config.preset_deployment_config #=> String resp.resource_key #=> String resp.edge_packaging_job_status #=> String, one of "STARTING", "INPROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED" resp.edge_packaging_job_status_message #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.model_artifact #=> String resp.model_signature #=> String resp.preset_deployment_output.type #=> String, one of "GreengrassV2Component" resp.preset_deployment_output.artifact #=> String resp.preset_deployment_output.status #=> String, one of "COMPLETED", "FAILED" resp.preset_deployment_output.status_message #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeEdgePackagingJob AWS API Documentation
@overload describe_edge_packaging_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8334 def describe_edge_packaging_job(params = {}, options = {}) req = build_request(:describe_edge_packaging_job, params) req.send_request(options) end
Returns the description of an endpoint.
@option params [required, String] :endpoint_name
The name of the endpoint.
@return [Types::DescribeEndpointOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeEndpointOutput#endpoint_name #endpoint_name} => String * {Types::DescribeEndpointOutput#endpoint_arn #endpoint_arn} => String * {Types::DescribeEndpointOutput#endpoint_config_name #endpoint_config_name} => String * {Types::DescribeEndpointOutput#production_variants #production_variants} => Array<Types::ProductionVariantSummary> * {Types::DescribeEndpointOutput#data_capture_config #data_capture_config} => Types::DataCaptureConfigSummary * {Types::DescribeEndpointOutput#endpoint_status #endpoint_status} => String * {Types::DescribeEndpointOutput#failure_reason #failure_reason} => String * {Types::DescribeEndpointOutput#creation_time #creation_time} => Time * {Types::DescribeEndpointOutput#last_modified_time #last_modified_time} => Time * {Types::DescribeEndpointOutput#last_deployment_config #last_deployment_config} => Types::DeploymentConfig * {Types::DescribeEndpointOutput#async_inference_config #async_inference_config} => Types::AsyncInferenceConfig
@example Request syntax with placeholder values
resp = client.describe_endpoint({ endpoint_name: "EndpointName", # required })
@example Response structure
resp.endpoint_name #=> String resp.endpoint_arn #=> String resp.endpoint_config_name #=> String resp.production_variants #=> Array resp.production_variants[0].variant_name #=> String resp.production_variants[0].deployed_images #=> Array resp.production_variants[0].deployed_images[0].specified_image #=> String resp.production_variants[0].deployed_images[0].resolved_image #=> String resp.production_variants[0].deployed_images[0].resolution_time #=> Time resp.production_variants[0].current_weight #=> Float resp.production_variants[0].desired_weight #=> Float resp.production_variants[0].current_instance_count #=> Integer resp.production_variants[0].desired_instance_count #=> Integer resp.data_capture_config.enable_capture #=> Boolean resp.data_capture_config.capture_status #=> String, one of "Started", "Stopped" resp.data_capture_config.current_sampling_percentage #=> Integer resp.data_capture_config.destination_s3_uri #=> String resp.data_capture_config.kms_key_id #=> String resp.endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed" resp.failure_reason #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.type #=> String, one of "ALL_AT_ONCE", "CANARY" resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.wait_interval_in_seconds #=> Integer resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.canary_size.type #=> String, one of "INSTANCE_COUNT", "CAPACITY_PERCENT" resp.last_deployment_config.blue_green_update_policy.traffic_routing_configuration.canary_size.value #=> Integer resp.last_deployment_config.blue_green_update_policy.termination_wait_in_seconds #=> Integer resp.last_deployment_config.blue_green_update_policy.maximum_execution_timeout_in_seconds #=> Integer resp.last_deployment_config.auto_rollback_configuration.alarms #=> Array resp.last_deployment_config.auto_rollback_configuration.alarms[0].alarm_name #=> String resp.async_inference_config.client_config.max_concurrent_invocations_per_instance #=> Integer resp.async_inference_config.output_config.kms_key_id #=> String resp.async_inference_config.output_config.s3_output_path #=> String resp.async_inference_config.output_config.notification_config.success_topic #=> String resp.async_inference_config.output_config.notification_config.error_topic #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* endpoint_deleted * endpoint_in_service
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeEndpoint AWS API Documentation
@overload describe_endpoint
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8412 def describe_endpoint(params = {}, options = {}) req = build_request(:describe_endpoint, params) req.send_request(options) end
Returns the description of an endpoint configuration created using the `CreateEndpointConfig` API.
@option params [required, String] :endpoint_config_name
The name of the endpoint configuration.
@return [Types::DescribeEndpointConfigOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeEndpointConfigOutput#endpoint_config_name #endpoint_config_name} => String * {Types::DescribeEndpointConfigOutput#endpoint_config_arn #endpoint_config_arn} => String * {Types::DescribeEndpointConfigOutput#production_variants #production_variants} => Array<Types::ProductionVariant> * {Types::DescribeEndpointConfigOutput#data_capture_config #data_capture_config} => Types::DataCaptureConfig * {Types::DescribeEndpointConfigOutput#kms_key_id #kms_key_id} => String * {Types::DescribeEndpointConfigOutput#creation_time #creation_time} => Time * {Types::DescribeEndpointConfigOutput#async_inference_config #async_inference_config} => Types::AsyncInferenceConfig
@example Request syntax with placeholder values
resp = client.describe_endpoint_config({ endpoint_config_name: "EndpointConfigName", # required })
@example Response structure
resp.endpoint_config_name #=> String resp.endpoint_config_arn #=> String resp.production_variants #=> Array resp.production_variants[0].variant_name #=> String resp.production_variants[0].model_name #=> String resp.production_variants[0].initial_instance_count #=> Integer resp.production_variants[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge" resp.production_variants[0].initial_variant_weight #=> Float resp.production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge" resp.production_variants[0].core_dump_config.destination_s3_uri #=> String resp.production_variants[0].core_dump_config.kms_key_id #=> String resp.data_capture_config.enable_capture #=> Boolean resp.data_capture_config.initial_sampling_percentage #=> Integer resp.data_capture_config.destination_s3_uri #=> String resp.data_capture_config.kms_key_id #=> String resp.data_capture_config.capture_options #=> Array resp.data_capture_config.capture_options[0].capture_mode #=> String, one of "Input", "Output" resp.data_capture_config.capture_content_type_header.csv_content_types #=> Array resp.data_capture_config.capture_content_type_header.csv_content_types[0] #=> String resp.data_capture_config.capture_content_type_header.json_content_types #=> Array resp.data_capture_config.capture_content_type_header.json_content_types[0] #=> String resp.kms_key_id #=> String resp.creation_time #=> Time resp.async_inference_config.client_config.max_concurrent_invocations_per_instance #=> Integer resp.async_inference_config.output_config.kms_key_id #=> String resp.async_inference_config.output_config.s3_output_path #=> String resp.async_inference_config.output_config.notification_config.success_topic #=> String resp.async_inference_config.output_config.notification_config.error_topic #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeEndpointConfig AWS API Documentation
@overload describe_endpoint_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8474 def describe_endpoint_config(params = {}, options = {}) req = build_request(:describe_endpoint_config, params) req.send_request(options) end
Provides a list of an experiment's properties.
@option params [required, String] :experiment_name
The name of the experiment to describe.
@return [Types::DescribeExperimentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeExperimentResponse#experiment_name #experiment_name} => String * {Types::DescribeExperimentResponse#experiment_arn #experiment_arn} => String * {Types::DescribeExperimentResponse#display_name #display_name} => String * {Types::DescribeExperimentResponse#source #source} => Types::ExperimentSource * {Types::DescribeExperimentResponse#description #description} => String * {Types::DescribeExperimentResponse#creation_time #creation_time} => Time * {Types::DescribeExperimentResponse#created_by #created_by} => Types::UserContext * {Types::DescribeExperimentResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeExperimentResponse#last_modified_by #last_modified_by} => Types::UserContext
@example Request syntax with placeholder values
resp = client.describe_experiment({ experiment_name: "ExperimentEntityName", # required })
@example Response structure
resp.experiment_name #=> String resp.experiment_arn #=> String resp.display_name #=> String resp.source.source_arn #=> String resp.source.source_type #=> String resp.description #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeExperiment AWS API Documentation
@overload describe_experiment
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8523 def describe_experiment(params = {}, options = {}) req = build_request(:describe_experiment, params) req.send_request(options) end
Use this operation to describe a `FeatureGroup`. The response includes information on the creation time, `FeatureGroup` name, the unique identifier for each `FeatureGroup`, and more.
@option params [required, String] :feature_group_name
The name of the `FeatureGroup` you want described.
@option params [String] :next_token
A token to resume pagination of the list of `Features` (`FeatureDefinitions`). 2,500 `Features` are returned by default.
@return [Types::DescribeFeatureGroupResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeFeatureGroupResponse#feature_group_arn #feature_group_arn} => String * {Types::DescribeFeatureGroupResponse#feature_group_name #feature_group_name} => String * {Types::DescribeFeatureGroupResponse#record_identifier_feature_name #record_identifier_feature_name} => String * {Types::DescribeFeatureGroupResponse#event_time_feature_name #event_time_feature_name} => String * {Types::DescribeFeatureGroupResponse#feature_definitions #feature_definitions} => Array<Types::FeatureDefinition> * {Types::DescribeFeatureGroupResponse#creation_time #creation_time} => Time * {Types::DescribeFeatureGroupResponse#online_store_config #online_store_config} => Types::OnlineStoreConfig * {Types::DescribeFeatureGroupResponse#offline_store_config #offline_store_config} => Types::OfflineStoreConfig * {Types::DescribeFeatureGroupResponse#role_arn #role_arn} => String * {Types::DescribeFeatureGroupResponse#feature_group_status #feature_group_status} => String * {Types::DescribeFeatureGroupResponse#offline_store_status #offline_store_status} => Types::OfflineStoreStatus * {Types::DescribeFeatureGroupResponse#failure_reason #failure_reason} => String * {Types::DescribeFeatureGroupResponse#description #description} => String * {Types::DescribeFeatureGroupResponse#next_token #next_token} => String
@example Request syntax with placeholder values
resp = client.describe_feature_group({ feature_group_name: "FeatureGroupName", # required next_token: "NextToken", })
@example Response structure
resp.feature_group_arn #=> String resp.feature_group_name #=> String resp.record_identifier_feature_name #=> String resp.event_time_feature_name #=> String resp.feature_definitions #=> Array resp.feature_definitions[0].feature_name #=> String resp.feature_definitions[0].feature_type #=> String, one of "Integral", "Fractional", "String" resp.creation_time #=> Time resp.online_store_config.security_config.kms_key_id #=> String resp.online_store_config.enable_online_store #=> Boolean resp.offline_store_config.s3_storage_config.s3_uri #=> String resp.offline_store_config.s3_storage_config.kms_key_id #=> String resp.offline_store_config.s3_storage_config.resolved_output_s3_uri #=> String resp.offline_store_config.disable_glue_table_creation #=> Boolean resp.offline_store_config.data_catalog_config.table_name #=> String resp.offline_store_config.data_catalog_config.catalog #=> String resp.offline_store_config.data_catalog_config.database #=> String resp.role_arn #=> String resp.feature_group_status #=> String, one of "Creating", "Created", "CreateFailed", "Deleting", "DeleteFailed" resp.offline_store_status.status #=> String, one of "Active", "Blocked", "Disabled" resp.offline_store_status.blocked_reason #=> String resp.failure_reason #=> String resp.description #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeFeatureGroup AWS API Documentation
@overload describe_feature_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8594 def describe_feature_group(params = {}, options = {}) req = build_request(:describe_feature_group, params) req.send_request(options) end
Returns information about the specified flow definition.
@option params [required, String] :flow_definition_name
The name of the flow definition.
@return [Types::DescribeFlowDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeFlowDefinitionResponse#flow_definition_arn #flow_definition_arn} => String * {Types::DescribeFlowDefinitionResponse#flow_definition_name #flow_definition_name} => String * {Types::DescribeFlowDefinitionResponse#flow_definition_status #flow_definition_status} => String * {Types::DescribeFlowDefinitionResponse#creation_time #creation_time} => Time * {Types::DescribeFlowDefinitionResponse#human_loop_request_source #human_loop_request_source} => Types::HumanLoopRequestSource * {Types::DescribeFlowDefinitionResponse#human_loop_activation_config #human_loop_activation_config} => Types::HumanLoopActivationConfig * {Types::DescribeFlowDefinitionResponse#human_loop_config #human_loop_config} => Types::HumanLoopConfig * {Types::DescribeFlowDefinitionResponse#output_config #output_config} => Types::FlowDefinitionOutputConfig * {Types::DescribeFlowDefinitionResponse#role_arn #role_arn} => String * {Types::DescribeFlowDefinitionResponse#failure_reason #failure_reason} => String
@example Request syntax with placeholder values
resp = client.describe_flow_definition({ flow_definition_name: "FlowDefinitionName", # required })
@example Response structure
resp.flow_definition_arn #=> String resp.flow_definition_name #=> String resp.flow_definition_status #=> String, one of "Initializing", "Active", "Failed", "Deleting" resp.creation_time #=> Time resp.human_loop_request_source.aws_managed_human_loop_request_source #=> String, one of "AWS/Rekognition/DetectModerationLabels/Image/V3", "AWS/Textract/AnalyzeDocument/Forms/V1" resp.human_loop_activation_config.human_loop_activation_conditions_config.human_loop_activation_conditions #=> String resp.human_loop_config.workteam_arn #=> String resp.human_loop_config.human_task_ui_arn #=> String resp.human_loop_config.task_title #=> String resp.human_loop_config.task_description #=> String resp.human_loop_config.task_count #=> Integer resp.human_loop_config.task_availability_lifetime_in_seconds #=> Integer resp.human_loop_config.task_time_limit_in_seconds #=> Integer resp.human_loop_config.task_keywords #=> Array resp.human_loop_config.task_keywords[0] #=> String resp.human_loop_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer resp.human_loop_config.public_workforce_task_price.amount_in_usd.cents #=> Integer resp.human_loop_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer resp.output_config.s3_output_path #=> String resp.output_config.kms_key_id #=> String resp.role_arn #=> String resp.failure_reason #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeFlowDefinition AWS API Documentation
@overload describe_flow_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8652 def describe_flow_definition(params = {}, options = {}) req = build_request(:describe_flow_definition, params) req.send_request(options) end
Returns information about the requested human task user interface (worker task template).
@option params [required, String] :human_task_ui_name
The name of the human task user interface (worker task template) you want information about.
@return [Types::DescribeHumanTaskUiResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeHumanTaskUiResponse#human_task_ui_arn #human_task_ui_arn} => String * {Types::DescribeHumanTaskUiResponse#human_task_ui_name #human_task_ui_name} => String * {Types::DescribeHumanTaskUiResponse#human_task_ui_status #human_task_ui_status} => String * {Types::DescribeHumanTaskUiResponse#creation_time #creation_time} => Time * {Types::DescribeHumanTaskUiResponse#ui_template #ui_template} => Types::UiTemplateInfo
@example Request syntax with placeholder values
resp = client.describe_human_task_ui({ human_task_ui_name: "HumanTaskUiName", # required })
@example Response structure
resp.human_task_ui_arn #=> String resp.human_task_ui_name #=> String resp.human_task_ui_status #=> String, one of "Active", "Deleting" resp.creation_time #=> Time resp.ui_template.url #=> String resp.ui_template.content_sha_256 #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeHumanTaskUi AWS API Documentation
@overload describe_human_task_ui
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8691 def describe_human_task_ui(params = {}, options = {}) req = build_request(:describe_human_task_ui, params) req.send_request(options) end
Gets a description of a hyperparameter tuning job.
@option params [required, String] :hyper_parameter_tuning_job_name
The name of the tuning job.
@return [Types::DescribeHyperParameterTuningJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeHyperParameterTuningJobResponse#hyper_parameter_tuning_job_name #hyper_parameter_tuning_job_name} => String * {Types::DescribeHyperParameterTuningJobResponse#hyper_parameter_tuning_job_arn #hyper_parameter_tuning_job_arn} => String * {Types::DescribeHyperParameterTuningJobResponse#hyper_parameter_tuning_job_config #hyper_parameter_tuning_job_config} => Types::HyperParameterTuningJobConfig * {Types::DescribeHyperParameterTuningJobResponse#training_job_definition #training_job_definition} => Types::HyperParameterTrainingJobDefinition * {Types::DescribeHyperParameterTuningJobResponse#training_job_definitions #training_job_definitions} => Array<Types::HyperParameterTrainingJobDefinition> * {Types::DescribeHyperParameterTuningJobResponse#hyper_parameter_tuning_job_status #hyper_parameter_tuning_job_status} => String * {Types::DescribeHyperParameterTuningJobResponse#creation_time #creation_time} => Time * {Types::DescribeHyperParameterTuningJobResponse#hyper_parameter_tuning_end_time #hyper_parameter_tuning_end_time} => Time * {Types::DescribeHyperParameterTuningJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeHyperParameterTuningJobResponse#training_job_status_counters #training_job_status_counters} => Types::TrainingJobStatusCounters * {Types::DescribeHyperParameterTuningJobResponse#objective_status_counters #objective_status_counters} => Types::ObjectiveStatusCounters * {Types::DescribeHyperParameterTuningJobResponse#best_training_job #best_training_job} => Types::HyperParameterTrainingJobSummary * {Types::DescribeHyperParameterTuningJobResponse#overall_best_training_job #overall_best_training_job} => Types::HyperParameterTrainingJobSummary * {Types::DescribeHyperParameterTuningJobResponse#warm_start_config #warm_start_config} => Types::HyperParameterTuningJobWarmStartConfig * {Types::DescribeHyperParameterTuningJobResponse#failure_reason #failure_reason} => String
@example Request syntax with placeholder values
resp = client.describe_hyper_parameter_tuning_job({ hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required })
@example Response structure
resp.hyper_parameter_tuning_job_name #=> String resp.hyper_parameter_tuning_job_arn #=> String resp.hyper_parameter_tuning_job_config.strategy #=> String, one of "Bayesian", "Random" resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.type #=> String, one of "Maximize", "Minimize" resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.metric_name #=> String resp.hyper_parameter_tuning_job_config.resource_limits.max_number_of_training_jobs #=> Integer resp.hyper_parameter_tuning_job_config.resource_limits.max_parallel_training_jobs #=> Integer resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges #=> Array resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].name #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].min_value #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].max_value #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges #=> Array resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].name #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].min_value #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].max_value #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges #=> Array resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].name #=> String resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values #=> Array resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String resp.hyper_parameter_tuning_job_config.training_job_early_stopping_type #=> String, one of "Off", "Auto" resp.hyper_parameter_tuning_job_config.tuning_job_completion_criteria.target_objective_metric_value #=> Float resp.training_job_definition.definition_name #=> String resp.training_job_definition.tuning_objective.type #=> String, one of "Maximize", "Minimize" resp.training_job_definition.tuning_objective.metric_name #=> String resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges #=> Array resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges #=> Array resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges #=> Array resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String resp.training_job_definition.static_hyper_parameters #=> Hash resp.training_job_definition.static_hyper_parameters["HyperParameterKey"] #=> String resp.training_job_definition.algorithm_specification.training_image #=> String resp.training_job_definition.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File" resp.training_job_definition.algorithm_specification.algorithm_name #=> String resp.training_job_definition.algorithm_specification.metric_definitions #=> Array resp.training_job_definition.algorithm_specification.metric_definitions[0].name #=> String resp.training_job_definition.algorithm_specification.metric_definitions[0].regex #=> String resp.training_job_definition.role_arn #=> String resp.training_job_definition.input_data_config #=> Array resp.training_job_definition.input_data_config[0].channel_name #=> String resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.training_job_definition.input_data_config[0].content_type #=> String resp.training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer resp.training_job_definition.vpc_config.security_group_ids #=> Array resp.training_job_definition.vpc_config.security_group_ids[0] #=> String resp.training_job_definition.vpc_config.subnets #=> Array resp.training_job_definition.vpc_config.subnets[0] #=> String resp.training_job_definition.output_data_config.kms_key_id #=> String resp.training_job_definition.output_data_config.s3_output_path #=> String resp.training_job_definition.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.training_job_definition.resource_config.instance_count #=> Integer resp.training_job_definition.resource_config.volume_size_in_gb #=> Integer resp.training_job_definition.resource_config.volume_kms_key_id #=> String resp.training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer resp.training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer resp.training_job_definition.enable_network_isolation #=> Boolean resp.training_job_definition.enable_inter_container_traffic_encryption #=> Boolean resp.training_job_definition.enable_managed_spot_training #=> Boolean resp.training_job_definition.checkpoint_config.s3_uri #=> String resp.training_job_definition.checkpoint_config.local_path #=> String resp.training_job_definition.retry_strategy.maximum_retry_attempts #=> Integer resp.training_job_definitions #=> Array resp.training_job_definitions[0].definition_name #=> String resp.training_job_definitions[0].tuning_objective.type #=> String, one of "Maximize", "Minimize" resp.training_job_definitions[0].tuning_objective.metric_name #=> String resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges #=> Array resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges #=> Array resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic" resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges #=> Array resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String resp.training_job_definitions[0].static_hyper_parameters #=> Hash resp.training_job_definitions[0].static_hyper_parameters["HyperParameterKey"] #=> String resp.training_job_definitions[0].algorithm_specification.training_image #=> String resp.training_job_definitions[0].algorithm_specification.training_input_mode #=> String, one of "Pipe", "File" resp.training_job_definitions[0].algorithm_specification.algorithm_name #=> String resp.training_job_definitions[0].algorithm_specification.metric_definitions #=> Array resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].name #=> String resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].regex #=> String resp.training_job_definitions[0].role_arn #=> String resp.training_job_definitions[0].input_data_config #=> Array resp.training_job_definitions[0].input_data_config[0].channel_name #=> String resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.training_job_definitions[0].input_data_config[0].content_type #=> String resp.training_job_definitions[0].input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.training_job_definitions[0].input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.training_job_definitions[0].input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.training_job_definitions[0].input_data_config[0].shuffle_config.seed #=> Integer resp.training_job_definitions[0].vpc_config.security_group_ids #=> Array resp.training_job_definitions[0].vpc_config.security_group_ids[0] #=> String resp.training_job_definitions[0].vpc_config.subnets #=> Array resp.training_job_definitions[0].vpc_config.subnets[0] #=> String resp.training_job_definitions[0].output_data_config.kms_key_id #=> String resp.training_job_definitions[0].output_data_config.s3_output_path #=> String resp.training_job_definitions[0].resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.training_job_definitions[0].resource_config.instance_count #=> Integer resp.training_job_definitions[0].resource_config.volume_size_in_gb #=> Integer resp.training_job_definitions[0].resource_config.volume_kms_key_id #=> String resp.training_job_definitions[0].stopping_condition.max_runtime_in_seconds #=> Integer resp.training_job_definitions[0].stopping_condition.max_wait_time_in_seconds #=> Integer resp.training_job_definitions[0].enable_network_isolation #=> Boolean resp.training_job_definitions[0].enable_inter_container_traffic_encryption #=> Boolean resp.training_job_definitions[0].enable_managed_spot_training #=> Boolean resp.training_job_definitions[0].checkpoint_config.s3_uri #=> String resp.training_job_definitions[0].checkpoint_config.local_path #=> String resp.training_job_definitions[0].retry_strategy.maximum_retry_attempts #=> Integer resp.hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.creation_time #=> Time resp.hyper_parameter_tuning_end_time #=> Time resp.last_modified_time #=> Time resp.training_job_status_counters.completed #=> Integer resp.training_job_status_counters.in_progress #=> Integer resp.training_job_status_counters.retryable_error #=> Integer resp.training_job_status_counters.non_retryable_error #=> Integer resp.training_job_status_counters.stopped #=> Integer resp.objective_status_counters.succeeded #=> Integer resp.objective_status_counters.pending #=> Integer resp.objective_status_counters.failed #=> Integer resp.best_training_job.training_job_definition_name #=> String resp.best_training_job.training_job_name #=> String resp.best_training_job.training_job_arn #=> String resp.best_training_job.tuning_job_name #=> String resp.best_training_job.creation_time #=> Time resp.best_training_job.training_start_time #=> Time resp.best_training_job.training_end_time #=> Time resp.best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.best_training_job.tuned_hyper_parameters #=> Hash resp.best_training_job.tuned_hyper_parameters["HyperParameterKey"] #=> String resp.best_training_job.failure_reason #=> String resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize" resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float resp.best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed" resp.overall_best_training_job.training_job_definition_name #=> String resp.overall_best_training_job.training_job_name #=> String resp.overall_best_training_job.training_job_arn #=> String resp.overall_best_training_job.tuning_job_name #=> String resp.overall_best_training_job.creation_time #=> Time resp.overall_best_training_job.training_start_time #=> Time resp.overall_best_training_job.training_end_time #=> Time resp.overall_best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.overall_best_training_job.tuned_hyper_parameters #=> Hash resp.overall_best_training_job.tuned_hyper_parameters["HyperParameterKey"] #=> String resp.overall_best_training_job.failure_reason #=> String resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize" resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float resp.overall_best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed" resp.warm_start_config.parent_hyper_parameter_tuning_jobs #=> Array resp.warm_start_config.parent_hyper_parameter_tuning_jobs[0].hyper_parameter_tuning_job_name #=> String resp.warm_start_config.warm_start_type #=> String, one of "IdenticalDataAndAlgorithm", "TransferLearning" resp.failure_reason #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeHyperParameterTuningJob AWS API Documentation
@overload describe_hyper_parameter_tuning_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8922 def describe_hyper_parameter_tuning_job(params = {}, options = {}) req = build_request(:describe_hyper_parameter_tuning_job, params) req.send_request(options) end
Describes a SageMaker
image.
@option params [required, String] :image_name
The name of the image to describe.
@return [Types::DescribeImageResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeImageResponse#creation_time #creation_time} => Time * {Types::DescribeImageResponse#description #description} => String * {Types::DescribeImageResponse#display_name #display_name} => String * {Types::DescribeImageResponse#failure_reason #failure_reason} => String * {Types::DescribeImageResponse#image_arn #image_arn} => String * {Types::DescribeImageResponse#image_name #image_name} => String * {Types::DescribeImageResponse#image_status #image_status} => String * {Types::DescribeImageResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeImageResponse#role_arn #role_arn} => String
@example Request syntax with placeholder values
resp = client.describe_image({ image_name: "ImageName", # required })
@example Response structure
resp.creation_time #=> Time resp.description #=> String resp.display_name #=> String resp.failure_reason #=> String resp.image_arn #=> String resp.image_name #=> String resp.image_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "UPDATING", "UPDATE_FAILED", "DELETING", "DELETE_FAILED" resp.last_modified_time #=> Time resp.role_arn #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* image_created * image_deleted * image_updated
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeImage AWS API Documentation
@overload describe_image
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 8973 def describe_image(params = {}, options = {}) req = build_request(:describe_image, params) req.send_request(options) end
Describes a version of a SageMaker
image.
@option params [required, String] :image_name
The name of the image.
@option params [Integer] :version
The version of the image. If not specified, the latest version is described.
@return [Types::DescribeImageVersionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeImageVersionResponse#base_image #base_image} => String * {Types::DescribeImageVersionResponse#container_image #container_image} => String * {Types::DescribeImageVersionResponse#creation_time #creation_time} => Time * {Types::DescribeImageVersionResponse#failure_reason #failure_reason} => String * {Types::DescribeImageVersionResponse#image_arn #image_arn} => String * {Types::DescribeImageVersionResponse#image_version_arn #image_version_arn} => String * {Types::DescribeImageVersionResponse#image_version_status #image_version_status} => String * {Types::DescribeImageVersionResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeImageVersionResponse#version #version} => Integer
@example Request syntax with placeholder values
resp = client.describe_image_version({ image_name: "ImageName", # required version: 1, })
@example Response structure
resp.base_image #=> String resp.container_image #=> String resp.creation_time #=> Time resp.failure_reason #=> String resp.image_arn #=> String resp.image_version_arn #=> String resp.image_version_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "DELETING", "DELETE_FAILED" resp.last_modified_time #=> Time resp.version #=> Integer
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* image_version_created * image_version_deleted
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeImageVersion AWS API Documentation
@overload describe_image_version
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9028 def describe_image_version(params = {}, options = {}) req = build_request(:describe_image_version, params) req.send_request(options) end
Gets information about a labeling job.
@option params [required, String] :labeling_job_name
The name of the labeling job to return information for.
@return [Types::DescribeLabelingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeLabelingJobResponse#labeling_job_status #labeling_job_status} => String * {Types::DescribeLabelingJobResponse#label_counters #label_counters} => Types::LabelCounters * {Types::DescribeLabelingJobResponse#failure_reason #failure_reason} => String * {Types::DescribeLabelingJobResponse#creation_time #creation_time} => Time * {Types::DescribeLabelingJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeLabelingJobResponse#job_reference_code #job_reference_code} => String * {Types::DescribeLabelingJobResponse#labeling_job_name #labeling_job_name} => String * {Types::DescribeLabelingJobResponse#labeling_job_arn #labeling_job_arn} => String * {Types::DescribeLabelingJobResponse#label_attribute_name #label_attribute_name} => String * {Types::DescribeLabelingJobResponse#input_config #input_config} => Types::LabelingJobInputConfig * {Types::DescribeLabelingJobResponse#output_config #output_config} => Types::LabelingJobOutputConfig * {Types::DescribeLabelingJobResponse#role_arn #role_arn} => String * {Types::DescribeLabelingJobResponse#label_category_config_s3_uri #label_category_config_s3_uri} => String * {Types::DescribeLabelingJobResponse#stopping_conditions #stopping_conditions} => Types::LabelingJobStoppingConditions * {Types::DescribeLabelingJobResponse#labeling_job_algorithms_config #labeling_job_algorithms_config} => Types::LabelingJobAlgorithmsConfig * {Types::DescribeLabelingJobResponse#human_task_config #human_task_config} => Types::HumanTaskConfig * {Types::DescribeLabelingJobResponse#tags #tags} => Array<Types::Tag> * {Types::DescribeLabelingJobResponse#labeling_job_output #labeling_job_output} => Types::LabelingJobOutput
@example Request syntax with placeholder values
resp = client.describe_labeling_job({ labeling_job_name: "LabelingJobName", # required })
@example Response structure
resp.labeling_job_status #=> String, one of "Initializing", "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.label_counters.total_labeled #=> Integer resp.label_counters.human_labeled #=> Integer resp.label_counters.machine_labeled #=> Integer resp.label_counters.failed_non_retryable_error #=> Integer resp.label_counters.unlabeled #=> Integer resp.failure_reason #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.job_reference_code #=> String resp.labeling_job_name #=> String resp.labeling_job_arn #=> String resp.label_attribute_name #=> String resp.input_config.data_source.s3_data_source.manifest_s3_uri #=> String resp.input_config.data_source.sns_data_source.sns_topic_arn #=> String resp.input_config.data_attributes.content_classifiers #=> Array resp.input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent" resp.output_config.s3_output_path #=> String resp.output_config.kms_key_id #=> String resp.output_config.sns_topic_arn #=> String resp.role_arn #=> String resp.label_category_config_s3_uri #=> String resp.stopping_conditions.max_human_labeled_object_count #=> Integer resp.stopping_conditions.max_percentage_of_input_dataset_labeled #=> Integer resp.labeling_job_algorithms_config.labeling_job_algorithm_specification_arn #=> String resp.labeling_job_algorithms_config.initial_active_learning_model_arn #=> String resp.labeling_job_algorithms_config.labeling_job_resource_config.volume_kms_key_id #=> String resp.human_task_config.workteam_arn #=> String resp.human_task_config.ui_config.ui_template_s3_uri #=> String resp.human_task_config.ui_config.human_task_ui_arn #=> String resp.human_task_config.pre_human_task_lambda_arn #=> String resp.human_task_config.task_keywords #=> Array resp.human_task_config.task_keywords[0] #=> String resp.human_task_config.task_title #=> String resp.human_task_config.task_description #=> String resp.human_task_config.number_of_human_workers_per_data_object #=> Integer resp.human_task_config.task_time_limit_in_seconds #=> Integer resp.human_task_config.task_availability_lifetime_in_seconds #=> Integer resp.human_task_config.max_concurrent_task_count #=> Integer resp.human_task_config.annotation_consolidation_config.annotation_consolidation_lambda_arn #=> String resp.human_task_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer resp.human_task_config.public_workforce_task_price.amount_in_usd.cents #=> Integer resp.human_task_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer resp.tags #=> Array resp.tags[0].key #=> String resp.tags[0].value #=> String resp.labeling_job_output.output_dataset_s3_uri #=> String resp.labeling_job_output.final_active_learning_model_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeLabelingJob AWS API Documentation
@overload describe_labeling_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9120 def describe_labeling_job(params = {}, options = {}) req = build_request(:describe_labeling_job, params) req.send_request(options) end
Describes a model that you created using the `CreateModel` API.
@option params [required, String] :model_name
The name of the model.
@return [Types::DescribeModelOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelOutput#model_name #model_name} => String * {Types::DescribeModelOutput#primary_container #primary_container} => Types::ContainerDefinition * {Types::DescribeModelOutput#containers #containers} => Array<Types::ContainerDefinition> * {Types::DescribeModelOutput#inference_execution_config #inference_execution_config} => Types::InferenceExecutionConfig * {Types::DescribeModelOutput#execution_role_arn #execution_role_arn} => String * {Types::DescribeModelOutput#vpc_config #vpc_config} => Types::VpcConfig * {Types::DescribeModelOutput#creation_time #creation_time} => Time * {Types::DescribeModelOutput#model_arn #model_arn} => String * {Types::DescribeModelOutput#enable_network_isolation #enable_network_isolation} => Boolean
@example Request syntax with placeholder values
resp = client.describe_model({ model_name: "ModelName", # required })
@example Response structure
resp.model_name #=> String resp.primary_container.container_hostname #=> String resp.primary_container.image #=> String resp.primary_container.image_config.repository_access_mode #=> String, one of "Platform", "Vpc" resp.primary_container.image_config.repository_auth_config.repository_credentials_provider_arn #=> String resp.primary_container.mode #=> String, one of "SingleModel", "MultiModel" resp.primary_container.model_data_url #=> String resp.primary_container.environment #=> Hash resp.primary_container.environment["EnvironmentKey"] #=> String resp.primary_container.model_package_name #=> String resp.primary_container.multi_model_config.model_cache_setting #=> String, one of "Enabled", "Disabled" resp.containers #=> Array resp.containers[0].container_hostname #=> String resp.containers[0].image #=> String resp.containers[0].image_config.repository_access_mode #=> String, one of "Platform", "Vpc" resp.containers[0].image_config.repository_auth_config.repository_credentials_provider_arn #=> String resp.containers[0].mode #=> String, one of "SingleModel", "MultiModel" resp.containers[0].model_data_url #=> String resp.containers[0].environment #=> Hash resp.containers[0].environment["EnvironmentKey"] #=> String resp.containers[0].model_package_name #=> String resp.containers[0].multi_model_config.model_cache_setting #=> String, one of "Enabled", "Disabled" resp.inference_execution_config.mode #=> String, one of "Serial", "Direct" resp.execution_role_arn #=> String resp.vpc_config.security_group_ids #=> Array resp.vpc_config.security_group_ids[0] #=> String resp.vpc_config.subnets #=> Array resp.vpc_config.subnets[0] #=> String resp.creation_time #=> Time resp.model_arn #=> String resp.enable_network_isolation #=> Boolean
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModel AWS API Documentation
@overload describe_model
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9186 def describe_model(params = {}, options = {}) req = build_request(:describe_model, params) req.send_request(options) end
Returns a description of a model bias job definition.
@option params [required, String] :job_definition_name
The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@return [Types::DescribeModelBiasJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelBiasJobDefinitionResponse#job_definition_arn #job_definition_arn} => String * {Types::DescribeModelBiasJobDefinitionResponse#job_definition_name #job_definition_name} => String * {Types::DescribeModelBiasJobDefinitionResponse#creation_time #creation_time} => Time * {Types::DescribeModelBiasJobDefinitionResponse#model_bias_baseline_config #model_bias_baseline_config} => Types::ModelBiasBaselineConfig * {Types::DescribeModelBiasJobDefinitionResponse#model_bias_app_specification #model_bias_app_specification} => Types::ModelBiasAppSpecification * {Types::DescribeModelBiasJobDefinitionResponse#model_bias_job_input #model_bias_job_input} => Types::ModelBiasJobInput * {Types::DescribeModelBiasJobDefinitionResponse#model_bias_job_output_config #model_bias_job_output_config} => Types::MonitoringOutputConfig * {Types::DescribeModelBiasJobDefinitionResponse#job_resources #job_resources} => Types::MonitoringResources * {Types::DescribeModelBiasJobDefinitionResponse#network_config #network_config} => Types::MonitoringNetworkConfig * {Types::DescribeModelBiasJobDefinitionResponse#role_arn #role_arn} => String * {Types::DescribeModelBiasJobDefinitionResponse#stopping_condition #stopping_condition} => Types::MonitoringStoppingCondition
@example Request syntax with placeholder values
resp = client.describe_model_bias_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@example Response structure
resp.job_definition_arn #=> String resp.job_definition_name #=> String resp.creation_time #=> Time resp.model_bias_baseline_config.baselining_job_name #=> String resp.model_bias_baseline_config.constraints_resource.s3_uri #=> String resp.model_bias_app_specification.image_uri #=> String resp.model_bias_app_specification.config_uri #=> String resp.model_bias_app_specification.environment #=> Hash resp.model_bias_app_specification.environment["ProcessingEnvironmentKey"] #=> String resp.model_bias_job_input.endpoint_input.endpoint_name #=> String resp.model_bias_job_input.endpoint_input.local_path #=> String resp.model_bias_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.model_bias_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.model_bias_job_input.endpoint_input.features_attribute #=> String resp.model_bias_job_input.endpoint_input.inference_attribute #=> String resp.model_bias_job_input.endpoint_input.probability_attribute #=> String resp.model_bias_job_input.endpoint_input.probability_threshold_attribute #=> Float resp.model_bias_job_input.endpoint_input.start_time_offset #=> String resp.model_bias_job_input.endpoint_input.end_time_offset #=> String resp.model_bias_job_input.ground_truth_s3_input.s3_uri #=> String resp.model_bias_job_output_config.monitoring_outputs #=> Array resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.model_bias_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.model_bias_job_output_config.kms_key_id #=> String resp.job_resources.cluster_config.instance_count #=> Integer resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.job_resources.cluster_config.volume_size_in_gb #=> Integer resp.job_resources.cluster_config.volume_kms_key_id #=> String resp.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.network_config.enable_network_isolation #=> Boolean resp.network_config.vpc_config.security_group_ids #=> Array resp.network_config.vpc_config.security_group_ids[0] #=> String resp.network_config.vpc_config.subnets #=> Array resp.network_config.vpc_config.subnets[0] #=> String resp.role_arn #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModelBiasJobDefinition AWS API Documentation
@overload describe_model_bias_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9262 def describe_model_bias_job_definition(params = {}, options = {}) req = build_request(:describe_model_bias_job_definition, params) req.send_request(options) end
Returns a description of a model explainability job definition.
@option params [required, String] :job_definition_name
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@return [Types::DescribeModelExplainabilityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelExplainabilityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String * {Types::DescribeModelExplainabilityJobDefinitionResponse#job_definition_name #job_definition_name} => String * {Types::DescribeModelExplainabilityJobDefinitionResponse#creation_time #creation_time} => Time * {Types::DescribeModelExplainabilityJobDefinitionResponse#model_explainability_baseline_config #model_explainability_baseline_config} => Types::ModelExplainabilityBaselineConfig * {Types::DescribeModelExplainabilityJobDefinitionResponse#model_explainability_app_specification #model_explainability_app_specification} => Types::ModelExplainabilityAppSpecification * {Types::DescribeModelExplainabilityJobDefinitionResponse#model_explainability_job_input #model_explainability_job_input} => Types::ModelExplainabilityJobInput * {Types::DescribeModelExplainabilityJobDefinitionResponse#model_explainability_job_output_config #model_explainability_job_output_config} => Types::MonitoringOutputConfig * {Types::DescribeModelExplainabilityJobDefinitionResponse#job_resources #job_resources} => Types::MonitoringResources * {Types::DescribeModelExplainabilityJobDefinitionResponse#network_config #network_config} => Types::MonitoringNetworkConfig * {Types::DescribeModelExplainabilityJobDefinitionResponse#role_arn #role_arn} => String * {Types::DescribeModelExplainabilityJobDefinitionResponse#stopping_condition #stopping_condition} => Types::MonitoringStoppingCondition
@example Request syntax with placeholder values
resp = client.describe_model_explainability_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@example Response structure
resp.job_definition_arn #=> String resp.job_definition_name #=> String resp.creation_time #=> Time resp.model_explainability_baseline_config.baselining_job_name #=> String resp.model_explainability_baseline_config.constraints_resource.s3_uri #=> String resp.model_explainability_app_specification.image_uri #=> String resp.model_explainability_app_specification.config_uri #=> String resp.model_explainability_app_specification.environment #=> Hash resp.model_explainability_app_specification.environment["ProcessingEnvironmentKey"] #=> String resp.model_explainability_job_input.endpoint_input.endpoint_name #=> String resp.model_explainability_job_input.endpoint_input.local_path #=> String resp.model_explainability_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.model_explainability_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.model_explainability_job_input.endpoint_input.features_attribute #=> String resp.model_explainability_job_input.endpoint_input.inference_attribute #=> String resp.model_explainability_job_input.endpoint_input.probability_attribute #=> String resp.model_explainability_job_input.endpoint_input.probability_threshold_attribute #=> Float resp.model_explainability_job_input.endpoint_input.start_time_offset #=> String resp.model_explainability_job_input.endpoint_input.end_time_offset #=> String resp.model_explainability_job_output_config.monitoring_outputs #=> Array resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.model_explainability_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.model_explainability_job_output_config.kms_key_id #=> String resp.job_resources.cluster_config.instance_count #=> Integer resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.job_resources.cluster_config.volume_size_in_gb #=> Integer resp.job_resources.cluster_config.volume_kms_key_id #=> String resp.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.network_config.enable_network_isolation #=> Boolean resp.network_config.vpc_config.security_group_ids #=> Array resp.network_config.vpc_config.security_group_ids[0] #=> String resp.network_config.vpc_config.subnets #=> Array resp.network_config.vpc_config.subnets[0] #=> String resp.role_arn #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModelExplainabilityJobDefinition AWS API Documentation
@overload describe_model_explainability_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9337 def describe_model_explainability_job_definition(params = {}, options = {}) req = build_request(:describe_model_explainability_job_definition, params) req.send_request(options) end
Returns a description of the specified model package, which is used to create Amazon SageMaker
models or list them on Amazon Web Services Marketplace.
To create models in Amazon SageMaker
, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
@option params [required, String] :model_package_name
The name or Amazon Resource Name (ARN) of the model package to describe. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
@return [Types::DescribeModelPackageOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelPackageOutput#model_package_name #model_package_name} => String * {Types::DescribeModelPackageOutput#model_package_group_name #model_package_group_name} => String * {Types::DescribeModelPackageOutput#model_package_version #model_package_version} => Integer * {Types::DescribeModelPackageOutput#model_package_arn #model_package_arn} => String * {Types::DescribeModelPackageOutput#model_package_description #model_package_description} => String * {Types::DescribeModelPackageOutput#creation_time #creation_time} => Time * {Types::DescribeModelPackageOutput#inference_specification #inference_specification} => Types::InferenceSpecification * {Types::DescribeModelPackageOutput#source_algorithm_specification #source_algorithm_specification} => Types::SourceAlgorithmSpecification * {Types::DescribeModelPackageOutput#validation_specification #validation_specification} => Types::ModelPackageValidationSpecification * {Types::DescribeModelPackageOutput#model_package_status #model_package_status} => String * {Types::DescribeModelPackageOutput#model_package_status_details #model_package_status_details} => Types::ModelPackageStatusDetails * {Types::DescribeModelPackageOutput#certify_for_marketplace #certify_for_marketplace} => Boolean * {Types::DescribeModelPackageOutput#model_approval_status #model_approval_status} => String * {Types::DescribeModelPackageOutput#created_by #created_by} => Types::UserContext * {Types::DescribeModelPackageOutput#metadata_properties #metadata_properties} => Types::MetadataProperties * {Types::DescribeModelPackageOutput#model_metrics #model_metrics} => Types::ModelMetrics * {Types::DescribeModelPackageOutput#last_modified_time #last_modified_time} => Time * {Types::DescribeModelPackageOutput#last_modified_by #last_modified_by} => Types::UserContext * {Types::DescribeModelPackageOutput#approval_description #approval_description} => String
@example Request syntax with placeholder values
resp = client.describe_model_package({ model_package_name: "VersionedArnOrName", # required })
@example Response structure
resp.model_package_name #=> String resp.model_package_group_name #=> String resp.model_package_version #=> Integer resp.model_package_arn #=> String resp.model_package_description #=> String resp.creation_time #=> Time resp.inference_specification.containers #=> Array resp.inference_specification.containers[0].container_hostname #=> String resp.inference_specification.containers[0].image #=> String resp.inference_specification.containers[0].image_digest #=> String resp.inference_specification.containers[0].model_data_url #=> String resp.inference_specification.containers[0].product_id #=> String resp.inference_specification.containers[0].environment #=> Hash resp.inference_specification.containers[0].environment["EnvironmentKey"] #=> String resp.inference_specification.supported_transform_instance_types #=> Array resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.inference_specification.supported_realtime_inference_instance_types #=> Array resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge" resp.inference_specification.supported_content_types #=> Array resp.inference_specification.supported_content_types[0] #=> String resp.inference_specification.supported_response_mime_types #=> Array resp.inference_specification.supported_response_mime_types[0] #=> String resp.source_algorithm_specification.source_algorithms #=> Array resp.source_algorithm_specification.source_algorithms[0].model_data_url #=> String resp.source_algorithm_specification.source_algorithms[0].algorithm_name #=> String resp.validation_specification.validation_role #=> String resp.validation_specification.validation_profiles #=> Array resp.validation_specification.validation_profiles[0].profile_name #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord" resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String resp.model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting" resp.model_package_status_details.validation_statuses #=> Array resp.model_package_status_details.validation_statuses[0].name #=> String resp.model_package_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.model_package_status_details.validation_statuses[0].failure_reason #=> String resp.model_package_status_details.image_scan_statuses #=> Array resp.model_package_status_details.image_scan_statuses[0].name #=> String resp.model_package_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.model_package_status_details.image_scan_statuses[0].failure_reason #=> String resp.certify_for_marketplace #=> Boolean resp.model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval" resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.metadata_properties.commit_id #=> String resp.metadata_properties.repository #=> String resp.metadata_properties.generated_by #=> String resp.metadata_properties.project_id #=> String resp.model_metrics.model_quality.statistics.content_type #=> String resp.model_metrics.model_quality.statistics.content_digest #=> String resp.model_metrics.model_quality.statistics.s3_uri #=> String resp.model_metrics.model_quality.constraints.content_type #=> String resp.model_metrics.model_quality.constraints.content_digest #=> String resp.model_metrics.model_quality.constraints.s3_uri #=> String resp.model_metrics.model_data_quality.statistics.content_type #=> String resp.model_metrics.model_data_quality.statistics.content_digest #=> String resp.model_metrics.model_data_quality.statistics.s3_uri #=> String resp.model_metrics.model_data_quality.constraints.content_type #=> String resp.model_metrics.model_data_quality.constraints.content_digest #=> String resp.model_metrics.model_data_quality.constraints.s3_uri #=> String resp.model_metrics.bias.report.content_type #=> String resp.model_metrics.bias.report.content_digest #=> String resp.model_metrics.bias.report.s3_uri #=> String resp.model_metrics.explainability.report.content_type #=> String resp.model_metrics.explainability.report.content_digest #=> String resp.model_metrics.explainability.report.s3_uri #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String resp.approval_description #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModelPackage AWS API Documentation
@overload describe_model_package
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9477 def describe_model_package(params = {}, options = {}) req = build_request(:describe_model_package, params) req.send_request(options) end
Gets a description for the specified model group.
@option params [required, String] :model_package_group_name
The name of the model group to describe.
@return [Types::DescribeModelPackageGroupOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelPackageGroupOutput#model_package_group_name #model_package_group_name} => String * {Types::DescribeModelPackageGroupOutput#model_package_group_arn #model_package_group_arn} => String * {Types::DescribeModelPackageGroupOutput#model_package_group_description #model_package_group_description} => String * {Types::DescribeModelPackageGroupOutput#creation_time #creation_time} => Time * {Types::DescribeModelPackageGroupOutput#created_by #created_by} => Types::UserContext * {Types::DescribeModelPackageGroupOutput#model_package_group_status #model_package_group_status} => String
@example Request syntax with placeholder values
resp = client.describe_model_package_group({ model_package_group_name: "ArnOrName", # required })
@example Response structure
resp.model_package_group_name #=> String resp.model_package_group_arn #=> String resp.model_package_group_description #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.model_package_group_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting", "DeleteFailed"
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModelPackageGroup AWS API Documentation
@overload describe_model_package_group
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9517 def describe_model_package_group(params = {}, options = {}) req = build_request(:describe_model_package_group, params) req.send_request(options) end
Returns a description of a model quality job definition.
@option params [required, String] :job_definition_name
The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@return [Types::DescribeModelQualityJobDefinitionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeModelQualityJobDefinitionResponse#job_definition_arn #job_definition_arn} => String * {Types::DescribeModelQualityJobDefinitionResponse#job_definition_name #job_definition_name} => String * {Types::DescribeModelQualityJobDefinitionResponse#creation_time #creation_time} => Time * {Types::DescribeModelQualityJobDefinitionResponse#model_quality_baseline_config #model_quality_baseline_config} => Types::ModelQualityBaselineConfig * {Types::DescribeModelQualityJobDefinitionResponse#model_quality_app_specification #model_quality_app_specification} => Types::ModelQualityAppSpecification * {Types::DescribeModelQualityJobDefinitionResponse#model_quality_job_input #model_quality_job_input} => Types::ModelQualityJobInput * {Types::DescribeModelQualityJobDefinitionResponse#model_quality_job_output_config #model_quality_job_output_config} => Types::MonitoringOutputConfig * {Types::DescribeModelQualityJobDefinitionResponse#job_resources #job_resources} => Types::MonitoringResources * {Types::DescribeModelQualityJobDefinitionResponse#network_config #network_config} => Types::MonitoringNetworkConfig * {Types::DescribeModelQualityJobDefinitionResponse#role_arn #role_arn} => String * {Types::DescribeModelQualityJobDefinitionResponse#stopping_condition #stopping_condition} => Types::MonitoringStoppingCondition
@example Request syntax with placeholder values
resp = client.describe_model_quality_job_definition({ job_definition_name: "MonitoringJobDefinitionName", # required })
@example Response structure
resp.job_definition_arn #=> String resp.job_definition_name #=> String resp.creation_time #=> Time resp.model_quality_baseline_config.baselining_job_name #=> String resp.model_quality_baseline_config.constraints_resource.s3_uri #=> String resp.model_quality_app_specification.image_uri #=> String resp.model_quality_app_specification.container_entrypoint #=> Array resp.model_quality_app_specification.container_entrypoint[0] #=> String resp.model_quality_app_specification.container_arguments #=> Array resp.model_quality_app_specification.container_arguments[0] #=> String resp.model_quality_app_specification.record_preprocessor_source_uri #=> String resp.model_quality_app_specification.post_analytics_processor_source_uri #=> String resp.model_quality_app_specification.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression" resp.model_quality_app_specification.environment #=> Hash resp.model_quality_app_specification.environment["ProcessingEnvironmentKey"] #=> String resp.model_quality_job_input.endpoint_input.endpoint_name #=> String resp.model_quality_job_input.endpoint_input.local_path #=> String resp.model_quality_job_input.endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.model_quality_job_input.endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.model_quality_job_input.endpoint_input.features_attribute #=> String resp.model_quality_job_input.endpoint_input.inference_attribute #=> String resp.model_quality_job_input.endpoint_input.probability_attribute #=> String resp.model_quality_job_input.endpoint_input.probability_threshold_attribute #=> Float resp.model_quality_job_input.endpoint_input.start_time_offset #=> String resp.model_quality_job_input.endpoint_input.end_time_offset #=> String resp.model_quality_job_input.ground_truth_s3_input.s3_uri #=> String resp.model_quality_job_output_config.monitoring_outputs #=> Array resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.model_quality_job_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.model_quality_job_output_config.kms_key_id #=> String resp.job_resources.cluster_config.instance_count #=> Integer resp.job_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.job_resources.cluster_config.volume_size_in_gb #=> Integer resp.job_resources.cluster_config.volume_kms_key_id #=> String resp.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.network_config.enable_network_isolation #=> Boolean resp.network_config.vpc_config.security_group_ids #=> Array resp.network_config.vpc_config.security_group_ids[0] #=> String resp.network_config.vpc_config.subnets #=> Array resp.network_config.vpc_config.subnets[0] #=> String resp.role_arn #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeModelQualityJobDefinition AWS API Documentation
@overload describe_model_quality_job_definition
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9598 def describe_model_quality_job_definition(params = {}, options = {}) req = build_request(:describe_model_quality_job_definition, params) req.send_request(options) end
Describes the schedule for a monitoring job.
@option params [required, String] :monitoring_schedule_name
Name of a previously created monitoring schedule.
@return [Types::DescribeMonitoringScheduleResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeMonitoringScheduleResponse#monitoring_schedule_arn #monitoring_schedule_arn} => String * {Types::DescribeMonitoringScheduleResponse#monitoring_schedule_name #monitoring_schedule_name} => String * {Types::DescribeMonitoringScheduleResponse#monitoring_schedule_status #monitoring_schedule_status} => String * {Types::DescribeMonitoringScheduleResponse#monitoring_type #monitoring_type} => String * {Types::DescribeMonitoringScheduleResponse#failure_reason #failure_reason} => String * {Types::DescribeMonitoringScheduleResponse#creation_time #creation_time} => Time * {Types::DescribeMonitoringScheduleResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeMonitoringScheduleResponse#monitoring_schedule_config #monitoring_schedule_config} => Types::MonitoringScheduleConfig * {Types::DescribeMonitoringScheduleResponse#endpoint_name #endpoint_name} => String * {Types::DescribeMonitoringScheduleResponse#last_monitoring_execution_summary #last_monitoring_execution_summary} => Types::MonitoringExecutionSummary
@example Request syntax with placeholder values
resp = client.describe_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required })
@example Response structure
resp.monitoring_schedule_arn #=> String resp.monitoring_schedule_name #=> String resp.monitoring_schedule_status #=> String, one of "Pending", "Failed", "Scheduled", "Stopped" resp.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.failure_reason #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.monitoring_schedule_config.schedule_config.schedule_expression #=> String resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.baselining_job_name #=> String resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.constraints_resource.s3_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.baseline_config.statistics_resource.s3_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs #=> Array resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.endpoint_name #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.local_path #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.features_attribute #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.inference_attribute #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_attribute #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_threshold_attribute #=> Float resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.start_time_offset #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.end_time_offset #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs #=> Array resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.kms_key_id #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_count #=> Integer resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_size_in_gb #=> Integer resp.monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_kms_key_id #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.image_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint #=> Array resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint[0] #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments #=> Array resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments[0] #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.record_preprocessor_source_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.post_analytics_processor_source_uri #=> String resp.monitoring_schedule_config.monitoring_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer resp.monitoring_schedule_config.monitoring_job_definition.environment #=> Hash resp.monitoring_schedule_config.monitoring_job_definition.environment["ProcessingEnvironmentKey"] #=> String resp.monitoring_schedule_config.monitoring_job_definition.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.monitoring_schedule_config.monitoring_job_definition.network_config.enable_network_isolation #=> Boolean resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids #=> Array resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids[0] #=> String resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets #=> Array resp.monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets[0] #=> String resp.monitoring_schedule_config.monitoring_job_definition.role_arn #=> String resp.monitoring_schedule_config.monitoring_job_definition_name #=> String resp.monitoring_schedule_config.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.endpoint_name #=> String resp.last_monitoring_execution_summary.monitoring_schedule_name #=> String resp.last_monitoring_execution_summary.scheduled_time #=> Time resp.last_monitoring_execution_summary.creation_time #=> Time resp.last_monitoring_execution_summary.last_modified_time #=> Time resp.last_monitoring_execution_summary.monitoring_execution_status #=> String, one of "Pending", "Completed", "CompletedWithViolations", "InProgress", "Failed", "Stopping", "Stopped" resp.last_monitoring_execution_summary.processing_job_arn #=> String resp.last_monitoring_execution_summary.endpoint_name #=> String resp.last_monitoring_execution_summary.failure_reason #=> String resp.last_monitoring_execution_summary.monitoring_job_definition_name #=> String resp.last_monitoring_execution_summary.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability"
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeMonitoringSchedule AWS API Documentation
@overload describe_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9695 def describe_monitoring_schedule(params = {}, options = {}) req = build_request(:describe_monitoring_schedule, params) req.send_request(options) end
Returns information about a notebook instance.
@option params [required, String] :notebook_instance_name
The name of the notebook instance that you want information about.
@return [Types::DescribeNotebookInstanceOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeNotebookInstanceOutput#notebook_instance_arn #notebook_instance_arn} => String * {Types::DescribeNotebookInstanceOutput#notebook_instance_name #notebook_instance_name} => String * {Types::DescribeNotebookInstanceOutput#notebook_instance_status #notebook_instance_status} => String * {Types::DescribeNotebookInstanceOutput#failure_reason #failure_reason} => String * {Types::DescribeNotebookInstanceOutput#url #url} => String * {Types::DescribeNotebookInstanceOutput#instance_type #instance_type} => String * {Types::DescribeNotebookInstanceOutput#subnet_id #subnet_id} => String * {Types::DescribeNotebookInstanceOutput#security_groups #security_groups} => Array<String> * {Types::DescribeNotebookInstanceOutput#role_arn #role_arn} => String * {Types::DescribeNotebookInstanceOutput#kms_key_id #kms_key_id} => String * {Types::DescribeNotebookInstanceOutput#network_interface_id #network_interface_id} => String * {Types::DescribeNotebookInstanceOutput#last_modified_time #last_modified_time} => Time * {Types::DescribeNotebookInstanceOutput#creation_time #creation_time} => Time * {Types::DescribeNotebookInstanceOutput#notebook_instance_lifecycle_config_name #notebook_instance_lifecycle_config_name} => String * {Types::DescribeNotebookInstanceOutput#direct_internet_access #direct_internet_access} => String * {Types::DescribeNotebookInstanceOutput#volume_size_in_gb #volume_size_in_gb} => Integer * {Types::DescribeNotebookInstanceOutput#accelerator_types #accelerator_types} => Array<String> * {Types::DescribeNotebookInstanceOutput#default_code_repository #default_code_repository} => String * {Types::DescribeNotebookInstanceOutput#additional_code_repositories #additional_code_repositories} => Array<String> * {Types::DescribeNotebookInstanceOutput#root_access #root_access} => String * {Types::DescribeNotebookInstanceOutput#platform_identifier #platform_identifier} => String
@example Request syntax with placeholder values
resp = client.describe_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required })
@example Response structure
resp.notebook_instance_arn #=> String resp.notebook_instance_name #=> String resp.notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating" resp.failure_reason #=> String resp.url #=> String resp.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.subnet_id #=> String resp.security_groups #=> Array resp.security_groups[0] #=> String resp.role_arn #=> String resp.kms_key_id #=> String resp.network_interface_id #=> String resp.last_modified_time #=> Time resp.creation_time #=> Time resp.notebook_instance_lifecycle_config_name #=> String resp.direct_internet_access #=> String, one of "Enabled", "Disabled" resp.volume_size_in_gb #=> Integer resp.accelerator_types #=> Array resp.accelerator_types[0] #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge" resp.default_code_repository #=> String resp.additional_code_repositories #=> Array resp.additional_code_repositories[0] #=> String resp.root_access #=> String, one of "Enabled", "Disabled" resp.platform_identifier #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* notebook_instance_deleted * notebook_instance_in_service * notebook_instance_stopped
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeNotebookInstance AWS API Documentation
@overload describe_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9773 def describe_notebook_instance(params = {}, options = {}) req = build_request(:describe_notebook_instance, params) req.send_request(options) end
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance].
[1]: docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html
@option params [required, String] :notebook_instance_lifecycle_config_name
The name of the lifecycle configuration to describe.
@return [Types::DescribeNotebookInstanceLifecycleConfigOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeNotebookInstanceLifecycleConfigOutput#notebook_instance_lifecycle_config_arn #notebook_instance_lifecycle_config_arn} => String * {Types::DescribeNotebookInstanceLifecycleConfigOutput#notebook_instance_lifecycle_config_name #notebook_instance_lifecycle_config_name} => String * {Types::DescribeNotebookInstanceLifecycleConfigOutput#on_create #on_create} => Array<Types::NotebookInstanceLifecycleHook> * {Types::DescribeNotebookInstanceLifecycleConfigOutput#on_start #on_start} => Array<Types::NotebookInstanceLifecycleHook> * {Types::DescribeNotebookInstanceLifecycleConfigOutput#last_modified_time #last_modified_time} => Time * {Types::DescribeNotebookInstanceLifecycleConfigOutput#creation_time #creation_time} => Time
@example Request syntax with placeholder values
resp = client.describe_notebook_instance_lifecycle_config({ notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required })
@example Response structure
resp.notebook_instance_lifecycle_config_arn #=> String resp.notebook_instance_lifecycle_config_name #=> String resp.on_create #=> Array resp.on_create[0].content #=> String resp.on_start #=> Array resp.on_start[0].content #=> String resp.last_modified_time #=> Time resp.creation_time #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeNotebookInstanceLifecycleConfig AWS API Documentation
@overload describe_notebook_instance_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9820 def describe_notebook_instance_lifecycle_config(params = {}, options = {}) req = build_request(:describe_notebook_instance_lifecycle_config, params) req.send_request(options) end
Describes the details of a pipeline.
@option params [required, String] :pipeline_name
The name of the pipeline to describe.
@return [Types::DescribePipelineResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribePipelineResponse#pipeline_arn #pipeline_arn} => String * {Types::DescribePipelineResponse#pipeline_name #pipeline_name} => String * {Types::DescribePipelineResponse#pipeline_display_name #pipeline_display_name} => String * {Types::DescribePipelineResponse#pipeline_definition #pipeline_definition} => String * {Types::DescribePipelineResponse#pipeline_description #pipeline_description} => String * {Types::DescribePipelineResponse#role_arn #role_arn} => String * {Types::DescribePipelineResponse#pipeline_status #pipeline_status} => String * {Types::DescribePipelineResponse#creation_time #creation_time} => Time * {Types::DescribePipelineResponse#last_modified_time #last_modified_time} => Time * {Types::DescribePipelineResponse#last_run_time #last_run_time} => Time * {Types::DescribePipelineResponse#created_by #created_by} => Types::UserContext * {Types::DescribePipelineResponse#last_modified_by #last_modified_by} => Types::UserContext
@example Request syntax with placeholder values
resp = client.describe_pipeline({ pipeline_name: "PipelineName", # required })
@example Response structure
resp.pipeline_arn #=> String resp.pipeline_name #=> String resp.pipeline_display_name #=> String resp.pipeline_definition #=> String resp.pipeline_description #=> String resp.role_arn #=> String resp.pipeline_status #=> String, one of "Active" resp.creation_time #=> Time resp.last_modified_time #=> Time resp.last_run_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribePipeline AWS API Documentation
@overload describe_pipeline
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9874 def describe_pipeline(params = {}, options = {}) req = build_request(:describe_pipeline, params) req.send_request(options) end
Describes the details of an execution's pipeline definition.
@option params [required, String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@return [Types::DescribePipelineDefinitionForExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribePipelineDefinitionForExecutionResponse#pipeline_definition #pipeline_definition} => String * {Types::DescribePipelineDefinitionForExecutionResponse#creation_time #creation_time} => Time
@example Request syntax with placeholder values
resp = client.describe_pipeline_definition_for_execution({ pipeline_execution_arn: "PipelineExecutionArn", # required })
@example Response structure
resp.pipeline_definition #=> String resp.creation_time #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribePipelineDefinitionForExecution AWS API Documentation
@overload describe_pipeline_definition_for_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9904 def describe_pipeline_definition_for_execution(params = {}, options = {}) req = build_request(:describe_pipeline_definition_for_execution, params) req.send_request(options) end
Describes the details of a pipeline execution.
@option params [required, String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@return [Types::DescribePipelineExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribePipelineExecutionResponse#pipeline_arn #pipeline_arn} => String * {Types::DescribePipelineExecutionResponse#pipeline_execution_arn #pipeline_execution_arn} => String * {Types::DescribePipelineExecutionResponse#pipeline_execution_display_name #pipeline_execution_display_name} => String * {Types::DescribePipelineExecutionResponse#pipeline_execution_status #pipeline_execution_status} => String * {Types::DescribePipelineExecutionResponse#pipeline_execution_description #pipeline_execution_description} => String * {Types::DescribePipelineExecutionResponse#pipeline_experiment_config #pipeline_experiment_config} => Types::PipelineExperimentConfig * {Types::DescribePipelineExecutionResponse#failure_reason #failure_reason} => String * {Types::DescribePipelineExecutionResponse#creation_time #creation_time} => Time * {Types::DescribePipelineExecutionResponse#last_modified_time #last_modified_time} => Time * {Types::DescribePipelineExecutionResponse#created_by #created_by} => Types::UserContext * {Types::DescribePipelineExecutionResponse#last_modified_by #last_modified_by} => Types::UserContext
@example Request syntax with placeholder values
resp = client.describe_pipeline_execution({ pipeline_execution_arn: "PipelineExecutionArn", # required })
@example Response structure
resp.pipeline_arn #=> String resp.pipeline_execution_arn #=> String resp.pipeline_execution_display_name #=> String resp.pipeline_execution_status #=> String, one of "Executing", "Stopping", "Stopped", "Failed", "Succeeded" resp.pipeline_execution_description #=> String resp.pipeline_experiment_config.experiment_name #=> String resp.pipeline_experiment_config.trial_name #=> String resp.failure_reason #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribePipelineExecution AWS API Documentation
@overload describe_pipeline_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 9957 def describe_pipeline_execution(params = {}, options = {}) req = build_request(:describe_pipeline_execution, params) req.send_request(options) end
Returns a description of a processing job.
@option params [required, String] :processing_job_name
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
@return [Types::DescribeProcessingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeProcessingJobResponse#processing_inputs #processing_inputs} => Array<Types::ProcessingInput> * {Types::DescribeProcessingJobResponse#processing_output_config #processing_output_config} => Types::ProcessingOutputConfig * {Types::DescribeProcessingJobResponse#processing_job_name #processing_job_name} => String * {Types::DescribeProcessingJobResponse#processing_resources #processing_resources} => Types::ProcessingResources * {Types::DescribeProcessingJobResponse#stopping_condition #stopping_condition} => Types::ProcessingStoppingCondition * {Types::DescribeProcessingJobResponse#app_specification #app_specification} => Types::AppSpecification * {Types::DescribeProcessingJobResponse#environment #environment} => Hash<String,String> * {Types::DescribeProcessingJobResponse#network_config #network_config} => Types::NetworkConfig * {Types::DescribeProcessingJobResponse#role_arn #role_arn} => String * {Types::DescribeProcessingJobResponse#experiment_config #experiment_config} => Types::ExperimentConfig * {Types::DescribeProcessingJobResponse#processing_job_arn #processing_job_arn} => String * {Types::DescribeProcessingJobResponse#processing_job_status #processing_job_status} => String * {Types::DescribeProcessingJobResponse#exit_message #exit_message} => String * {Types::DescribeProcessingJobResponse#failure_reason #failure_reason} => String * {Types::DescribeProcessingJobResponse#processing_end_time #processing_end_time} => Time * {Types::DescribeProcessingJobResponse#processing_start_time #processing_start_time} => Time * {Types::DescribeProcessingJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeProcessingJobResponse#creation_time #creation_time} => Time * {Types::DescribeProcessingJobResponse#monitoring_schedule_arn #monitoring_schedule_arn} => String * {Types::DescribeProcessingJobResponse#auto_ml_job_arn #auto_ml_job_arn} => String * {Types::DescribeProcessingJobResponse#training_job_arn #training_job_arn} => String
@example Request syntax with placeholder values
resp = client.describe_processing_job({ processing_job_name: "ProcessingJobName", # required })
@example Response structure
resp.processing_inputs #=> Array resp.processing_inputs[0].input_name #=> String resp.processing_inputs[0].app_managed #=> Boolean resp.processing_inputs[0].s3_input.s3_uri #=> String resp.processing_inputs[0].s3_input.local_path #=> String resp.processing_inputs[0].s3_input.s3_data_type #=> String, one of "ManifestFile", "S3Prefix" resp.processing_inputs[0].s3_input.s3_input_mode #=> String, one of "Pipe", "File" resp.processing_inputs[0].s3_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.processing_inputs[0].s3_input.s3_compression_type #=> String, one of "None", "Gzip" resp.processing_inputs[0].dataset_definition.athena_dataset_definition.catalog #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.database #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.query_string #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.work_group #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_s3_uri #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.kms_key_id #=> String resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_format #=> String, one of "PARQUET", "ORC", "AVRO", "JSON", "TEXTFILE" resp.processing_inputs[0].dataset_definition.athena_dataset_definition.output_compression #=> String, one of "GZIP", "SNAPPY", "ZLIB" resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_id #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.database #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.db_user #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.query_string #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_role_arn #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_s3_uri #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.kms_key_id #=> String resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_format #=> String, one of "PARQUET", "CSV" resp.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_compression #=> String, one of "None", "GZIP", "BZIP2", "ZSTD", "SNAPPY" resp.processing_inputs[0].dataset_definition.local_path #=> String resp.processing_inputs[0].dataset_definition.data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.processing_inputs[0].dataset_definition.input_mode #=> String, one of "Pipe", "File" resp.processing_output_config.outputs #=> Array resp.processing_output_config.outputs[0].output_name #=> String resp.processing_output_config.outputs[0].s3_output.s3_uri #=> String resp.processing_output_config.outputs[0].s3_output.local_path #=> String resp.processing_output_config.outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.processing_output_config.outputs[0].feature_store_output.feature_group_name #=> String resp.processing_output_config.outputs[0].app_managed #=> Boolean resp.processing_output_config.kms_key_id #=> String resp.processing_job_name #=> String resp.processing_resources.cluster_config.instance_count #=> Integer resp.processing_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.processing_resources.cluster_config.volume_size_in_gb #=> Integer resp.processing_resources.cluster_config.volume_kms_key_id #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer resp.app_specification.image_uri #=> String resp.app_specification.container_entrypoint #=> Array resp.app_specification.container_entrypoint[0] #=> String resp.app_specification.container_arguments #=> Array resp.app_specification.container_arguments[0] #=> String resp.environment #=> Hash resp.environment["ProcessingEnvironmentKey"] #=> String resp.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.network_config.enable_network_isolation #=> Boolean resp.network_config.vpc_config.security_group_ids #=> Array resp.network_config.vpc_config.security_group_ids[0] #=> String resp.network_config.vpc_config.subnets #=> Array resp.network_config.vpc_config.subnets[0] #=> String resp.role_arn #=> String resp.experiment_config.experiment_name #=> String resp.experiment_config.trial_name #=> String resp.experiment_config.trial_component_display_name #=> String resp.processing_job_arn #=> String resp.processing_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.exit_message #=> String resp.failure_reason #=> String resp.processing_end_time #=> Time resp.processing_start_time #=> Time resp.last_modified_time #=> Time resp.creation_time #=> Time resp.monitoring_schedule_arn #=> String resp.auto_ml_job_arn #=> String resp.training_job_arn #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* processing_job_completed_or_stopped
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeProcessingJob AWS API Documentation
@overload describe_processing_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10081 def describe_processing_job(params = {}, options = {}) req = build_request(:describe_processing_job, params) req.send_request(options) end
Describes the details of a project.
@option params [required, String] :project_name
The name of the project to describe.
@return [Types::DescribeProjectOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeProjectOutput#project_arn #project_arn} => String * {Types::DescribeProjectOutput#project_name #project_name} => String * {Types::DescribeProjectOutput#project_id #project_id} => String * {Types::DescribeProjectOutput#project_description #project_description} => String * {Types::DescribeProjectOutput#service_catalog_provisioning_details #service_catalog_provisioning_details} => Types::ServiceCatalogProvisioningDetails * {Types::DescribeProjectOutput#service_catalog_provisioned_product_details #service_catalog_provisioned_product_details} => Types::ServiceCatalogProvisionedProductDetails * {Types::DescribeProjectOutput#project_status #project_status} => String * {Types::DescribeProjectOutput#created_by #created_by} => Types::UserContext * {Types::DescribeProjectOutput#creation_time #creation_time} => Time
@example Request syntax with placeholder values
resp = client.describe_project({ project_name: "ProjectEntityName", # required })
@example Response structure
resp.project_arn #=> String resp.project_name #=> String resp.project_id #=> String resp.project_description #=> String resp.service_catalog_provisioning_details.product_id #=> String resp.service_catalog_provisioning_details.provisioning_artifact_id #=> String resp.service_catalog_provisioning_details.path_id #=> String resp.service_catalog_provisioning_details.provisioning_parameters #=> Array resp.service_catalog_provisioning_details.provisioning_parameters[0].key #=> String resp.service_catalog_provisioning_details.provisioning_parameters[0].value #=> String resp.service_catalog_provisioned_product_details.provisioned_product_id #=> String resp.service_catalog_provisioned_product_details.provisioned_product_status_message #=> String resp.project_status #=> String, one of "Pending", "CreateInProgress", "CreateCompleted", "CreateFailed", "DeleteInProgress", "DeleteFailed", "DeleteCompleted" resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.creation_time #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeProject AWS API Documentation
@overload describe_project
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10133 def describe_project(params = {}, options = {}) req = build_request(:describe_project, params) req.send_request(options) end
Describes the Studio Lifecycle Configuration.
@option params [required, String] :studio_lifecycle_config_name
The name of the Studio Lifecycle Configuration to describe.
@return [Types::DescribeStudioLifecycleConfigResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeStudioLifecycleConfigResponse#studio_lifecycle_config_arn #studio_lifecycle_config_arn} => String * {Types::DescribeStudioLifecycleConfigResponse#studio_lifecycle_config_name #studio_lifecycle_config_name} => String * {Types::DescribeStudioLifecycleConfigResponse#creation_time #creation_time} => Time * {Types::DescribeStudioLifecycleConfigResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeStudioLifecycleConfigResponse#studio_lifecycle_config_content #studio_lifecycle_config_content} => String * {Types::DescribeStudioLifecycleConfigResponse#studio_lifecycle_config_app_type #studio_lifecycle_config_app_type} => String
@example Request syntax with placeholder values
resp = client.describe_studio_lifecycle_config({ studio_lifecycle_config_name: "StudioLifecycleConfigName", # required })
@example Response structure
resp.studio_lifecycle_config_arn #=> String resp.studio_lifecycle_config_name #=> String resp.creation_time #=> Time resp.last_modified_time #=> Time resp.studio_lifecycle_config_content #=> String resp.studio_lifecycle_config_app_type #=> String, one of "JupyterServer", "KernelGateway"
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeStudioLifecycleConfig AWS API Documentation
@overload describe_studio_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10171 def describe_studio_lifecycle_config(params = {}, options = {}) req = build_request(:describe_studio_lifecycle_config, params) req.send_request(options) end
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
@option params [required, String] :workteam_arn
The Amazon Resource Name (ARN) of the subscribed work team to describe.
@return [Types::DescribeSubscribedWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeSubscribedWorkteamResponse#subscribed_workteam #subscribed_workteam} => Types::SubscribedWorkteam
@example Request syntax with placeholder values
resp = client.describe_subscribed_workteam({ workteam_arn: "WorkteamArn", # required })
@example Response structure
resp.subscribed_workteam.workteam_arn #=> String resp.subscribed_workteam.marketplace_title #=> String resp.subscribed_workteam.seller_name #=> String resp.subscribed_workteam.marketplace_description #=> String resp.subscribed_workteam.listing_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeSubscribedWorkteam AWS API Documentation
@overload describe_subscribed_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10206 def describe_subscribed_workteam(params = {}, options = {}) req = build_request(:describe_subscribed_workteam, params) req.send_request(options) end
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails, `TrainingJobStatus` is `Failed` and, depending on the `FailureReason`, attributes like `TrainingStartTime`, `TrainingTimeInSeconds`, `TrainingEndTime`, and `BillableTimeInSeconds` may not be present in the response.
@option params [required, String] :training_job_name
The name of the training job.
@return [Types::DescribeTrainingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeTrainingJobResponse#training_job_name #training_job_name} => String * {Types::DescribeTrainingJobResponse#training_job_arn #training_job_arn} => String * {Types::DescribeTrainingJobResponse#tuning_job_arn #tuning_job_arn} => String * {Types::DescribeTrainingJobResponse#labeling_job_arn #labeling_job_arn} => String * {Types::DescribeTrainingJobResponse#auto_ml_job_arn #auto_ml_job_arn} => String * {Types::DescribeTrainingJobResponse#model_artifacts #model_artifacts} => Types::ModelArtifacts * {Types::DescribeTrainingJobResponse#training_job_status #training_job_status} => String * {Types::DescribeTrainingJobResponse#secondary_status #secondary_status} => String * {Types::DescribeTrainingJobResponse#failure_reason #failure_reason} => String * {Types::DescribeTrainingJobResponse#hyper_parameters #hyper_parameters} => Hash<String,String> * {Types::DescribeTrainingJobResponse#algorithm_specification #algorithm_specification} => Types::AlgorithmSpecification * {Types::DescribeTrainingJobResponse#role_arn #role_arn} => String * {Types::DescribeTrainingJobResponse#input_data_config #input_data_config} => Array<Types::Channel> * {Types::DescribeTrainingJobResponse#output_data_config #output_data_config} => Types::OutputDataConfig * {Types::DescribeTrainingJobResponse#resource_config #resource_config} => Types::ResourceConfig * {Types::DescribeTrainingJobResponse#vpc_config #vpc_config} => Types::VpcConfig * {Types::DescribeTrainingJobResponse#stopping_condition #stopping_condition} => Types::StoppingCondition * {Types::DescribeTrainingJobResponse#creation_time #creation_time} => Time * {Types::DescribeTrainingJobResponse#training_start_time #training_start_time} => Time * {Types::DescribeTrainingJobResponse#training_end_time #training_end_time} => Time * {Types::DescribeTrainingJobResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeTrainingJobResponse#secondary_status_transitions #secondary_status_transitions} => Array<Types::SecondaryStatusTransition> * {Types::DescribeTrainingJobResponse#final_metric_data_list #final_metric_data_list} => Array<Types::MetricData> * {Types::DescribeTrainingJobResponse#enable_network_isolation #enable_network_isolation} => Boolean * {Types::DescribeTrainingJobResponse#enable_inter_container_traffic_encryption #enable_inter_container_traffic_encryption} => Boolean * {Types::DescribeTrainingJobResponse#enable_managed_spot_training #enable_managed_spot_training} => Boolean * {Types::DescribeTrainingJobResponse#checkpoint_config #checkpoint_config} => Types::CheckpointConfig * {Types::DescribeTrainingJobResponse#training_time_in_seconds #training_time_in_seconds} => Integer * {Types::DescribeTrainingJobResponse#billable_time_in_seconds #billable_time_in_seconds} => Integer * {Types::DescribeTrainingJobResponse#debug_hook_config #debug_hook_config} => Types::DebugHookConfig * {Types::DescribeTrainingJobResponse#experiment_config #experiment_config} => Types::ExperimentConfig * {Types::DescribeTrainingJobResponse#debug_rule_configurations #debug_rule_configurations} => Array<Types::DebugRuleConfiguration> * {Types::DescribeTrainingJobResponse#tensor_board_output_config #tensor_board_output_config} => Types::TensorBoardOutputConfig * {Types::DescribeTrainingJobResponse#debug_rule_evaluation_statuses #debug_rule_evaluation_statuses} => Array<Types::DebugRuleEvaluationStatus> * {Types::DescribeTrainingJobResponse#profiler_config #profiler_config} => Types::ProfilerConfig * {Types::DescribeTrainingJobResponse#profiler_rule_configurations #profiler_rule_configurations} => Array<Types::ProfilerRuleConfiguration> * {Types::DescribeTrainingJobResponse#profiler_rule_evaluation_statuses #profiler_rule_evaluation_statuses} => Array<Types::ProfilerRuleEvaluationStatus> * {Types::DescribeTrainingJobResponse#profiling_status #profiling_status} => String * {Types::DescribeTrainingJobResponse#retry_strategy #retry_strategy} => Types::RetryStrategy * {Types::DescribeTrainingJobResponse#environment #environment} => Hash<String,String>
@example Request syntax with placeholder values
resp = client.describe_training_job({ training_job_name: "TrainingJobName", # required })
@example Response structure
resp.training_job_name #=> String resp.training_job_arn #=> String resp.tuning_job_arn #=> String resp.labeling_job_arn #=> String resp.auto_ml_job_arn #=> String resp.model_artifacts.s3_model_artifacts #=> String resp.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.failure_reason #=> String resp.hyper_parameters #=> Hash resp.hyper_parameters["HyperParameterKey"] #=> String resp.algorithm_specification.training_image #=> String resp.algorithm_specification.algorithm_name #=> String resp.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File" resp.algorithm_specification.metric_definitions #=> Array resp.algorithm_specification.metric_definitions[0].name #=> String resp.algorithm_specification.metric_definitions[0].regex #=> String resp.algorithm_specification.enable_sage_maker_metrics_time_series #=> Boolean resp.role_arn #=> String resp.input_data_config #=> Array resp.input_data_config[0].channel_name #=> String resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.input_data_config[0].content_type #=> String resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.input_data_config[0].shuffle_config.seed #=> Integer resp.output_data_config.kms_key_id #=> String resp.output_data_config.s3_output_path #=> String resp.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.resource_config.instance_count #=> Integer resp.resource_config.volume_size_in_gb #=> Integer resp.resource_config.volume_kms_key_id #=> String resp.vpc_config.security_group_ids #=> Array resp.vpc_config.security_group_ids[0] #=> String resp.vpc_config.subnets #=> Array resp.vpc_config.subnets[0] #=> String resp.stopping_condition.max_runtime_in_seconds #=> Integer resp.stopping_condition.max_wait_time_in_seconds #=> Integer resp.creation_time #=> Time resp.training_start_time #=> Time resp.training_end_time #=> Time resp.last_modified_time #=> Time resp.secondary_status_transitions #=> Array resp.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.secondary_status_transitions[0].start_time #=> Time resp.secondary_status_transitions[0].end_time #=> Time resp.secondary_status_transitions[0].status_message #=> String resp.final_metric_data_list #=> Array resp.final_metric_data_list[0].metric_name #=> String resp.final_metric_data_list[0].value #=> Float resp.final_metric_data_list[0].timestamp #=> Time resp.enable_network_isolation #=> Boolean resp.enable_inter_container_traffic_encryption #=> Boolean resp.enable_managed_spot_training #=> Boolean resp.checkpoint_config.s3_uri #=> String resp.checkpoint_config.local_path #=> String resp.training_time_in_seconds #=> Integer resp.billable_time_in_seconds #=> Integer resp.debug_hook_config.local_path #=> String resp.debug_hook_config.s3_output_path #=> String resp.debug_hook_config.hook_parameters #=> Hash resp.debug_hook_config.hook_parameters["ConfigKey"] #=> String resp.debug_hook_config.collection_configurations #=> Array resp.debug_hook_config.collection_configurations[0].collection_name #=> String resp.debug_hook_config.collection_configurations[0].collection_parameters #=> Hash resp.debug_hook_config.collection_configurations[0].collection_parameters["ConfigKey"] #=> String resp.experiment_config.experiment_name #=> String resp.experiment_config.trial_name #=> String resp.experiment_config.trial_component_display_name #=> String resp.debug_rule_configurations #=> Array resp.debug_rule_configurations[0].rule_configuration_name #=> String resp.debug_rule_configurations[0].local_path #=> String resp.debug_rule_configurations[0].s3_output_path #=> String resp.debug_rule_configurations[0].rule_evaluator_image #=> String resp.debug_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.debug_rule_configurations[0].volume_size_in_gb #=> Integer resp.debug_rule_configurations[0].rule_parameters #=> Hash resp.debug_rule_configurations[0].rule_parameters["ConfigKey"] #=> String resp.tensor_board_output_config.local_path #=> String resp.tensor_board_output_config.s3_output_path #=> String resp.debug_rule_evaluation_statuses #=> Array resp.debug_rule_evaluation_statuses[0].rule_configuration_name #=> String resp.debug_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String resp.debug_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped" resp.debug_rule_evaluation_statuses[0].status_details #=> String resp.debug_rule_evaluation_statuses[0].last_modified_time #=> Time resp.profiler_config.s3_output_path #=> String resp.profiler_config.profiling_interval_in_milliseconds #=> Integer resp.profiler_config.profiling_parameters #=> Hash resp.profiler_config.profiling_parameters["ConfigKey"] #=> String resp.profiler_rule_configurations #=> Array resp.profiler_rule_configurations[0].rule_configuration_name #=> String resp.profiler_rule_configurations[0].local_path #=> String resp.profiler_rule_configurations[0].s3_output_path #=> String resp.profiler_rule_configurations[0].rule_evaluator_image #=> String resp.profiler_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.profiler_rule_configurations[0].volume_size_in_gb #=> Integer resp.profiler_rule_configurations[0].rule_parameters #=> Hash resp.profiler_rule_configurations[0].rule_parameters["ConfigKey"] #=> String resp.profiler_rule_evaluation_statuses #=> Array resp.profiler_rule_evaluation_statuses[0].rule_configuration_name #=> String resp.profiler_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String resp.profiler_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped" resp.profiler_rule_evaluation_statuses[0].status_details #=> String resp.profiler_rule_evaluation_statuses[0].last_modified_time #=> Time resp.profiling_status #=> String, one of "Enabled", "Disabled" resp.retry_strategy.maximum_retry_attempts #=> Integer resp.environment #=> Hash resp.environment["TrainingEnvironmentKey"] #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* training_job_completed_or_stopped
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeTrainingJob AWS API Documentation
@overload describe_training_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10401 def describe_training_job(params = {}, options = {}) req = build_request(:describe_training_job, params) req.send_request(options) end
Returns information about a transform job.
@option params [required, String] :transform_job_name
The name of the transform job that you want to view details of.
@return [Types::DescribeTransformJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeTransformJobResponse#transform_job_name #transform_job_name} => String * {Types::DescribeTransformJobResponse#transform_job_arn #transform_job_arn} => String * {Types::DescribeTransformJobResponse#transform_job_status #transform_job_status} => String * {Types::DescribeTransformJobResponse#failure_reason #failure_reason} => String * {Types::DescribeTransformJobResponse#model_name #model_name} => String * {Types::DescribeTransformJobResponse#max_concurrent_transforms #max_concurrent_transforms} => Integer * {Types::DescribeTransformJobResponse#model_client_config #model_client_config} => Types::ModelClientConfig * {Types::DescribeTransformJobResponse#max_payload_in_mb #max_payload_in_mb} => Integer * {Types::DescribeTransformJobResponse#batch_strategy #batch_strategy} => String * {Types::DescribeTransformJobResponse#environment #environment} => Hash<String,String> * {Types::DescribeTransformJobResponse#transform_input #transform_input} => Types::TransformInput * {Types::DescribeTransformJobResponse#transform_output #transform_output} => Types::TransformOutput * {Types::DescribeTransformJobResponse#transform_resources #transform_resources} => Types::TransformResources * {Types::DescribeTransformJobResponse#creation_time #creation_time} => Time * {Types::DescribeTransformJobResponse#transform_start_time #transform_start_time} => Time * {Types::DescribeTransformJobResponse#transform_end_time #transform_end_time} => Time * {Types::DescribeTransformJobResponse#labeling_job_arn #labeling_job_arn} => String * {Types::DescribeTransformJobResponse#auto_ml_job_arn #auto_ml_job_arn} => String * {Types::DescribeTransformJobResponse#data_processing #data_processing} => Types::DataProcessing * {Types::DescribeTransformJobResponse#experiment_config #experiment_config} => Types::ExperimentConfig
@example Request syntax with placeholder values
resp = client.describe_transform_job({ transform_job_name: "TransformJobName", # required })
@example Response structure
resp.transform_job_name #=> String resp.transform_job_arn #=> String resp.transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.failure_reason #=> String resp.model_name #=> String resp.max_concurrent_transforms #=> Integer resp.model_client_config.invocations_timeout_in_seconds #=> Integer resp.model_client_config.invocations_max_retries #=> Integer resp.max_payload_in_mb #=> Integer resp.batch_strategy #=> String, one of "MultiRecord", "SingleRecord" resp.environment #=> Hash resp.environment["TransformEnvironmentKey"] #=> String resp.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.transform_input.data_source.s3_data_source.s3_uri #=> String resp.transform_input.content_type #=> String resp.transform_input.compression_type #=> String, one of "None", "Gzip" resp.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord" resp.transform_output.s3_output_path #=> String resp.transform_output.accept #=> String resp.transform_output.assemble_with #=> String, one of "None", "Line" resp.transform_output.kms_key_id #=> String resp.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.transform_resources.instance_count #=> Integer resp.transform_resources.volume_kms_key_id #=> String resp.creation_time #=> Time resp.transform_start_time #=> Time resp.transform_end_time #=> Time resp.labeling_job_arn #=> String resp.auto_ml_job_arn #=> String resp.data_processing.input_filter #=> String resp.data_processing.output_filter #=> String resp.data_processing.join_source #=> String, one of "Input", "None" resp.experiment_config.experiment_name #=> String resp.experiment_config.trial_name #=> String resp.experiment_config.trial_component_display_name #=> String
The following waiters are defined for this operation (see {Client#wait_until} for detailed usage):
* transform_job_completed_or_stopped
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeTransformJob AWS API Documentation
@overload describe_transform_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10487 def describe_transform_job(params = {}, options = {}) req = build_request(:describe_transform_job, params) req.send_request(options) end
Provides a list of a trial's properties.
@option params [required, String] :trial_name
The name of the trial to describe.
@return [Types::DescribeTrialResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeTrialResponse#trial_name #trial_name} => String * {Types::DescribeTrialResponse#trial_arn #trial_arn} => String * {Types::DescribeTrialResponse#display_name #display_name} => String * {Types::DescribeTrialResponse#experiment_name #experiment_name} => String * {Types::DescribeTrialResponse#source #source} => Types::TrialSource * {Types::DescribeTrialResponse#creation_time #creation_time} => Time * {Types::DescribeTrialResponse#created_by #created_by} => Types::UserContext * {Types::DescribeTrialResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeTrialResponse#last_modified_by #last_modified_by} => Types::UserContext * {Types::DescribeTrialResponse#metadata_properties #metadata_properties} => Types::MetadataProperties
@example Request syntax with placeholder values
resp = client.describe_trial({ trial_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_name #=> String resp.trial_arn #=> String resp.display_name #=> String resp.experiment_name #=> String resp.source.source_arn #=> String resp.source.source_type #=> String resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String resp.metadata_properties.commit_id #=> String resp.metadata_properties.repository #=> String resp.metadata_properties.generated_by #=> String resp.metadata_properties.project_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeTrial AWS API Documentation
@overload describe_trial
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10541 def describe_trial(params = {}, options = {}) req = build_request(:describe_trial, params) req.send_request(options) end
Provides a list of a trials component's properties.
@option params [required, String] :trial_component_name
The name of the trial component to describe.
@return [Types::DescribeTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeTrialComponentResponse#trial_component_name #trial_component_name} => String * {Types::DescribeTrialComponentResponse#trial_component_arn #trial_component_arn} => String * {Types::DescribeTrialComponentResponse#display_name #display_name} => String * {Types::DescribeTrialComponentResponse#source #source} => Types::TrialComponentSource * {Types::DescribeTrialComponentResponse#status #status} => Types::TrialComponentStatus * {Types::DescribeTrialComponentResponse#start_time #start_time} => Time * {Types::DescribeTrialComponentResponse#end_time #end_time} => Time * {Types::DescribeTrialComponentResponse#creation_time #creation_time} => Time * {Types::DescribeTrialComponentResponse#created_by #created_by} => Types::UserContext * {Types::DescribeTrialComponentResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeTrialComponentResponse#last_modified_by #last_modified_by} => Types::UserContext * {Types::DescribeTrialComponentResponse#parameters #parameters} => Hash<String,Types::TrialComponentParameterValue> * {Types::DescribeTrialComponentResponse#input_artifacts #input_artifacts} => Hash<String,Types::TrialComponentArtifact> * {Types::DescribeTrialComponentResponse#output_artifacts #output_artifacts} => Hash<String,Types::TrialComponentArtifact> * {Types::DescribeTrialComponentResponse#metadata_properties #metadata_properties} => Types::MetadataProperties * {Types::DescribeTrialComponentResponse#metrics #metrics} => Array<Types::TrialComponentMetricSummary>
@example Request syntax with placeholder values
resp = client.describe_trial_component({ trial_component_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_component_name #=> String resp.trial_component_arn #=> String resp.display_name #=> String resp.source.source_arn #=> String resp.source.source_type #=> String resp.status.primary_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.status.message #=> String resp.start_time #=> Time resp.end_time #=> Time resp.creation_time #=> Time resp.created_by.user_profile_arn #=> String resp.created_by.user_profile_name #=> String resp.created_by.domain_id #=> String resp.last_modified_time #=> Time resp.last_modified_by.user_profile_arn #=> String resp.last_modified_by.user_profile_name #=> String resp.last_modified_by.domain_id #=> String resp.parameters #=> Hash resp.parameters["TrialComponentKey256"].string_value #=> String resp.parameters["TrialComponentKey256"].number_value #=> Float resp.input_artifacts #=> Hash resp.input_artifacts["TrialComponentKey64"].media_type #=> String resp.input_artifacts["TrialComponentKey64"].value #=> String resp.output_artifacts #=> Hash resp.output_artifacts["TrialComponentKey64"].media_type #=> String resp.output_artifacts["TrialComponentKey64"].value #=> String resp.metadata_properties.commit_id #=> String resp.metadata_properties.repository #=> String resp.metadata_properties.generated_by #=> String resp.metadata_properties.project_id #=> String resp.metrics #=> Array resp.metrics[0].metric_name #=> String resp.metrics[0].source_arn #=> String resp.metrics[0].time_stamp #=> Time resp.metrics[0].max #=> Float resp.metrics[0].min #=> Float resp.metrics[0].last #=> Float resp.metrics[0].count #=> Integer resp.metrics[0].avg #=> Float resp.metrics[0].std_dev #=> Float
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeTrialComponent AWS API Documentation
@overload describe_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10623 def describe_trial_component(params = {}, options = {}) req = build_request(:describe_trial_component, params) req.send_request(options) end
Describes a user profile. For more information, see `CreateUserProfile`.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name. This value is not case sensitive.
@return [Types::DescribeUserProfileResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeUserProfileResponse#domain_id #domain_id} => String * {Types::DescribeUserProfileResponse#user_profile_arn #user_profile_arn} => String * {Types::DescribeUserProfileResponse#user_profile_name #user_profile_name} => String * {Types::DescribeUserProfileResponse#home_efs_file_system_uid #home_efs_file_system_uid} => String * {Types::DescribeUserProfileResponse#status #status} => String * {Types::DescribeUserProfileResponse#last_modified_time #last_modified_time} => Time * {Types::DescribeUserProfileResponse#creation_time #creation_time} => Time * {Types::DescribeUserProfileResponse#failure_reason #failure_reason} => String * {Types::DescribeUserProfileResponse#single_sign_on_user_identifier #single_sign_on_user_identifier} => String * {Types::DescribeUserProfileResponse#single_sign_on_user_value #single_sign_on_user_value} => String * {Types::DescribeUserProfileResponse#user_settings #user_settings} => Types::UserSettings
@example Request syntax with placeholder values
resp = client.describe_user_profile({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required })
@example Response structure
resp.domain_id #=> String resp.user_profile_arn #=> String resp.user_profile_name #=> String resp.home_efs_file_system_uid #=> String resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed" resp.last_modified_time #=> Time resp.creation_time #=> Time resp.failure_reason #=> String resp.single_sign_on_user_identifier #=> String resp.single_sign_on_user_value #=> String resp.user_settings.execution_role #=> String resp.user_settings.security_groups #=> Array resp.user_settings.security_groups[0] #=> String resp.user_settings.sharing_settings.notebook_output_option #=> String, one of "Allowed", "Disabled" resp.user_settings.sharing_settings.s3_output_path #=> String resp.user_settings.sharing_settings.s3_kms_key_id #=> String resp.user_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.user_settings.jupyter_server_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.user_settings.jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.user_settings.jupyter_server_app_settings.default_resource_spec.lifecycle_config_arn #=> String resp.user_settings.jupyter_server_app_settings.lifecycle_config_arns #=> Array resp.user_settings.jupyter_server_app_settings.lifecycle_config_arns[0] #=> String resp.user_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.user_settings.kernel_gateway_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.user_settings.kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.user_settings.kernel_gateway_app_settings.default_resource_spec.lifecycle_config_arn #=> String resp.user_settings.kernel_gateway_app_settings.custom_images #=> Array resp.user_settings.kernel_gateway_app_settings.custom_images[0].image_name #=> String resp.user_settings.kernel_gateway_app_settings.custom_images[0].image_version_number #=> Integer resp.user_settings.kernel_gateway_app_settings.custom_images[0].app_image_config_name #=> String resp.user_settings.kernel_gateway_app_settings.lifecycle_config_arns #=> Array resp.user_settings.kernel_gateway_app_settings.lifecycle_config_arns[0] #=> String resp.user_settings.tensor_board_app_settings.default_resource_spec.sage_maker_image_arn #=> String resp.user_settings.tensor_board_app_settings.default_resource_spec.sage_maker_image_version_arn #=> String resp.user_settings.tensor_board_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.user_settings.tensor_board_app_settings.default_resource_spec.lifecycle_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeUserProfile AWS API Documentation
@overload describe_user_profile
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10701 def describe_user_profile(params = {}, options = {}) req = build_request(:describe_user_profile, params) req.send_request(options) end
Lists private workforce information, including workforce name, Amazon Resource
Name (ARN), and, if applicable, allowed IP address ranges ([CIDRs]). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
This operation applies only to private workforces.
[1]: docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html
@option params [required, String] :workforce_name
The name of the private workforce whose access you want to restrict. `WorkforceName` is automatically set to `default` when a workforce is created and cannot be modified.
@return [Types::DescribeWorkforceResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeWorkforceResponse#workforce #workforce} => Types::Workforce
@example Request syntax with placeholder values
resp = client.describe_workforce({ workforce_name: "WorkforceName", # required })
@example Response structure
resp.workforce.workforce_name #=> String resp.workforce.workforce_arn #=> String resp.workforce.last_updated_date #=> Time resp.workforce.source_ip_config.cidrs #=> Array resp.workforce.source_ip_config.cidrs[0] #=> String resp.workforce.sub_domain #=> String resp.workforce.cognito_config.user_pool #=> String resp.workforce.cognito_config.client_id #=> String resp.workforce.oidc_config.client_id #=> String resp.workforce.oidc_config.issuer #=> String resp.workforce.oidc_config.authorization_endpoint #=> String resp.workforce.oidc_config.token_endpoint #=> String resp.workforce.oidc_config.user_info_endpoint #=> String resp.workforce.oidc_config.logout_endpoint #=> String resp.workforce.oidc_config.jwks_uri #=> String resp.workforce.create_date #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeWorkforce AWS API Documentation
@overload describe_workforce
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10755 def describe_workforce(params = {}, options = {}) req = build_request(:describe_workforce, params) req.send_request(options) end
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource
Name (ARN).
@option params [required, String] :workteam_name
The name of the work team to return a description of.
@return [Types::DescribeWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DescribeWorkteamResponse#workteam #workteam} => Types::Workteam
@example Request syntax with placeholder values
resp = client.describe_workteam({ workteam_name: "WorkteamName", # required })
@example Response structure
resp.workteam.workteam_name #=> String resp.workteam.member_definitions #=> Array resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String resp.workteam.member_definitions[0].oidc_member_definition.groups #=> Array resp.workteam.member_definitions[0].oidc_member_definition.groups[0] #=> String resp.workteam.workteam_arn #=> String resp.workteam.workforce_arn #=> String resp.workteam.product_listing_ids #=> Array resp.workteam.product_listing_ids[0] #=> String resp.workteam.description #=> String resp.workteam.sub_domain #=> String resp.workteam.create_date #=> Time resp.workteam.last_updated_date #=> Time resp.workteam.notification_configuration.notification_topic_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeWorkteam AWS API Documentation
@overload describe_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10800 def describe_workteam(params = {}, options = {}) req = build_request(:describe_workteam, params) req.send_request(options) end
Disables using Service Catalog in SageMaker
. Service Catalog is used to create SageMaker
projects.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DisableSagemakerServicecatalogPortfolio AWS API Documentation
@overload disable_sagemaker_servicecatalog_portfolio
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10814 def disable_sagemaker_servicecatalog_portfolio(params = {}, options = {}) req = build_request(:disable_sagemaker_servicecatalog_portfolio, params) req.send_request(options) end
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify `ExperimentTrialComponent` for the `Resource` parameter. The list appears in the response under `Results.TrialComponent.Parents`.
@option params [required, String] :trial_component_name
The name of the component to disassociate from the trial.
@option params [required, String] :trial_name
The name of the trial to disassociate from.
@return [Types::DisassociateTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DisassociateTrialComponentResponse#trial_component_arn #trial_component_arn} => String * {Types::DisassociateTrialComponentResponse#trial_arn #trial_arn} => String
@example Request syntax with placeholder values
resp = client.disassociate_trial_component({ trial_component_name: "ExperimentEntityName", # required trial_name: "ExperimentEntityName", # required })
@example Response structure
resp.trial_component_arn #=> String resp.trial_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DisassociateTrialComponent AWS API Documentation
@overload disassociate_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10857 def disassociate_trial_component(params = {}, options = {}) req = build_request(:disassociate_trial_component, params) req.send_request(options) end
Enables using Service Catalog in SageMaker
. Service Catalog is used to create SageMaker
projects.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/EnableSagemakerServicecatalogPortfolio AWS API Documentation
@overload enable_sagemaker_servicecatalog_portfolio
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10871 def enable_sagemaker_servicecatalog_portfolio(params = {}, options = {}) req = build_request(:enable_sagemaker_servicecatalog_portfolio, params) req.send_request(options) end
Describes a fleet.
@option params [required, String] :device_fleet_name
The name of the fleet.
@return [Types::GetDeviceFleetReportResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetDeviceFleetReportResponse#device_fleet_arn #device_fleet_arn} => String * {Types::GetDeviceFleetReportResponse#device_fleet_name #device_fleet_name} => String * {Types::GetDeviceFleetReportResponse#output_config #output_config} => Types::EdgeOutputConfig * {Types::GetDeviceFleetReportResponse#description #description} => String * {Types::GetDeviceFleetReportResponse#report_generated #report_generated} => Time * {Types::GetDeviceFleetReportResponse#device_stats #device_stats} => Types::DeviceStats * {Types::GetDeviceFleetReportResponse#agent_versions #agent_versions} => Array<Types::AgentVersion> * {Types::GetDeviceFleetReportResponse#model_stats #model_stats} => Array<Types::EdgeModelStat>
@example Request syntax with placeholder values
resp = client.get_device_fleet_report({ device_fleet_name: "EntityName", # required })
@example Response structure
resp.device_fleet_arn #=> String resp.device_fleet_name #=> String resp.output_config.s3_output_location #=> String resp.output_config.kms_key_id #=> String resp.output_config.preset_deployment_type #=> String, one of "GreengrassV2Component" resp.output_config.preset_deployment_config #=> String resp.description #=> String resp.report_generated #=> Time resp.device_stats.connected_device_count #=> Integer resp.device_stats.registered_device_count #=> Integer resp.agent_versions #=> Array resp.agent_versions[0].version #=> String resp.agent_versions[0].agent_count #=> Integer resp.model_stats #=> Array resp.model_stats[0].model_name #=> String resp.model_stats[0].model_version #=> String resp.model_stats[0].offline_device_count #=> Integer resp.model_stats[0].connected_device_count #=> Integer resp.model_stats[0].active_device_count #=> Integer resp.model_stats[0].sampling_device_count #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/GetDeviceFleetReport AWS API Documentation
@overload get_device_fleet_report
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10925 def get_device_fleet_report(params = {}, options = {}) req = build_request(:get_device_fleet_report, params) req.send_request(options) end
Gets a resource policy that manages access for a model group. For information about resource policies, see [Identity-based policies and resource-based policies] in the *Amazon Web Services Identity and Access Management User Guide.*.
[1]: docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html
@option params [required, String] :model_package_group_name
The name of the model group for which to get the resource policy.
@return [Types::GetModelPackageGroupPolicyOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetModelPackageGroupPolicyOutput#resource_policy #resource_policy} => String
@example Request syntax with placeholder values
resp = client.get_model_package_group_policy({ model_package_group_name: "EntityName", # required })
@example Response structure
resp.resource_policy #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/GetModelPackageGroupPolicy AWS API Documentation
@overload get_model_package_group_policy
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10960 def get_model_package_group_policy(params = {}, options = {}) req = build_request(:get_model_package_group_policy, params) req.send_request(options) end
Gets the status of Service Catalog in SageMaker
. Service Catalog is used to create SageMaker
projects.
@return [Types::GetSagemakerServicecatalogPortfolioStatusOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetSagemakerServicecatalogPortfolioStatusOutput#status #status} => String
@example Response structure
resp.status #=> String, one of "Enabled", "Disabled"
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/GetSagemakerServicecatalogPortfolioStatus AWS API Documentation
@overload get_sagemaker_servicecatalog_portfolio_status
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 10980 def get_sagemaker_servicecatalog_portfolio_status(params = {}, options = {}) req = build_request(:get_sagemaker_servicecatalog_portfolio_status, params) req.send_request(options) end
An auto-complete API for the search functionality in the Amazon SageMaker
console. It returns suggestions of possible matches for the property name to use in `Search` queries. Provides suggestions for `HyperParameters`, `Tags`, and `Metrics`.
@option params [required, String] :resource
The name of the Amazon SageMaker resource to search for.
@option params [Types::SuggestionQuery] :suggestion_query
Limits the property names that are included in the response.
@return [Types::GetSearchSuggestionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetSearchSuggestionsResponse#property_name_suggestions #property_name_suggestions} => Array<Types::PropertyNameSuggestion>
@example Request syntax with placeholder values
resp = client.get_search_suggestions({ resource: "TrainingJob", # required, accepts TrainingJob, Experiment, ExperimentTrial, ExperimentTrialComponent, Endpoint, ModelPackage, ModelPackageGroup, Pipeline, PipelineExecution, FeatureGroup suggestion_query: { property_name_query: { property_name_hint: "PropertyNameHint", # required }, }, })
@example Response structure
resp.property_name_suggestions #=> Array resp.property_name_suggestions[0].property_name #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/GetSearchSuggestions AWS API Documentation
@overload get_search_suggestions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11020 def get_search_suggestions(params = {}, options = {}) req = build_request(:get_search_suggestions, params) req.send_request(options) end
Lists the actions in your account and their properties.
@option params [String] :source_uri
A filter that returns only actions with the specified source URI.
@option params [String] :action_type
A filter that returns only actions of the specified type.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only actions created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only actions created on or before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [String] :next_token
If the previous call to `ListActions` didn't return the full set of actions, the call returns a token for getting the next set of actions.
@option params [Integer] :max_results
The maximum number of actions to return in the response. The default value is 10.
@return [Types::ListActionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListActionsResponse#action_summaries #action_summaries} => Array<Types::ActionSummary> * {Types::ListActionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_actions({ source_uri: "SourceUri", action_type: "String256", created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.action_summaries #=> Array resp.action_summaries[0].action_arn #=> String resp.action_summaries[0].action_name #=> String resp.action_summaries[0].source.source_uri #=> String resp.action_summaries[0].source.source_type #=> String resp.action_summaries[0].source.source_id #=> String resp.action_summaries[0].action_type #=> String resp.action_summaries[0].status #=> String, one of "Unknown", "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.action_summaries[0].creation_time #=> Time resp.action_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListActions AWS API Documentation
@overload list_actions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11094 def list_actions(params = {}, options = {}) req = build_request(:list_actions, params) req.send_request(options) end
Lists the machine learning algorithms that have been created.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only algorithms created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only algorithms created before the specified time (timestamp).
@option params [Integer] :max_results
The maximum number of algorithms to return in the response.
@option params [String] :name_contains
A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
@option params [String] :next_token
If the response to a previous `ListAlgorithms` request was truncated, the response includes a `NextToken`. To retrieve the next set of algorithms, use the token in the next request.
@option params [String] :sort_by
The parameter by which to sort the results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for the results. The default is `Ascending`.
@return [Types::ListAlgorithmsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListAlgorithmsOutput#algorithm_summary_list #algorithm_summary_list} => Array<Types::AlgorithmSummary> * {Types::ListAlgorithmsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_algorithms({ creation_time_after: Time.now, creation_time_before: Time.now, max_results: 1, name_contains: "NameContains", next_token: "NextToken", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.algorithm_summary_list #=> Array resp.algorithm_summary_list[0].algorithm_name #=> String resp.algorithm_summary_list[0].algorithm_arn #=> String resp.algorithm_summary_list[0].algorithm_description #=> String resp.algorithm_summary_list[0].creation_time #=> Time resp.algorithm_summary_list[0].algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListAlgorithms AWS API Documentation
@overload list_algorithms
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11161 def list_algorithms(params = {}, options = {}) req = build_request(:list_algorithms, params) req.send_request(options) end
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
@option params [Integer] :max_results
The maximum number of AppImageConfigs to return in the response. The default value is 10.
@option params [String] :next_token
If the previous call to `ListImages` didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.
@option params [String] :name_contains
A filter that returns only AppImageConfigs whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only AppImageConfigs created on or before the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only AppImageConfigs created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :modified_time_before
A filter that returns only AppImageConfigs modified on or before the specified time.
@option params [Time,DateTime,Date,Integer,String] :modified_time_after
A filter that returns only AppImageConfigs modified on or after the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@return [Types::ListAppImageConfigsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListAppImageConfigsResponse#next_token #next_token} => String * {Types::ListAppImageConfigsResponse#app_image_configs #app_image_configs} => Array<Types::AppImageConfigDetails>
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_app_image_configs({ max_results: 1, next_token: "NextToken", name_contains: "AppImageConfigName", creation_time_before: Time.now, creation_time_after: Time.now, modified_time_before: Time.now, modified_time_after: Time.now, sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime, Name sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.next_token #=> String resp.app_image_configs #=> Array resp.app_image_configs[0].app_image_config_arn #=> String resp.app_image_configs[0].app_image_config_name #=> String resp.app_image_configs[0].creation_time #=> Time resp.app_image_configs[0].last_modified_time #=> Time resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs #=> Array resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs[0].name #=> String resp.app_image_configs[0].kernel_gateway_image_config.kernel_specs[0].display_name #=> String resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.mount_path #=> String resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.default_uid #=> Integer resp.app_image_configs[0].kernel_gateway_image_config.file_system_config.default_gid #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListAppImageConfigs AWS API Documentation
@overload list_app_image_configs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11246 def list_app_image_configs(params = {}, options = {}) req = build_request(:list_app_image_configs, params) req.send_request(options) end
Lists apps.
@option params [String] :next_token
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
@option params [Integer] :max_results
Returns a list up to a specified limit.
@option params [String] :sort_order
The sort order for the results. The default is Ascending.
@option params [String] :sort_by
The parameter by which to sort the results. The default is CreationTime.
@option params [String] :domain_id_equals
A parameter to search for the domain ID.
@option params [String] :user_profile_name_equals
A parameter to search by user profile name.
@return [Types::ListAppsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListAppsResponse#apps #apps} => Array<Types::AppDetails> * {Types::ListAppsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_apps({ next_token: "NextToken", max_results: 1, sort_order: "Ascending", # accepts Ascending, Descending sort_by: "CreationTime", # accepts CreationTime domain_id_equals: "DomainId", user_profile_name_equals: "UserProfileName", })
@example Response structure
resp.apps #=> Array resp.apps[0].domain_id #=> String resp.apps[0].user_profile_name #=> String resp.apps[0].app_type #=> String, one of "JupyterServer", "KernelGateway", "TensorBoard" resp.apps[0].app_name #=> String resp.apps[0].status #=> String, one of "Deleted", "Deleting", "Failed", "InService", "Pending" resp.apps[0].creation_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListApps AWS API Documentation
@overload list_apps
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11306 def list_apps(params = {}, options = {}) req = build_request(:list_apps, params) req.send_request(options) end
Lists the artifacts in your account and their properties.
@option params [String] :source_uri
A filter that returns only artifacts with the specified source URI.
@option params [String] :artifact_type
A filter that returns only artifacts of the specified type.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only artifacts created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only artifacts created on or before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [String] :next_token
If the previous call to `ListArtifacts` didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.
@option params [Integer] :max_results
The maximum number of artifacts to return in the response. The default value is 10.
@return [Types::ListArtifactsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListArtifactsResponse#artifact_summaries #artifact_summaries} => Array<Types::ArtifactSummary> * {Types::ListArtifactsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_artifacts({ source_uri: "SourceUri", artifact_type: "String256", created_after: Time.now, created_before: Time.now, sort_by: "CreationTime", # accepts CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.artifact_summaries #=> Array resp.artifact_summaries[0].artifact_arn #=> String resp.artifact_summaries[0].artifact_name #=> String resp.artifact_summaries[0].source.source_uri #=> String resp.artifact_summaries[0].source.source_types #=> Array resp.artifact_summaries[0].source.source_types[0].source_id_type #=> String, one of "MD5Hash", "S3ETag", "S3Version", "Custom" resp.artifact_summaries[0].source.source_types[0].value #=> String resp.artifact_summaries[0].artifact_type #=> String resp.artifact_summaries[0].creation_time #=> Time resp.artifact_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListArtifacts AWS API Documentation
@overload list_artifacts
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11381 def list_artifacts(params = {}, options = {}) req = build_request(:list_artifacts, params) req.send_request(options) end
Lists the associations in your account and their properties.
@option params [String] :source_arn
A filter that returns only associations with the specified source ARN.
@option params [String] :destination_arn
A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
@option params [String] :source_type
A filter that returns only associations with the specified source type.
@option params [String] :destination_type
A filter that returns only associations with the specified destination type.
@option params [String] :association_type
A filter that returns only associations of the specified type.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only associations created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only associations created on or before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [String] :next_token
If the previous call to `ListAssociations` didn't return the full set of associations, the call returns a token for getting the next set of associations.
@option params [Integer] :max_results
The maximum number of associations to return in the response. The default value is 10.
@return [Types::ListAssociationsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListAssociationsResponse#association_summaries #association_summaries} => Array<Types::AssociationSummary> * {Types::ListAssociationsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_associations({ source_arn: "AssociationEntityArn", destination_arn: "AssociationEntityArn", source_type: "String256", destination_type: "String256", association_type: "ContributedTo", # accepts ContributedTo, AssociatedWith, DerivedFrom, Produced created_after: Time.now, created_before: Time.now, sort_by: "SourceArn", # accepts SourceArn, DestinationArn, SourceType, DestinationType, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.association_summaries #=> Array resp.association_summaries[0].source_arn #=> String resp.association_summaries[0].destination_arn #=> String resp.association_summaries[0].source_type #=> String resp.association_summaries[0].destination_type #=> String resp.association_summaries[0].association_type #=> String, one of "ContributedTo", "AssociatedWith", "DerivedFrom", "Produced" resp.association_summaries[0].source_name #=> String resp.association_summaries[0].destination_name #=> String resp.association_summaries[0].creation_time #=> Time resp.association_summaries[0].created_by.user_profile_arn #=> String resp.association_summaries[0].created_by.user_profile_name #=> String resp.association_summaries[0].created_by.domain_id #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListAssociations AWS API Documentation
@overload list_associations
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11473 def list_associations(params = {}, options = {}) req = build_request(:list_associations, params) req.send_request(options) end
Request a list of jobs.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
Request a list of jobs, using a filter for time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
Request a list of jobs, using a filter for time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
Request a list of jobs, using a filter for time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
Request a list of jobs, using a filter for time.
@option params [String] :name_contains
Request a list of jobs, using a search filter for name.
@option params [String] :status_equals
Request a list of jobs, using a filter for status.
@option params [String] :sort_order
The sort order for the results. The default is `Descending`.
@option params [String] :sort_by
The parameter by which to sort the results. The default is `Name`.
@option params [Integer] :max_results
Request a list of jobs up to a specified limit.
@option params [String] :next_token
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
@return [Types::ListAutoMLJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListAutoMLJobsResponse#auto_ml_job_summaries #auto_ml_job_summaries} => Array<Types::AutoMLJobSummary> * {Types::ListAutoMLJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_auto_ml_jobs({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "AutoMLNameContains", status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping sort_order: "Ascending", # accepts Ascending, Descending sort_by: "Name", # accepts Name, CreationTime, Status max_results: 1, next_token: "NextToken", })
@example Response structure
resp.auto_ml_job_summaries #=> Array resp.auto_ml_job_summaries[0].auto_ml_job_name #=> String resp.auto_ml_job_summaries[0].auto_ml_job_arn #=> String resp.auto_ml_job_summaries[0].auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.auto_ml_job_summaries[0].auto_ml_job_secondary_status #=> String, one of "Starting", "AnalyzingData", "FeatureEngineering", "ModelTuning", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated", "GeneratingExplainabilityReport", "Completed", "ExplainabilityError", "DeployingModel", "ModelDeploymentError" resp.auto_ml_job_summaries[0].creation_time #=> Time resp.auto_ml_job_summaries[0].end_time #=> Time resp.auto_ml_job_summaries[0].last_modified_time #=> Time resp.auto_ml_job_summaries[0].failure_reason #=> String resp.auto_ml_job_summaries[0].partial_failure_reasons #=> Array resp.auto_ml_job_summaries[0].partial_failure_reasons[0].partial_failure_message #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListAutoMLJobs AWS API Documentation
@overload list_auto_ml_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11552 def list_auto_ml_jobs(params = {}, options = {}) req = build_request(:list_auto_ml_jobs, params) req.send_request(options) end
List the candidates created for the job.
@option params [required, String] :auto_ml_job_name
List the candidates created for the job by providing the job's name.
@option params [String] :status_equals
List the candidates for the job and filter by status.
@option params [String] :candidate_name_equals
List the candidates for the job and filter by candidate name.
@option params [String] :sort_order
The sort order for the results. The default is `Ascending`.
@option params [String] :sort_by
The parameter by which to sort the results. The default is `Descending`.
@option params [Integer] :max_results
List the job's candidates up to a specified limit.
@option params [String] :next_token
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
@return [Types::ListCandidatesForAutoMLJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListCandidatesForAutoMLJobResponse#candidates #candidates} => Array<Types::AutoMLCandidate> * {Types::ListCandidatesForAutoMLJobResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_candidates_for_auto_ml_job({ auto_ml_job_name: "AutoMLJobName", # required status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping candidate_name_equals: "CandidateName", sort_order: "Ascending", # accepts Ascending, Descending sort_by: "CreationTime", # accepts CreationTime, Status, FinalObjectiveMetricValue max_results: 1, next_token: "NextToken", })
@example Response structure
resp.candidates #=> Array resp.candidates[0].candidate_name #=> String resp.candidates[0].final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize" resp.candidates[0].final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.candidates[0].final_auto_ml_job_objective_metric.value #=> Float resp.candidates[0].objective_status #=> String, one of "Succeeded", "Pending", "Failed" resp.candidates[0].candidate_steps #=> Array resp.candidates[0].candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob" resp.candidates[0].candidate_steps[0].candidate_step_arn #=> String resp.candidates[0].candidate_steps[0].candidate_step_name #=> String resp.candidates[0].candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.candidates[0].inference_containers #=> Array resp.candidates[0].inference_containers[0].image #=> String resp.candidates[0].inference_containers[0].model_data_url #=> String resp.candidates[0].inference_containers[0].environment #=> Hash resp.candidates[0].inference_containers[0].environment["EnvironmentKey"] #=> String resp.candidates[0].creation_time #=> Time resp.candidates[0].end_time #=> Time resp.candidates[0].last_modified_time #=> Time resp.candidates[0].failure_reason #=> String resp.candidates[0].candidate_properties.candidate_artifact_locations.explainability #=> String resp.candidates[0].candidate_properties.candidate_metrics #=> Array resp.candidates[0].candidate_properties.candidate_metrics[0].metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro", "AUC" resp.candidates[0].candidate_properties.candidate_metrics[0].value #=> Float resp.candidates[0].candidate_properties.candidate_metrics[0].set #=> String, one of "Train", "Validation", "Test" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListCandidatesForAutoMLJob AWS API Documentation
@overload list_candidates_for_auto_ml_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11634 def list_candidates_for_auto_ml_job(params = {}, options = {}) req = build_request(:list_candidates_for_auto_ml_job, params) req.send_request(options) end
Gets a list of the Git repositories in your account.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only Git repositories that were created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only Git repositories that were created before the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only Git repositories that were last modified after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only Git repositories that were last modified before the specified time.
@option params [Integer] :max_results
The maximum number of Git repositories to return in the response.
@option params [String] :name_contains
A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
@option params [String] :next_token
If the result of a `ListCodeRepositoriesOutput` request was truncated, the response includes a `NextToken`. To get the next set of Git repositories, use the token in the next request.
@option params [String] :sort_by
The field to sort results by. The default is `Name`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListCodeRepositoriesOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListCodeRepositoriesOutput#code_repository_summary_list #code_repository_summary_list} => Array<Types::CodeRepositorySummary> * {Types::ListCodeRepositoriesOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_code_repositories({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, max_results: 1, name_contains: "CodeRepositoryNameContains", next_token: "NextToken", sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.code_repository_summary_list #=> Array resp.code_repository_summary_list[0].code_repository_name #=> String resp.code_repository_summary_list[0].code_repository_arn #=> String resp.code_repository_summary_list[0].creation_time #=> Time resp.code_repository_summary_list[0].last_modified_time #=> Time resp.code_repository_summary_list[0].git_config.repository_url #=> String resp.code_repository_summary_list[0].git_config.branch #=> String resp.code_repository_summary_list[0].git_config.secret_arn #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListCodeRepositories AWS API Documentation
@overload list_code_repositories
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11712 def list_code_repositories(params = {}, options = {}) req = build_request(:list_code_repositories, params) req.send_request(options) end
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
@option params [String] :next_token
If the result of the previous `ListCompilationJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of model compilation jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of model compilation jobs to return in the response.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns the model compilation jobs that were created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns the model compilation jobs that were created before a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns the model compilation jobs that were modified after a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns the model compilation jobs that were modified before a specified time.
@option params [String] :name_contains
A filter that returns the model compilation jobs whose name contains a specified string.
@option params [String] :status_equals
A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
@option params [String] :sort_by
The field by which to sort results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListCompilationJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListCompilationJobsResponse#compilation_job_summaries #compilation_job_summaries} => Array<Types::CompilationJobSummary> * {Types::ListCompilationJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_compilation_jobs({ next_token: "NextToken", max_results: 1, creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "NameContains", status_equals: "INPROGRESS", # accepts INPROGRESS, COMPLETED, FAILED, STARTING, STOPPING, STOPPED sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.compilation_job_summaries #=> Array resp.compilation_job_summaries[0].compilation_job_name #=> String resp.compilation_job_summaries[0].compilation_job_arn #=> String resp.compilation_job_summaries[0].creation_time #=> Time resp.compilation_job_summaries[0].compilation_start_time #=> Time resp.compilation_job_summaries[0].compilation_end_time #=> Time resp.compilation_job_summaries[0].compilation_target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_c4", "ml_c5", "ml_p2", "ml_p3", "ml_g4dn", "ml_inf1", "ml_eia2", "jetson_tx1", "jetson_tx2", "jetson_nano", "jetson_xavier", "rasp3b", "imx8qm", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603", "sitara_am57x", "amba_cv22", "amba_cv25", "x86_win32", "x86_win64", "coreml", "jacinto_tda4vm", "imx8mplus" resp.compilation_job_summaries[0].compilation_target_platform_os #=> String, one of "ANDROID", "LINUX" resp.compilation_job_summaries[0].compilation_target_platform_arch #=> String, one of "X86_64", "X86", "ARM64", "ARM_EABI", "ARM_EABIHF" resp.compilation_job_summaries[0].compilation_target_platform_accelerator #=> String, one of "INTEL_GRAPHICS", "MALI", "NVIDIA" resp.compilation_job_summaries[0].last_modified_time #=> Time resp.compilation_job_summaries[0].compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListCompilationJobs AWS API Documentation
@overload list_compilation_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11804 def list_compilation_jobs(params = {}, options = {}) req = build_request(:list_compilation_jobs, params) req.send_request(options) end
Lists the contexts in your account and their properties.
@option params [String] :source_uri
A filter that returns only contexts with the specified source URI.
@option params [String] :context_type
A filter that returns only contexts of the specified type.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only contexts created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only contexts created on or before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [String] :next_token
If the previous call to `ListContexts` didn't return the full set of contexts, the call returns a token for getting the next set of contexts.
@option params [Integer] :max_results
The maximum number of contexts to return in the response. The default value is 10.
@return [Types::ListContextsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListContextsResponse#context_summaries #context_summaries} => Array<Types::ContextSummary> * {Types::ListContextsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_contexts({ source_uri: "SourceUri", context_type: "String256", created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.context_summaries #=> Array resp.context_summaries[0].context_arn #=> String resp.context_summaries[0].context_name #=> String resp.context_summaries[0].source.source_uri #=> String resp.context_summaries[0].source.source_type #=> String resp.context_summaries[0].source.source_id #=> String resp.context_summaries[0].context_type #=> String resp.context_summaries[0].creation_time #=> Time resp.context_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListContexts AWS API Documentation
@overload list_contexts
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11878 def list_contexts(params = {}, options = {}) req = build_request(:list_contexts, params) req.send_request(options) end
Lists the data quality job definitions in your account.
@option params [String] :endpoint_name
A filter that lists the data quality job definitions associated with the specified endpoint.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the result of the previous `ListDataQualityJobDefinitions` request was truncated, the response includes a `NextToken`. To retrieve the next set of transform jobs, use the token in the next request.>
@option params [Integer] :max_results
The maximum number of data quality monitoring job definitions to return in the response.
@option params [String] :name_contains
A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only data quality monitoring job definitions created before the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only data quality monitoring job definitions created after the specified time.
@return [Types::ListDataQualityJobDefinitionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListDataQualityJobDefinitionsResponse#job_definition_summaries #job_definition_summaries} => Array<Types::MonitoringJobDefinitionSummary> * {Types::ListDataQualityJobDefinitionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_data_quality_job_definitions({ endpoint_name: "EndpointName", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, name_contains: "NameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.job_definition_summaries #=> Array resp.job_definition_summaries[0].monitoring_job_definition_name #=> String resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String resp.job_definition_summaries[0].creation_time #=> Time resp.job_definition_summaries[0].endpoint_name #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListDataQualityJobDefinitions AWS API Documentation
@overload list_data_quality_job_definitions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 11950 def list_data_quality_job_definitions(params = {}, options = {}) req = build_request(:list_data_quality_job_definitions, params) req.send_request(options) end
Returns a list of devices in the fleet.
@option params [String] :next_token
The response from the last list when returning a list large enough to need tokening.
@option params [Integer] :max_results
The maximum number of results to select.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
Filter fleets where packaging job was created after specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
Filter fleets where the edge packaging job was created before specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
Select fleets where the job was updated after X
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
Select fleets where the job was updated before X
@option params [String] :name_contains
Filter for fleets containing this name in their fleet device name.
@option params [String] :sort_by
The column to sort by.
@option params [String] :sort_order
What direction to sort in.
@return [Types::ListDeviceFleetsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListDeviceFleetsResponse#device_fleet_summaries #device_fleet_summaries} => Array<Types::DeviceFleetSummary> * {Types::ListDeviceFleetsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_device_fleets({ next_token: "NextToken", max_results: 1, creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "NameContains", sort_by: "NAME", # accepts NAME, CREATION_TIME, LAST_MODIFIED_TIME sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.device_fleet_summaries #=> Array resp.device_fleet_summaries[0].device_fleet_arn #=> String resp.device_fleet_summaries[0].device_fleet_name #=> String resp.device_fleet_summaries[0].creation_time #=> Time resp.device_fleet_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListDeviceFleets AWS API Documentation
@overload list_device_fleets
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12020 def list_device_fleets(params = {}, options = {}) req = build_request(:list_device_fleets, params) req.send_request(options) end
A list of devices.
@option params [String] :next_token
The response from the last list when returning a list large enough to need tokening.
@option params [Integer] :max_results
Maximum number of results to select.
@option params [Time,DateTime,Date,Integer,String] :latest_heartbeat_after
Select fleets where the job was updated after X
@option params [String] :model_name
A filter that searches devices that contains this name in any of their models.
@option params [String] :device_fleet_name
Filter for fleets containing this name in their device fleet name.
@return [Types::ListDevicesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListDevicesResponse#device_summaries #device_summaries} => Array<Types::DeviceSummary> * {Types::ListDevicesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_devices({ next_token: "NextToken", max_results: 1, latest_heartbeat_after: Time.now, model_name: "EntityName", device_fleet_name: "EntityName", })
@example Response structure
resp.device_summaries #=> Array resp.device_summaries[0].device_name #=> String resp.device_summaries[0].device_arn #=> String resp.device_summaries[0].description #=> String resp.device_summaries[0].device_fleet_name #=> String resp.device_summaries[0].iot_thing_name #=> String resp.device_summaries[0].registration_time #=> Time resp.device_summaries[0].latest_heartbeat #=> Time resp.device_summaries[0].models #=> Array resp.device_summaries[0].models[0].model_name #=> String resp.device_summaries[0].models[0].model_version #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListDevices AWS API Documentation
@overload list_devices
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12080 def list_devices(params = {}, options = {}) req = build_request(:list_devices, params) req.send_request(options) end
Lists the domains.
@option params [String] :next_token
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
@option params [Integer] :max_results
Returns a list up to a specified limit.
@return [Types::ListDomainsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListDomainsResponse#domains #domains} => Array<Types::DomainDetails> * {Types::ListDomainsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_domains({ next_token: "NextToken", max_results: 1, })
@example Response structure
resp.domains #=> Array resp.domains[0].domain_arn #=> String resp.domains[0].domain_id #=> String resp.domains[0].domain_name #=> String resp.domains[0].status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed" resp.domains[0].creation_time #=> Time resp.domains[0].last_modified_time #=> Time resp.domains[0].url #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListDomains AWS API Documentation
@overload list_domains
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12124 def list_domains(params = {}, options = {}) req = build_request(:list_domains, params) req.send_request(options) end
Returns a list of edge packaging jobs.
@option params [String] :next_token
The response from the last list when returning a list large enough to need tokening.
@option params [Integer] :max_results
Maximum number of results to select.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
Select jobs where the job was created after specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
Select jobs where the job was created before specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
Select jobs where the job was updated after specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
Select jobs where the job was updated before specified time.
@option params [String] :name_contains
Filter for jobs containing this name in their packaging job name.
@option params [String] :model_name_contains
Filter for jobs where the model name contains this string.
@option params [String] :status_equals
The job status to filter for.
@option params [String] :sort_by
Use to specify what column to sort by.
@option params [String] :sort_order
What direction to sort by.
@return [Types::ListEdgePackagingJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListEdgePackagingJobsResponse#edge_packaging_job_summaries #edge_packaging_job_summaries} => Array<Types::EdgePackagingJobSummary> * {Types::ListEdgePackagingJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_edge_packaging_jobs({ next_token: "NextToken", max_results: 1, creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "NameContains", model_name_contains: "NameContains", status_equals: "STARTING", # accepts STARTING, INPROGRESS, COMPLETED, FAILED, STOPPING, STOPPED sort_by: "NAME", # accepts NAME, MODEL_NAME, CREATION_TIME, LAST_MODIFIED_TIME, STATUS sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.edge_packaging_job_summaries #=> Array resp.edge_packaging_job_summaries[0].edge_packaging_job_arn #=> String resp.edge_packaging_job_summaries[0].edge_packaging_job_name #=> String resp.edge_packaging_job_summaries[0].edge_packaging_job_status #=> String, one of "STARTING", "INPROGRESS", "COMPLETED", "FAILED", "STOPPING", "STOPPED" resp.edge_packaging_job_summaries[0].compilation_job_name #=> String resp.edge_packaging_job_summaries[0].model_name #=> String resp.edge_packaging_job_summaries[0].model_version #=> String resp.edge_packaging_job_summaries[0].creation_time #=> Time resp.edge_packaging_job_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListEdgePackagingJobs AWS API Documentation
@overload list_edge_packaging_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12205 def list_edge_packaging_jobs(params = {}, options = {}) req = build_request(:list_edge_packaging_jobs, params) req.send_request(options) end
Lists endpoint configurations.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the result of the previous `ListEndpointConfig` request was truncated, the response includes a `NextToken`. To retrieve the next set of endpoint configurations, use the token in the next request.
@option params [Integer] :max_results
The maximum number of training jobs to return in the response.
@option params [String] :name_contains
A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only endpoint configurations created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
@return [Types::ListEndpointConfigsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListEndpointConfigsOutput#endpoint_configs #endpoint_configs} => Array<Types::EndpointConfigSummary> * {Types::ListEndpointConfigsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_endpoint_configs({ sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "PaginationToken", max_results: 1, name_contains: "EndpointConfigNameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.endpoint_configs #=> Array resp.endpoint_configs[0].endpoint_config_name #=> String resp.endpoint_configs[0].endpoint_config_arn #=> String resp.endpoint_configs[0].creation_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListEndpointConfigs AWS API Documentation
@overload list_endpoint_configs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12269 def list_endpoint_configs(params = {}, options = {}) req = build_request(:list_endpoint_configs, params) req.send_request(options) end
Lists endpoints.
@option params [String] :sort_by
Sorts the list of results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the result of a `ListEndpoints` request was truncated, the response includes a `NextToken`. To retrieve the next set of endpoints, use the token in the next request.
@option params [Integer] :max_results
The maximum number of endpoints to return in the response. This value defaults to 10.
@option params [String] :name_contains
A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only endpoints that were created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only endpoints that were modified before the specified timestamp.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only endpoints that were modified after the specified timestamp.
@option params [String] :status_equals
A filter that returns only endpoints with the specified status.
@return [Types::ListEndpointsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListEndpointsOutput#endpoints #endpoints} => Array<Types::EndpointSummary> * {Types::ListEndpointsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_endpoints({ sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending next_token: "PaginationToken", max_results: 1, name_contains: "EndpointNameContains", creation_time_before: Time.now, creation_time_after: Time.now, last_modified_time_before: Time.now, last_modified_time_after: Time.now, status_equals: "OutOfService", # accepts OutOfService, Creating, Updating, SystemUpdating, RollingBack, InService, Deleting, Failed })
@example Response structure
resp.endpoints #=> Array resp.endpoints[0].endpoint_name #=> String resp.endpoints[0].endpoint_arn #=> String resp.endpoints[0].creation_time #=> Time resp.endpoints[0].last_modified_time #=> Time resp.endpoints[0].endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListEndpoints AWS API Documentation
@overload list_endpoints
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12350 def list_endpoints(params = {}, options = {}) req = build_request(:list_endpoints, params) req.send_request(options) end
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only experiments created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only experiments created before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [String] :next_token
If the previous call to `ListExperiments` didn't return the full set of experiments, the call returns a token for getting the next set of experiments.
@option params [Integer] :max_results
The maximum number of experiments to return in the response. The default value is 10.
@return [Types::ListExperimentsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListExperimentsResponse#experiment_summaries #experiment_summaries} => Array<Types::ExperimentSummary> * {Types::ListExperimentsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_experiments({ created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.experiment_summaries #=> Array resp.experiment_summaries[0].experiment_arn #=> String resp.experiment_summaries[0].experiment_name #=> String resp.experiment_summaries[0].display_name #=> String resp.experiment_summaries[0].experiment_source.source_arn #=> String resp.experiment_summaries[0].experiment_source.source_type #=> String resp.experiment_summaries[0].creation_time #=> Time resp.experiment_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListExperiments AWS API Documentation
@overload list_experiments
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12417 def list_experiments(params = {}, options = {}) req = build_request(:list_experiments, params) req.send_request(options) end
List `FeatureGroup`s based on given filter and order.
@option params [String] :name_contains
A string that partially matches one or more `FeatureGroup`s names. Filters `FeatureGroup`s by name.
@option params [String] :feature_group_status_equals
A `FeatureGroup` status. Filters by `FeatureGroup` status.
@option params [String] :offline_store_status_equals
An `OfflineStore` status. Filters by `OfflineStore` status.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
Use this parameter to search for `FeatureGroups`s created after a specific date and time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
Use this parameter to search for `FeatureGroups`s created before a specific date and time.
@option params [String] :sort_order
The order in which feature groups are listed.
@option params [String] :sort_by
The value on which the feature group list is sorted.
@option params [Integer] :max_results
The maximum number of results returned by `ListFeatureGroups`.
@option params [String] :next_token
A token to resume pagination of `ListFeatureGroups` results.
@return [Types::ListFeatureGroupsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListFeatureGroupsResponse#feature_group_summaries #feature_group_summaries} => Array<Types::FeatureGroupSummary> * {Types::ListFeatureGroupsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_feature_groups({ name_contains: "FeatureGroupNameContains", feature_group_status_equals: "Creating", # accepts Creating, Created, CreateFailed, Deleting, DeleteFailed offline_store_status_equals: "Active", # accepts Active, Blocked, Disabled creation_time_after: Time.now, creation_time_before: Time.now, sort_order: "Ascending", # accepts Ascending, Descending sort_by: "Name", # accepts Name, FeatureGroupStatus, OfflineStoreStatus, CreationTime max_results: 1, next_token: "NextToken", })
@example Response structure
resp.feature_group_summaries #=> Array resp.feature_group_summaries[0].feature_group_name #=> String resp.feature_group_summaries[0].feature_group_arn #=> String resp.feature_group_summaries[0].creation_time #=> Time resp.feature_group_summaries[0].feature_group_status #=> String, one of "Creating", "Created", "CreateFailed", "Deleting", "DeleteFailed" resp.feature_group_summaries[0].offline_store_status.status #=> String, one of "Active", "Blocked", "Disabled" resp.feature_group_summaries[0].offline_store_status.blocked_reason #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListFeatureGroups AWS API Documentation
@overload list_feature_groups
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12490 def list_feature_groups(params = {}, options = {}) req = build_request(:list_feature_groups, params) req.send_request(options) end
Returns information about the flow definitions in your account.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only flow definitions that were created before the specified timestamp.
@option params [String] :sort_order
An optional value that specifies whether you want the results sorted in `Ascending` or `Descending` order.
@option params [String] :next_token
A token to resume pagination.
@option params [Integer] :max_results
The total number of items to return. If the total number of available items is more than the value specified in `MaxResults`, then a `NextToken` will be provided in the output that you can use to resume pagination.
@return [Types::ListFlowDefinitionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListFlowDefinitionsResponse#flow_definition_summaries #flow_definition_summaries} => Array<Types::FlowDefinitionSummary> * {Types::ListFlowDefinitionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_flow_definitions({ creation_time_after: Time.now, creation_time_before: Time.now, sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.flow_definition_summaries #=> Array resp.flow_definition_summaries[0].flow_definition_name #=> String resp.flow_definition_summaries[0].flow_definition_arn #=> String resp.flow_definition_summaries[0].flow_definition_status #=> String, one of "Initializing", "Active", "Failed", "Deleting" resp.flow_definition_summaries[0].creation_time #=> Time resp.flow_definition_summaries[0].failure_reason #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListFlowDefinitions AWS API Documentation
@overload list_flow_definitions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12549 def list_flow_definitions(params = {}, options = {}) req = build_request(:list_flow_definitions, params) req.send_request(options) end
Returns information about the human task user interfaces in your account.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only human task user interfaces that were created before the specified timestamp.
@option params [String] :sort_order
An optional value that specifies whether you want the results sorted in `Ascending` or `Descending` order.
@option params [String] :next_token
A token to resume pagination.
@option params [Integer] :max_results
The total number of items to return. If the total number of available items is more than the value specified in `MaxResults`, then a `NextToken` will be provided in the output that you can use to resume pagination.
@return [Types::ListHumanTaskUisResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListHumanTaskUisResponse#human_task_ui_summaries #human_task_ui_summaries} => Array<Types::HumanTaskUiSummary> * {Types::ListHumanTaskUisResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_human_task_uis({ creation_time_after: Time.now, creation_time_before: Time.now, sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.human_task_ui_summaries #=> Array resp.human_task_ui_summaries[0].human_task_ui_name #=> String resp.human_task_ui_summaries[0].human_task_ui_arn #=> String resp.human_task_ui_summaries[0].creation_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListHumanTaskUis AWS API Documentation
@overload list_human_task_uis
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12607 def list_human_task_uis(params = {}, options = {}) req = build_request(:list_human_task_uis, params) req.send_request(options) end
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
@option params [String] :next_token
If the result of the previous `ListHyperParameterTuningJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of tuning jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of tuning jobs to return. The default value is 10.
@option params [String] :sort_by
The field to sort results by. The default is `Name`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@option params [String] :name_contains
A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only tuning jobs that were created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only tuning jobs that were created before the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only tuning jobs that were modified after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only tuning jobs that were modified before the specified time.
@option params [String] :status_equals
A filter that returns only tuning jobs with the specified status.
@return [Types::ListHyperParameterTuningJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListHyperParameterTuningJobsResponse#hyper_parameter_tuning_job_summaries #hyper_parameter_tuning_job_summaries} => Array<Types::HyperParameterTuningJobSummary> * {Types::ListHyperParameterTuningJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_hyper_parameter_tuning_jobs({ next_token: "NextToken", max_results: 1, sort_by: "Name", # accepts Name, Status, CreationTime sort_order: "Ascending", # accepts Ascending, Descending name_contains: "NameContains", creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping })
@example Response structure
resp.hyper_parameter_tuning_job_summaries #=> Array resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_name #=> String resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_arn #=> String resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping" resp.hyper_parameter_tuning_job_summaries[0].strategy #=> String, one of "Bayesian", "Random" resp.hyper_parameter_tuning_job_summaries[0].creation_time #=> Time resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_end_time #=> Time resp.hyper_parameter_tuning_job_summaries[0].last_modified_time #=> Time resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.completed #=> Integer resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.in_progress #=> Integer resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.retryable_error #=> Integer resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.non_retryable_error #=> Integer resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.stopped #=> Integer resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.succeeded #=> Integer resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.pending #=> Integer resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.failed #=> Integer resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_number_of_training_jobs #=> Integer resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_parallel_training_jobs #=> Integer resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListHyperParameterTuningJobs AWS API Documentation
@overload list_hyper_parameter_tuning_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12700 def list_hyper_parameter_tuning_jobs(params = {}, options = {}) req = build_request(:list_hyper_parameter_tuning_jobs, params) req.send_request(options) end
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only versions created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only versions created on or before the specified time.
@option params [required, String] :image_name
The name of the image to list the versions of.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only versions modified on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only versions modified on or before the specified time.
@option params [Integer] :max_results
The maximum number of versions to return in the response. The default value is 10.
@option params [String] :next_token
If the previous call to `ListImageVersions` didn't return the full set of versions, the call returns a token for getting the next set of versions.
@option params [String] :sort_by
The property used to sort results. The default value is `CREATION_TIME`.
@option params [String] :sort_order
The sort order. The default value is `DESCENDING`.
@return [Types::ListImageVersionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListImageVersionsResponse#image_versions #image_versions} => Array<Types::ImageVersion> * {Types::ListImageVersionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_image_versions({ creation_time_after: Time.now, creation_time_before: Time.now, image_name: "ImageName", # required last_modified_time_after: Time.now, last_modified_time_before: Time.now, max_results: 1, next_token: "NextToken", sort_by: "CREATION_TIME", # accepts CREATION_TIME, LAST_MODIFIED_TIME, VERSION sort_order: "ASCENDING", # accepts ASCENDING, DESCENDING })
@example Response structure
resp.image_versions #=> Array resp.image_versions[0].creation_time #=> Time resp.image_versions[0].failure_reason #=> String resp.image_versions[0].image_arn #=> String resp.image_versions[0].image_version_arn #=> String resp.image_versions[0].image_version_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "DELETING", "DELETE_FAILED" resp.image_versions[0].last_modified_time #=> Time resp.image_versions[0].version #=> Integer resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListImageVersions AWS API Documentation
@overload list_image_versions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12780 def list_image_versions(params = {}, options = {}) req = build_request(:list_image_versions, params) req.send_request(options) end
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only images created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only images created on or before the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only images modified on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only images modified on or before the specified time.
@option params [Integer] :max_results
The maximum number of images to return in the response. The default value is 10.
@option params [String] :name_contains
A filter that returns only images whose name contains the specified string.
@option params [String] :next_token
If the previous call to `ListImages` didn't return the full set of images, the call returns a token for getting the next set of images.
@option params [String] :sort_by
The property used to sort results. The default value is `CREATION_TIME`.
@option params [String] :sort_order
The sort order. The default value is `DESCENDING`.
@return [Types::ListImagesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListImagesResponse#images #images} => Array<Types::Image> * {Types::ListImagesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_images({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, max_results: 1, name_contains: "ImageNameContains", next_token: "NextToken", sort_by: "CREATION_TIME", # accepts CREATION_TIME, LAST_MODIFIED_TIME, IMAGE_NAME sort_order: "ASCENDING", # accepts ASCENDING, DESCENDING })
@example Response structure
resp.images #=> Array resp.images[0].creation_time #=> Time resp.images[0].description #=> String resp.images[0].display_name #=> String resp.images[0].failure_reason #=> String resp.images[0].image_arn #=> String resp.images[0].image_name #=> String resp.images[0].image_status #=> String, one of "CREATING", "CREATED", "CREATE_FAILED", "UPDATING", "UPDATE_FAILED", "DELETING", "DELETE_FAILED" resp.images[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListImages AWS API Documentation
@overload list_images
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12862 def list_images(params = {}, options = {}) req = build_request(:list_images, params) req.send_request(options) end
Gets a list of labeling jobs.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only labeling jobs created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only labeling jobs created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only labeling jobs modified after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only labeling jobs modified before the specified time (timestamp).
@option params [Integer] :max_results
The maximum number of labeling jobs to return in each page of the response.
@option params [String] :next_token
If the result of the previous `ListLabelingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.
@option params [String] :name_contains
A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@option params [String] :status_equals
A filter that retrieves only labeling jobs with a specific status.
@return [Types::ListLabelingJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListLabelingJobsResponse#labeling_job_summary_list #labeling_job_summary_list} => Array<Types::LabelingJobSummary> * {Types::ListLabelingJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_labeling_jobs({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, max_results: 1, next_token: "NextToken", name_contains: "NameContains", sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending status_equals: "Initializing", # accepts Initializing, InProgress, Completed, Failed, Stopping, Stopped })
@example Response structure
resp.labeling_job_summary_list #=> Array resp.labeling_job_summary_list[0].labeling_job_name #=> String resp.labeling_job_summary_list[0].labeling_job_arn #=> String resp.labeling_job_summary_list[0].creation_time #=> Time resp.labeling_job_summary_list[0].last_modified_time #=> Time resp.labeling_job_summary_list[0].labeling_job_status #=> String, one of "Initializing", "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.labeling_job_summary_list[0].label_counters.total_labeled #=> Integer resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer resp.labeling_job_summary_list[0].label_counters.machine_labeled #=> Integer resp.labeling_job_summary_list[0].label_counters.failed_non_retryable_error #=> Integer resp.labeling_job_summary_list[0].label_counters.unlabeled #=> Integer resp.labeling_job_summary_list[0].workteam_arn #=> String resp.labeling_job_summary_list[0].pre_human_task_lambda_arn #=> String resp.labeling_job_summary_list[0].annotation_consolidation_lambda_arn #=> String resp.labeling_job_summary_list[0].failure_reason #=> String resp.labeling_job_summary_list[0].labeling_job_output.output_dataset_s3_uri #=> String resp.labeling_job_summary_list[0].labeling_job_output.final_active_learning_model_arn #=> String resp.labeling_job_summary_list[0].input_config.data_source.s3_data_source.manifest_s3_uri #=> String resp.labeling_job_summary_list[0].input_config.data_source.sns_data_source.sns_topic_arn #=> String resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers #=> Array resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListLabelingJobs AWS API Documentation
@overload list_labeling_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 12958 def list_labeling_jobs(params = {}, options = {}) req = build_request(:list_labeling_jobs, params) req.send_request(options) end
Gets a list of labeling jobs assigned to a specified work team.
@option params [required, String] :workteam_arn
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
@option params [Integer] :max_results
The maximum number of labeling jobs to return in each page of the response.
@option params [String] :next_token
If the result of the previous `ListLabelingJobsForWorkteam` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only labeling jobs created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only labeling jobs created before the specified time (timestamp).
@option params [String] :job_reference_code_contains
A filter the limits jobs to only the ones whose job reference code contains the specified string.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListLabelingJobsForWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListLabelingJobsForWorkteamResponse#labeling_job_summary_list #labeling_job_summary_list} => Array<Types::LabelingJobForWorkteamSummary> * {Types::ListLabelingJobsForWorkteamResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_labeling_jobs_for_workteam({ workteam_arn: "WorkteamArn", # required max_results: 1, next_token: "NextToken", creation_time_after: Time.now, creation_time_before: Time.now, job_reference_code_contains: "JobReferenceCodeContains", sort_by: "CreationTime", # accepts CreationTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.labeling_job_summary_list #=> Array resp.labeling_job_summary_list[0].labeling_job_name #=> String resp.labeling_job_summary_list[0].job_reference_code #=> String resp.labeling_job_summary_list[0].work_requester_account_id #=> String resp.labeling_job_summary_list[0].creation_time #=> Time resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer resp.labeling_job_summary_list[0].label_counters.pending_human #=> Integer resp.labeling_job_summary_list[0].label_counters.total #=> Integer resp.labeling_job_summary_list[0].number_of_human_workers_per_data_object #=> Integer resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListLabelingJobsForWorkteam AWS API Documentation
@overload list_labeling_jobs_for_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13033 def list_labeling_jobs_for_workteam(params = {}, options = {}) req = build_request(:list_labeling_jobs_for_workteam, params) req.send_request(options) end
Lists model bias jobs definitions that satisfy various filters.
@option params [String] :endpoint_name
Name of the endpoint to monitor for model bias.
@option params [String] :sort_by
Whether to sort results by the `Name` or `CreationTime` field. The default is `CreationTime`.
@option params [String] :sort_order
Whether to sort the results in `Ascending` or `Descending` order. The default is `Descending`.
@option params [String] :next_token
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
@option params [Integer] :max_results
The maximum number of model bias jobs to return in the response. The default value is 10.
@option params [String] :name_contains
Filter for model bias jobs whose name contains a specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only model bias jobs created before a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only model bias jobs created after a specified time.
@return [Types::ListModelBiasJobDefinitionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelBiasJobDefinitionsResponse#job_definition_summaries #job_definition_summaries} => Array<Types::MonitoringJobDefinitionSummary> * {Types::ListModelBiasJobDefinitionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_model_bias_job_definitions({ endpoint_name: "EndpointName", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, name_contains: "NameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.job_definition_summaries #=> Array resp.job_definition_summaries[0].monitoring_job_definition_name #=> String resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String resp.job_definition_summaries[0].creation_time #=> Time resp.job_definition_summaries[0].endpoint_name #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModelBiasJobDefinitions AWS API Documentation
@overload list_model_bias_job_definitions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13103 def list_model_bias_job_definitions(params = {}, options = {}) req = build_request(:list_model_bias_job_definitions, params) req.send_request(options) end
Lists model explainability job definitions that satisfy various filters.
@option params [String] :endpoint_name
Name of the endpoint to monitor for model explainability.
@option params [String] :sort_by
Whether to sort results by the `Name` or `CreationTime` field. The default is `CreationTime`.
@option params [String] :sort_order
Whether to sort the results in `Ascending` or `Descending` order. The default is `Descending`.
@option params [String] :next_token
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
@option params [Integer] :max_results
The maximum number of jobs to return in the response. The default value is 10.
@option params [String] :name_contains
Filter for model explainability jobs whose name contains a specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only model explainability jobs created before a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only model explainability jobs created after a specified time.
@return [Types::ListModelExplainabilityJobDefinitionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelExplainabilityJobDefinitionsResponse#job_definition_summaries #job_definition_summaries} => Array<Types::MonitoringJobDefinitionSummary> * {Types::ListModelExplainabilityJobDefinitionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_model_explainability_job_definitions({ endpoint_name: "EndpointName", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, name_contains: "NameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.job_definition_summaries #=> Array resp.job_definition_summaries[0].monitoring_job_definition_name #=> String resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String resp.job_definition_summaries[0].creation_time #=> Time resp.job_definition_summaries[0].endpoint_name #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModelExplainabilityJobDefinitions AWS API Documentation
@overload list_model_explainability_job_definitions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13175 def list_model_explainability_job_definitions(params = {}, options = {}) req = build_request(:list_model_explainability_job_definitions, params) req.send_request(options) end
Gets a list of the model groups in your Amazon Web Services account.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only model groups created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only model groups created before the specified time.
@option params [Integer] :max_results
The maximum number of results to return in the response.
@option params [String] :name_contains
A string in the model group name. This filter returns only model groups whose name contains the specified string.
@option params [String] :next_token
If the result of the previous `ListModelPackageGroups` request was truncated, the response includes a `NextToken`. To retrieve the next set of model groups, use the token in the next request.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListModelPackageGroupsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelPackageGroupsOutput#model_package_group_summary_list #model_package_group_summary_list} => Array<Types::ModelPackageGroupSummary> * {Types::ListModelPackageGroupsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_model_package_groups({ creation_time_after: Time.now, creation_time_before: Time.now, max_results: 1, name_contains: "NameContains", next_token: "NextToken", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.model_package_group_summary_list #=> Array resp.model_package_group_summary_list[0].model_package_group_name #=> String resp.model_package_group_summary_list[0].model_package_group_arn #=> String resp.model_package_group_summary_list[0].model_package_group_description #=> String resp.model_package_group_summary_list[0].creation_time #=> Time resp.model_package_group_summary_list[0].model_package_group_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting", "DeleteFailed" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModelPackageGroups AWS API Documentation
@overload list_model_package_groups
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13241 def list_model_package_groups(params = {}, options = {}) req = build_request(:list_model_package_groups, params) req.send_request(options) end
Lists the model packages that have been created.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only model packages created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only model packages created before the specified time (timestamp).
@option params [Integer] :max_results
The maximum number of model packages to return in the response.
@option params [String] :name_contains
A string in the model package name. This filter returns only model packages whose name contains the specified string.
@option params [String] :model_approval_status
A filter that returns only the model packages with the specified approval status.
@option params [String] :model_package_group_name
A filter that returns only model versions that belong to the specified model group.
@option params [String] :model_package_type
A filter that returns onlyl the model packages of the specified type. This can be one of the following values. * `VERSIONED` - List only versioned models. * `UNVERSIONED` - List only unversioined models. * `BOTH` - List both versioned and unversioned models.
@option params [String] :next_token
If the response to a previous `ListModelPackages` request was truncated, the response includes a `NextToken`. To retrieve the next set of model packages, use the token in the next request.
@option params [String] :sort_by
The parameter by which to sort the results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for the results. The default is `Ascending`.
@return [Types::ListModelPackagesOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelPackagesOutput#model_package_summary_list #model_package_summary_list} => Array<Types::ModelPackageSummary> * {Types::ListModelPackagesOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_model_packages({ creation_time_after: Time.now, creation_time_before: Time.now, max_results: 1, name_contains: "NameContains", model_approval_status: "Approved", # accepts Approved, Rejected, PendingManualApproval model_package_group_name: "ArnOrName", model_package_type: "Versioned", # accepts Versioned, Unversioned, Both next_token: "NextToken", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.model_package_summary_list #=> Array resp.model_package_summary_list[0].model_package_name #=> String resp.model_package_summary_list[0].model_package_group_name #=> String resp.model_package_summary_list[0].model_package_version #=> Integer resp.model_package_summary_list[0].model_package_arn #=> String resp.model_package_summary_list[0].model_package_description #=> String resp.model_package_summary_list[0].creation_time #=> Time resp.model_package_summary_list[0].model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting" resp.model_package_summary_list[0].model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModelPackages AWS API Documentation
@overload list_model_packages
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13332 def list_model_packages(params = {}, options = {}) req = build_request(:list_model_packages, params) req.send_request(options) end
Gets a list of model quality monitoring job definitions in your account.
@option params [String] :endpoint_name
A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the result of the previous `ListModelQualityJobDefinitions` request was truncated, the response includes a `NextToken`. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.
@option params [Integer] :max_results
The maximum number of results to return in a call to `ListModelQualityJobDefinitions`.
@option params [String] :name_contains
A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only model quality monitoring job definitions created before the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only model quality monitoring job definitions created after the specified time.
@return [Types::ListModelQualityJobDefinitionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelQualityJobDefinitionsResponse#job_definition_summaries #job_definition_summaries} => Array<Types::MonitoringJobDefinitionSummary> * {Types::ListModelQualityJobDefinitionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_model_quality_job_definitions({ endpoint_name: "EndpointName", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, name_contains: "NameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.job_definition_summaries #=> Array resp.job_definition_summaries[0].monitoring_job_definition_name #=> String resp.job_definition_summaries[0].monitoring_job_definition_arn #=> String resp.job_definition_summaries[0].creation_time #=> Time resp.job_definition_summaries[0].endpoint_name #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModelQualityJobDefinitions AWS API Documentation
@overload list_model_quality_job_definitions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13406 def list_model_quality_job_definitions(params = {}, options = {}) req = build_request(:list_model_quality_job_definitions, params) req.send_request(options) end
Lists models created with the `CreateModel` API.
@option params [String] :sort_by
Sorts the list of results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the response to a previous `ListModels` request was truncated, the response includes a `NextToken`. To retrieve the next set of models, use the token in the next request.
@option params [Integer] :max_results
The maximum number of models to return in the response.
@option params [String] :name_contains
A string in the training job name. This filter returns only models in the training job whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only models created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
@return [Types::ListModelsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListModelsOutput#models #models} => Array<Types::ModelSummary> * {Types::ListModelsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_models({ sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "PaginationToken", max_results: 1, name_contains: "ModelNameContains", creation_time_before: Time.now, creation_time_after: Time.now, })
@example Response structure
resp.models #=> Array resp.models[0].model_name #=> String resp.models[0].model_arn #=> String resp.models[0].creation_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListModels AWS API Documentation
@overload list_models
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13470 def list_models(params = {}, options = {}) req = build_request(:list_models, params) req.send_request(options) end
Returns list of all monitoring job executions.
@option params [String] :monitoring_schedule_name
Name of a specific schedule to fetch jobs for.
@option params [String] :endpoint_name
Name of a specific endpoint to fetch jobs for.
@option params [String] :sort_by
Whether to sort results by `Status`, `CreationTime`, `ScheduledTime` field. The default is `CreationTime`.
@option params [String] :sort_order
Whether to sort the results in `Ascending` or `Descending` order. The default is `Descending`.
@option params [String] :next_token
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
@option params [Integer] :max_results
The maximum number of jobs to return in the response. The default value is 10.
@option params [Time,DateTime,Date,Integer,String] :scheduled_time_before
Filter for jobs scheduled before a specified time.
@option params [Time,DateTime,Date,Integer,String] :scheduled_time_after
Filter for jobs scheduled after a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only jobs created before a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only jobs created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only jobs modified after a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only jobs modified before a specified time.
@option params [String] :status_equals
A filter that retrieves only jobs with a specific status.
@option params [String] :monitoring_job_definition_name
Gets a list of the monitoring job runs of the specified monitoring job definitions.
@option params [String] :monitoring_type_equals
A filter that returns only the monitoring job runs of the specified monitoring type.
@return [Types::ListMonitoringExecutionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListMonitoringExecutionsResponse#monitoring_execution_summaries #monitoring_execution_summaries} => Array<Types::MonitoringExecutionSummary> * {Types::ListMonitoringExecutionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_monitoring_executions({ monitoring_schedule_name: "MonitoringScheduleName", endpoint_name: "EndpointName", sort_by: "CreationTime", # accepts CreationTime, ScheduledTime, Status sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, scheduled_time_before: Time.now, scheduled_time_after: Time.now, creation_time_before: Time.now, creation_time_after: Time.now, last_modified_time_before: Time.now, last_modified_time_after: Time.now, status_equals: "Pending", # accepts Pending, Completed, CompletedWithViolations, InProgress, Failed, Stopping, Stopped monitoring_job_definition_name: "MonitoringJobDefinitionName", monitoring_type_equals: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability })
@example Response structure
resp.monitoring_execution_summaries #=> Array resp.monitoring_execution_summaries[0].monitoring_schedule_name #=> String resp.monitoring_execution_summaries[0].scheduled_time #=> Time resp.monitoring_execution_summaries[0].creation_time #=> Time resp.monitoring_execution_summaries[0].last_modified_time #=> Time resp.monitoring_execution_summaries[0].monitoring_execution_status #=> String, one of "Pending", "Completed", "CompletedWithViolations", "InProgress", "Failed", "Stopping", "Stopped" resp.monitoring_execution_summaries[0].processing_job_arn #=> String resp.monitoring_execution_summaries[0].endpoint_name #=> String resp.monitoring_execution_summaries[0].failure_reason #=> String resp.monitoring_execution_summaries[0].monitoring_job_definition_name #=> String resp.monitoring_execution_summaries[0].monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListMonitoringExecutions AWS API Documentation
@overload list_monitoring_executions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13574 def list_monitoring_executions(params = {}, options = {}) req = build_request(:list_monitoring_executions, params) req.send_request(options) end
Returns list of all monitoring schedules.
@option params [String] :endpoint_name
Name of a specific endpoint to fetch schedules for.
@option params [String] :sort_by
Whether to sort results by `Status`, `CreationTime`, `ScheduledTime` field. The default is `CreationTime`.
@option params [String] :sort_order
Whether to sort the results in `Ascending` or `Descending` order. The default is `Descending`.
@option params [String] :next_token
The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
@option params [Integer] :max_results
The maximum number of jobs to return in the response. The default value is 10.
@option params [String] :name_contains
Filter for monitoring schedules whose name contains a specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only monitoring schedules created before a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only monitoring schedules created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only monitoring schedules modified before a specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only monitoring schedules modified after a specified time.
@option params [String] :status_equals
A filter that returns only monitoring schedules modified before a specified time.
@option params [String] :monitoring_job_definition_name
Gets a list of the monitoring schedules for the specified monitoring job definition.
@option params [String] :monitoring_type_equals
A filter that returns only the monitoring schedules for the specified monitoring type.
@return [Types::ListMonitoringSchedulesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListMonitoringSchedulesResponse#monitoring_schedule_summaries #monitoring_schedule_summaries} => Array<Types::MonitoringScheduleSummary> * {Types::ListMonitoringSchedulesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_monitoring_schedules({ endpoint_name: "EndpointName", sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, name_contains: "NameContains", creation_time_before: Time.now, creation_time_after: Time.now, last_modified_time_before: Time.now, last_modified_time_after: Time.now, status_equals: "Pending", # accepts Pending, Failed, Scheduled, Stopped monitoring_job_definition_name: "MonitoringJobDefinitionName", monitoring_type_equals: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability })
@example Response structure
resp.monitoring_schedule_summaries #=> Array resp.monitoring_schedule_summaries[0].monitoring_schedule_name #=> String resp.monitoring_schedule_summaries[0].monitoring_schedule_arn #=> String resp.monitoring_schedule_summaries[0].creation_time #=> Time resp.monitoring_schedule_summaries[0].last_modified_time #=> Time resp.monitoring_schedule_summaries[0].monitoring_schedule_status #=> String, one of "Pending", "Failed", "Scheduled", "Stopped" resp.monitoring_schedule_summaries[0].endpoint_name #=> String resp.monitoring_schedule_summaries[0].monitoring_job_definition_name #=> String resp.monitoring_schedule_summaries[0].monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListMonitoringSchedules AWS API Documentation
@overload list_monitoring_schedules
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13674 def list_monitoring_schedules(params = {}, options = {}) req = build_request(:list_monitoring_schedules, params) req.send_request(options) end
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
@option params [String] :next_token
If the result of a `ListNotebookInstanceLifecycleConfigs` request was truncated, the response includes a `NextToken`. To get the next set of lifecycle configurations, use the token in the next request.
@option params [Integer] :max_results
The maximum number of lifecycle configurations to return in the response.
@option params [String] :sort_by
Sorts the list of results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results.
@option params [String] :name_contains
A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
@return [Types::ListNotebookInstanceLifecycleConfigsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListNotebookInstanceLifecycleConfigsOutput#next_token #next_token} => String * {Types::ListNotebookInstanceLifecycleConfigsOutput#notebook_instance_lifecycle_configs #notebook_instance_lifecycle_configs} => Array<Types::NotebookInstanceLifecycleConfigSummary>
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_notebook_instance_lifecycle_configs({ next_token: "NextToken", max_results: 1, sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime sort_order: "Ascending", # accepts Ascending, Descending name_contains: "NotebookInstanceLifecycleConfigNameContains", creation_time_before: Time.now, creation_time_after: Time.now, last_modified_time_before: Time.now, last_modified_time_after: Time.now, })
@example Response structure
resp.next_token #=> String resp.notebook_instance_lifecycle_configs #=> Array resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_name #=> String resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_arn #=> String resp.notebook_instance_lifecycle_configs[0].creation_time #=> Time resp.notebook_instance_lifecycle_configs[0].last_modified_time #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListNotebookInstanceLifecycleConfigs AWS API Documentation
@overload list_notebook_instance_lifecycle_configs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13751 def list_notebook_instance_lifecycle_configs(params = {}, options = {}) req = build_request(:list_notebook_instance_lifecycle_configs, params) req.send_request(options) end
Returns a list of the Amazon SageMaker
notebook instances in the requester's account in an Amazon Web Services Region.
@option params [String] :next_token
If the previous call to the `ListNotebookInstances` is truncated, the response includes a `NextToken`. You can use this token in your subsequent `ListNotebookInstances` request to fetch the next set of notebook instances. <note markdown="1"> You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request. </note>
@option params [Integer] :max_results
The maximum number of notebook instances to return.
@option params [String] :sort_by
The field to sort results by. The default is `Name`.
@option params [String] :sort_order
The sort order for results.
@option params [String] :name_contains
A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only notebook instances that were created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only notebook instances that were created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only notebook instances that were modified before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only notebook instances that were modified after the specified time (timestamp).
@option params [String] :status_equals
A filter that returns only notebook instances with the specified status.
@option params [String] :notebook_instance_lifecycle_config_name_contains
A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
@option params [String] :default_code_repository_contains
A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
@option params [String] :additional_code_repository_equals
A filter that returns only notebook instances with associated with the specified git repository.
@return [Types::ListNotebookInstancesOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListNotebookInstancesOutput#next_token #next_token} => String * {Types::ListNotebookInstancesOutput#notebook_instances #notebook_instances} => Array<Types::NotebookInstanceSummary>
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_notebook_instances({ next_token: "NextToken", max_results: 1, sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending name_contains: "NotebookInstanceNameContains", creation_time_before: Time.now, creation_time_after: Time.now, last_modified_time_before: Time.now, last_modified_time_after: Time.now, status_equals: "Pending", # accepts Pending, InService, Stopping, Stopped, Failed, Deleting, Updating notebook_instance_lifecycle_config_name_contains: "NotebookInstanceLifecycleConfigName", default_code_repository_contains: "CodeRepositoryContains", additional_code_repository_equals: "CodeRepositoryNameOrUrl", })
@example Response structure
resp.next_token #=> String resp.notebook_instances #=> Array resp.notebook_instances[0].notebook_instance_name #=> String resp.notebook_instances[0].notebook_instance_arn #=> String resp.notebook_instances[0].notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating" resp.notebook_instances[0].url #=> String resp.notebook_instances[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.8xlarge", "ml.m5d.12xlarge", "ml.m5d.16xlarge", "ml.m5d.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge" resp.notebook_instances[0].creation_time #=> Time resp.notebook_instances[0].last_modified_time #=> Time resp.notebook_instances[0].notebook_instance_lifecycle_config_name #=> String resp.notebook_instances[0].default_code_repository #=> String resp.notebook_instances[0].additional_code_repositories #=> Array resp.notebook_instances[0].additional_code_repositories[0] #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListNotebookInstances AWS API Documentation
@overload list_notebook_instances
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13865 def list_notebook_instances(params = {}, options = {}) req = build_request(:list_notebook_instances, params) req.send_request(options) end
Gets a list of `PipeLineExecutionStep` objects.
@option params [String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@option params [String] :next_token
If the result of the previous `ListPipelineExecutionSteps` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipeline execution steps, use the token in the next request.
@option params [Integer] :max_results
The maximum number of pipeline execution steps to return in the response.
@option params [String] :sort_order
The field by which to sort results. The default is `CreatedTime`.
@return [Types::ListPipelineExecutionStepsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListPipelineExecutionStepsResponse#pipeline_execution_steps #pipeline_execution_steps} => Array<Types::PipelineExecutionStep> * {Types::ListPipelineExecutionStepsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_pipeline_execution_steps({ pipeline_execution_arn: "PipelineExecutionArn", next_token: "NextToken", max_results: 1, sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.pipeline_execution_steps #=> Array resp.pipeline_execution_steps[0].step_name #=> String resp.pipeline_execution_steps[0].start_time #=> Time resp.pipeline_execution_steps[0].end_time #=> Time resp.pipeline_execution_steps[0].step_status #=> String, one of "Starting", "Executing", "Stopping", "Stopped", "Failed", "Succeeded" resp.pipeline_execution_steps[0].cache_hit_result.source_pipeline_execution_arn #=> String resp.pipeline_execution_steps[0].failure_reason #=> String resp.pipeline_execution_steps[0].metadata.training_job.arn #=> String resp.pipeline_execution_steps[0].metadata.processing_job.arn #=> String resp.pipeline_execution_steps[0].metadata.transform_job.arn #=> String resp.pipeline_execution_steps[0].metadata.tuning_job.arn #=> String resp.pipeline_execution_steps[0].metadata.model.arn #=> String resp.pipeline_execution_steps[0].metadata.register_model.arn #=> String resp.pipeline_execution_steps[0].metadata.condition.outcome #=> String, one of "True", "False" resp.pipeline_execution_steps[0].metadata.callback.callback_token #=> String resp.pipeline_execution_steps[0].metadata.callback.sqs_queue_url #=> String resp.pipeline_execution_steps[0].metadata.callback.output_parameters #=> Array resp.pipeline_execution_steps[0].metadata.callback.output_parameters[0].name #=> String resp.pipeline_execution_steps[0].metadata.callback.output_parameters[0].value #=> String resp.pipeline_execution_steps[0].metadata.lambda.arn #=> String resp.pipeline_execution_steps[0].metadata.lambda.output_parameters #=> Array resp.pipeline_execution_steps[0].metadata.lambda.output_parameters[0].name #=> String resp.pipeline_execution_steps[0].metadata.lambda.output_parameters[0].value #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListPipelineExecutionSteps AWS API Documentation
@overload list_pipeline_execution_steps
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13934 def list_pipeline_execution_steps(params = {}, options = {}) req = build_request(:list_pipeline_execution_steps, params) req.send_request(options) end
Gets a list of the pipeline executions.
@option params [required, String] :pipeline_name
The name of the pipeline.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns the pipeline executions that were created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns the pipeline executions that were created before a specified time.
@option params [String] :sort_by
The field by which to sort results. The default is `CreatedTime`.
@option params [String] :sort_order
The sort order for results.
@option params [String] :next_token
If the result of the previous `ListPipelineExecutions` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipeline executions, use the token in the next request.
@option params [Integer] :max_results
The maximum number of pipeline executions to return in the response.
@return [Types::ListPipelineExecutionsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListPipelineExecutionsResponse#pipeline_execution_summaries #pipeline_execution_summaries} => Array<Types::PipelineExecutionSummary> * {Types::ListPipelineExecutionsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_pipeline_executions({ pipeline_name: "PipelineName", # required created_after: Time.now, created_before: Time.now, sort_by: "CreationTime", # accepts CreationTime, PipelineExecutionArn sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.pipeline_execution_summaries #=> Array resp.pipeline_execution_summaries[0].pipeline_execution_arn #=> String resp.pipeline_execution_summaries[0].start_time #=> Time resp.pipeline_execution_summaries[0].pipeline_execution_status #=> String, one of "Executing", "Stopping", "Stopped", "Failed", "Succeeded" resp.pipeline_execution_summaries[0].pipeline_execution_description #=> String resp.pipeline_execution_summaries[0].pipeline_execution_display_name #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListPipelineExecutions AWS API Documentation
@overload list_pipeline_executions
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 13999 def list_pipeline_executions(params = {}, options = {}) req = build_request(:list_pipeline_executions, params) req.send_request(options) end
Gets a list of parameters for a pipeline execution.
@option params [required, String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@option params [String] :next_token
If the result of the previous `ListPipelineParametersForExecution` request was truncated, the response includes a `NextToken`. To retrieve the next set of parameters, use the token in the next request.
@option params [Integer] :max_results
The maximum number of parameters to return in the response.
@return [Types::ListPipelineParametersForExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListPipelineParametersForExecutionResponse#pipeline_parameters #pipeline_parameters} => Array<Types::Parameter> * {Types::ListPipelineParametersForExecutionResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_pipeline_parameters_for_execution({ pipeline_execution_arn: "PipelineExecutionArn", # required next_token: "NextToken", max_results: 1, })
@example Response structure
resp.pipeline_parameters #=> Array resp.pipeline_parameters[0].name #=> String resp.pipeline_parameters[0].value #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListPipelineParametersForExecution AWS API Documentation
@overload list_pipeline_parameters_for_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14044 def list_pipeline_parameters_for_execution(params = {}, options = {}) req = build_request(:list_pipeline_parameters_for_execution, params) req.send_request(options) end
Gets a list of pipelines.
@option params [String] :pipeline_name_prefix
The prefix of the pipeline name.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns the pipelines that were created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns the pipelines that were created before a specified time.
@option params [String] :sort_by
The field by which to sort results. The default is `CreatedTime`.
@option params [String] :sort_order
The sort order for results.
@option params [String] :next_token
If the result of the previous `ListPipelines` request was truncated, the response includes a `NextToken`. To retrieve the next set of pipelines, use the token in the next request.
@option params [Integer] :max_results
The maximum number of pipelines to return in the response.
@return [Types::ListPipelinesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListPipelinesResponse#pipeline_summaries #pipeline_summaries} => Array<Types::PipelineSummary> * {Types::ListPipelinesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_pipelines({ pipeline_name_prefix: "PipelineName", created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.pipeline_summaries #=> Array resp.pipeline_summaries[0].pipeline_arn #=> String resp.pipeline_summaries[0].pipeline_name #=> String resp.pipeline_summaries[0].pipeline_display_name #=> String resp.pipeline_summaries[0].pipeline_description #=> String resp.pipeline_summaries[0].role_arn #=> String resp.pipeline_summaries[0].creation_time #=> Time resp.pipeline_summaries[0].last_modified_time #=> Time resp.pipeline_summaries[0].last_execution_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListPipelines AWS API Documentation
@overload list_pipelines
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14112 def list_pipelines(params = {}, options = {}) req = build_request(:list_pipelines, params) req.send_request(options) end
Lists processing jobs that satisfy various filters.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only processing jobs created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only processing jobs created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only processing jobs modified after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only processing jobs modified before the specified time.
@option params [String] :name_contains
A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
@option params [String] :status_equals
A filter that retrieves only processing jobs with a specific status.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@option params [String] :next_token
If the result of the previous `ListProcessingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of processing jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of processing jobs to return in the response.
@return [Types::ListProcessingJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListProcessingJobsResponse#processing_job_summaries #processing_job_summaries} => Array<Types::ProcessingJobSummary> * {Types::ListProcessingJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_processing_jobs({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "String", status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.processing_job_summaries #=> Array resp.processing_job_summaries[0].processing_job_name #=> String resp.processing_job_summaries[0].processing_job_arn #=> String resp.processing_job_summaries[0].creation_time #=> Time resp.processing_job_summaries[0].processing_end_time #=> Time resp.processing_job_summaries[0].last_modified_time #=> Time resp.processing_job_summaries[0].processing_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.processing_job_summaries[0].failure_reason #=> String resp.processing_job_summaries[0].exit_message #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListProcessingJobs AWS API Documentation
@overload list_processing_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14195 def list_processing_jobs(params = {}, options = {}) req = build_request(:list_processing_jobs, params) req.send_request(options) end
Gets a list of the projects in an Amazon Web Services account.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns the projects that were created after a specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns the projects that were created before a specified time.
@option params [Integer] :max_results
The maximum number of projects to return in the response.
@option params [String] :name_contains
A filter that returns the projects whose name contains a specified string.
@option params [String] :next_token
If the result of the previous `ListProjects` request was truncated, the response includes a `NextToken`. To retrieve the next set of projects, use the token in the next request.
@option params [String] :sort_by
The field by which to sort results. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListProjectsOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListProjectsOutput#project_summary_list #project_summary_list} => Array<Types::ProjectSummary> * {Types::ListProjectsOutput#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_projects({ creation_time_after: Time.now, creation_time_before: Time.now, max_results: 1, name_contains: "ProjectEntityName", next_token: "NextToken", sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.project_summary_list #=> Array resp.project_summary_list[0].project_name #=> String resp.project_summary_list[0].project_description #=> String resp.project_summary_list[0].project_arn #=> String resp.project_summary_list[0].project_id #=> String resp.project_summary_list[0].creation_time #=> Time resp.project_summary_list[0].project_status #=> String, one of "Pending", "CreateInProgress", "CreateCompleted", "CreateFailed", "DeleteInProgress", "DeleteFailed", "DeleteCompleted" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListProjects AWS API Documentation
@overload list_projects
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14262 def list_projects(params = {}, options = {}) req = build_request(:list_projects, params) req.send_request(options) end
Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.
@option params [Integer] :max_results
The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.
@option params [String] :next_token
If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
@option params [String] :name_contains
A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
@option params [String] :app_type_equals
A parameter to search for the App Type to which the Lifecycle Configuration is attached.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only Lifecycle Configurations created on or before the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only Lifecycle Configurations created on or after the specified time.
@option params [Time,DateTime,Date,Integer,String] :modified_time_before
A filter that returns only Lifecycle Configurations modified before the specified time.
@option params [Time,DateTime,Date,Integer,String] :modified_time_after
A filter that returns only Lifecycle Configurations modified after the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is CreationTime.
@option params [String] :sort_order
The sort order. The default value is Descending.
@return [Types::ListStudioLifecycleConfigsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListStudioLifecycleConfigsResponse#next_token #next_token} => String * {Types::ListStudioLifecycleConfigsResponse#studio_lifecycle_configs #studio_lifecycle_configs} => Array<Types::StudioLifecycleConfigDetails>
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_studio_lifecycle_configs({ max_results: 1, next_token: "NextToken", name_contains: "StudioLifecycleConfigName", app_type_equals: "JupyterServer", # accepts JupyterServer, KernelGateway creation_time_before: Time.now, creation_time_after: Time.now, modified_time_before: Time.now, modified_time_after: Time.now, sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime, Name sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.next_token #=> String resp.studio_lifecycle_configs #=> Array resp.studio_lifecycle_configs[0].studio_lifecycle_config_arn #=> String resp.studio_lifecycle_configs[0].studio_lifecycle_config_name #=> String resp.studio_lifecycle_configs[0].creation_time #=> Time resp.studio_lifecycle_configs[0].last_modified_time #=> Time resp.studio_lifecycle_configs[0].studio_lifecycle_config_app_type #=> String, one of "JupyterServer", "KernelGateway"
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListStudioLifecycleConfigs AWS API Documentation
@overload list_studio_lifecycle_configs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14345 def list_studio_lifecycle_configs(params = {}, options = {}) req = build_request(:list_studio_lifecycle_configs, params) req.send_request(options) end
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the `NameContains` parameter.
@option params [String] :name_contains
A string in the work team name. This filter returns only work teams whose name contains the specified string.
@option params [String] :next_token
If the result of the previous `ListSubscribedWorkteams` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of work teams to return in each page of the response.
@return [Types::ListSubscribedWorkteamsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListSubscribedWorkteamsResponse#subscribed_workteams #subscribed_workteams} => Array<Types::SubscribedWorkteam> * {Types::ListSubscribedWorkteamsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_subscribed_workteams({ name_contains: "WorkteamName", next_token: "NextToken", max_results: 1, })
@example Response structure
resp.subscribed_workteams #=> Array resp.subscribed_workteams[0].workteam_arn #=> String resp.subscribed_workteams[0].marketplace_title #=> String resp.subscribed_workteams[0].seller_name #=> String resp.subscribed_workteams[0].marketplace_description #=> String resp.subscribed_workteams[0].listing_id #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListSubscribedWorkteams AWS API Documentation
@overload list_subscribed_workteams
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14396 def list_subscribed_workteams(params = {}, options = {}) req = build_request(:list_subscribed_workteams, params) req.send_request(options) end
Lists training jobs.
<note markdown=“1”> When `StatusEquals` and `MaxResults` are set at the same time, the `MaxResults` number of training jobs are first retrieved ignoring the `StatusEquals` parameter and then they are filtered by the `StatusEquals` parameter, which is returned as a response.
For example, if `ListTrainingJobs` is invoked with the following
parameters:
`\{ ... MaxResults: 100, StatusEquals: InProgress ... \}` First, 100 trainings jobs with any status, including those other than
`InProgress`, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of `InProgress` are returned.
You can quickly test the API using the following Amazon Web Services
CLI code.
`aws sagemaker list-training-jobs --max-results 100 --status-equals
InProgress`
</note>
@option params [String] :next_token
If the result of the previous `ListTrainingJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of training jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of training jobs to return in the response.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only training jobs created after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only training jobs created before the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only training jobs modified after the specified time (timestamp).
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only training jobs modified before the specified time (timestamp).
@option params [String] :name_contains
A string in the training job name. This filter returns only training jobs whose name contains the specified string.
@option params [String] :status_equals
A filter that retrieves only training jobs with a specific status.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListTrainingJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListTrainingJobsResponse#training_job_summaries #training_job_summaries} => Array<Types::TrainingJobSummary> * {Types::ListTrainingJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_training_jobs({ next_token: "NextToken", max_results: 1, creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "NameContains", status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.training_job_summaries #=> Array resp.training_job_summaries[0].training_job_name #=> String resp.training_job_summaries[0].training_job_arn #=> String resp.training_job_summaries[0].creation_time #=> Time resp.training_job_summaries[0].training_end_time #=> Time resp.training_job_summaries[0].last_modified_time #=> Time resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListTrainingJobs AWS API Documentation
@overload list_training_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14545 def list_training_jobs(params = {}, options = {}) req = build_request(:list_training_jobs, params) req.send_request(options) end
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
@option params [required, String] :hyper_parameter_tuning_job_name
The name of the tuning job whose training jobs you want to list.
@option params [String] :next_token
If the result of the previous `ListTrainingJobsForHyperParameterTuningJob` request was truncated, the response includes a `NextToken`. To retrieve the next set of training jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of training jobs to return. The default value is 10.
@option params [String] :status_equals
A filter that returns only training jobs with the specified status.
@option params [String] :sort_by
The field to sort results by. The default is `Name`. If the value of this field is `FinalObjectiveMetricValue`, any training jobs that did not return an objective metric are not listed.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@return [Types::ListTrainingJobsForHyperParameterTuningJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListTrainingJobsForHyperParameterTuningJobResponse#training_job_summaries #training_job_summaries} => Array<Types::HyperParameterTrainingJobSummary> * {Types::ListTrainingJobsForHyperParameterTuningJobResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_training_jobs_for_hyper_parameter_tuning_job({ hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required next_token: "NextToken", max_results: 1, status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped sort_by: "Name", # accepts Name, CreationTime, Status, FinalObjectiveMetricValue sort_order: "Ascending", # accepts Ascending, Descending })
@example Response structure
resp.training_job_summaries #=> Array resp.training_job_summaries[0].training_job_definition_name #=> String resp.training_job_summaries[0].training_job_name #=> String resp.training_job_summaries[0].training_job_arn #=> String resp.training_job_summaries[0].tuning_job_name #=> String resp.training_job_summaries[0].creation_time #=> Time resp.training_job_summaries[0].training_start_time #=> Time resp.training_job_summaries[0].training_end_time #=> Time resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.training_job_summaries[0].tuned_hyper_parameters #=> Hash resp.training_job_summaries[0].tuned_hyper_parameters["HyperParameterKey"] #=> String resp.training_job_summaries[0].failure_reason #=> String resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize" resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.value #=> Float resp.training_job_summaries[0].objective_status #=> String, one of "Succeeded", "Pending", "Failed" resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListTrainingJobsForHyperParameterTuningJob AWS API Documentation
@overload list_training_jobs_for_hyper_parameter_tuning_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14620 def list_training_jobs_for_hyper_parameter_tuning_job(params = {}, options = {}) req = build_request(:list_training_jobs_for_hyper_parameter_tuning_job, params) req.send_request(options) end
Lists transform jobs.
@option params [Time,DateTime,Date,Integer,String] :creation_time_after
A filter that returns only transform jobs created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :creation_time_before
A filter that returns only transform jobs created before the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_after
A filter that returns only transform jobs modified after the specified time.
@option params [Time,DateTime,Date,Integer,String] :last_modified_time_before
A filter that returns only transform jobs modified before the specified time.
@option params [String] :name_contains
A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
@option params [String] :status_equals
A filter that retrieves only transform jobs with a specific status.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Descending`.
@option params [String] :next_token
If the result of the previous `ListTransformJobs` request was truncated, the response includes a `NextToken`. To retrieve the next set of transform jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of transform jobs to return in the response. The default value is `10`.
@return [Types::ListTransformJobsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListTransformJobsResponse#transform_job_summaries #transform_job_summaries} => Array<Types::TransformJobSummary> * {Types::ListTransformJobsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_transform_jobs({ creation_time_after: Time.now, creation_time_before: Time.now, last_modified_time_after: Time.now, last_modified_time_before: Time.now, name_contains: "NameContains", status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped sort_by: "Name", # accepts Name, CreationTime, Status sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.transform_job_summaries #=> Array resp.transform_job_summaries[0].transform_job_name #=> String resp.transform_job_summaries[0].transform_job_arn #=> String resp.transform_job_summaries[0].creation_time #=> Time resp.transform_job_summaries[0].transform_end_time #=> Time resp.transform_job_summaries[0].last_modified_time #=> Time resp.transform_job_summaries[0].transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.transform_job_summaries[0].failure_reason #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListTransformJobs AWS API Documentation
@overload list_transform_jobs
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14703 def list_transform_jobs(params = {}, options = {}) req = build_request(:list_transform_jobs, params) req.send_request(options) end
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
-
`ExperimentName`
-
`SourceArn`
-
`TrialName`
@option params [String] :experiment_name
A filter that returns only components that are part of the specified experiment. If you specify `ExperimentName`, you can't filter by `SourceArn` or `TrialName`.
@option params [String] :trial_name
A filter that returns only components that are part of the specified trial. If you specify `TrialName`, you can't filter by `ExperimentName` or `SourceArn`.
@option params [String] :source_arn
A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify `SourceArn`, you can't filter by `ExperimentName` or `TrialName`.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only components created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only components created before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [Integer] :max_results
The maximum number of components to return in the response. The default value is 10.
@option params [String] :next_token
If the previous call to `ListTrialComponents` didn't return the full set of components, the call returns a token for getting the next set of components.
@return [Types::ListTrialComponentsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListTrialComponentsResponse#trial_component_summaries #trial_component_summaries} => Array<Types::TrialComponentSummary> * {Types::ListTrialComponentsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_trial_components({ experiment_name: "ExperimentEntityName", trial_name: "ExperimentEntityName", source_arn: "String256", created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending max_results: 1, next_token: "NextToken", })
@example Response structure
resp.trial_component_summaries #=> Array resp.trial_component_summaries[0].trial_component_name #=> String resp.trial_component_summaries[0].trial_component_arn #=> String resp.trial_component_summaries[0].display_name #=> String resp.trial_component_summaries[0].trial_component_source.source_arn #=> String resp.trial_component_summaries[0].trial_component_source.source_type #=> String resp.trial_component_summaries[0].status.primary_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.trial_component_summaries[0].status.message #=> String resp.trial_component_summaries[0].start_time #=> Time resp.trial_component_summaries[0].end_time #=> Time resp.trial_component_summaries[0].creation_time #=> Time resp.trial_component_summaries[0].created_by.user_profile_arn #=> String resp.trial_component_summaries[0].created_by.user_profile_name #=> String resp.trial_component_summaries[0].created_by.domain_id #=> String resp.trial_component_summaries[0].last_modified_time #=> Time resp.trial_component_summaries[0].last_modified_by.user_profile_arn #=> String resp.trial_component_summaries[0].last_modified_by.user_profile_name #=> String resp.trial_component_summaries[0].last_modified_by.domain_id #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListTrialComponents AWS API Documentation
@overload list_trial_components
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14805 def list_trial_components(params = {}, options = {}) req = build_request(:list_trial_components, params) req.send_request(options) end
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
@option params [String] :experiment_name
A filter that returns only trials that are part of the specified experiment.
@option params [String] :trial_component_name
A filter that returns only trials that are associated with the specified trial component.
@option params [Time,DateTime,Date,Integer,String] :created_after
A filter that returns only trials created after the specified time.
@option params [Time,DateTime,Date,Integer,String] :created_before
A filter that returns only trials created before the specified time.
@option params [String] :sort_by
The property used to sort results. The default value is `CreationTime`.
@option params [String] :sort_order
The sort order. The default value is `Descending`.
@option params [Integer] :max_results
The maximum number of trials to return in the response. The default value is 10.
@option params [String] :next_token
If the previous call to `ListTrials` didn't return the full set of trials, the call returns a token for getting the next set of trials.
@return [Types::ListTrialsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListTrialsResponse#trial_summaries #trial_summaries} => Array<Types::TrialSummary> * {Types::ListTrialsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_trials({ experiment_name: "ExperimentEntityName", trial_component_name: "ExperimentEntityName", created_after: Time.now, created_before: Time.now, sort_by: "Name", # accepts Name, CreationTime sort_order: "Ascending", # accepts Ascending, Descending max_results: 1, next_token: "NextToken", })
@example Response structure
resp.trial_summaries #=> Array resp.trial_summaries[0].trial_arn #=> String resp.trial_summaries[0].trial_name #=> String resp.trial_summaries[0].display_name #=> String resp.trial_summaries[0].trial_source.source_arn #=> String resp.trial_summaries[0].trial_source.source_type #=> String resp.trial_summaries[0].creation_time #=> Time resp.trial_summaries[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListTrials AWS API Documentation
@overload list_trials
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14882 def list_trials(params = {}, options = {}) req = build_request(:list_trials, params) req.send_request(options) end
Lists user profiles.
@option params [String] :next_token
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
@option params [Integer] :max_results
Returns a list up to a specified limit.
@option params [String] :sort_order
The sort order for the results. The default is Ascending.
@option params [String] :sort_by
The parameter by which to sort the results. The default is CreationTime.
@option params [String] :domain_id_equals
A parameter by which to filter the results.
@option params [String] :user_profile_name_contains
A parameter by which to filter the results.
@return [Types::ListUserProfilesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListUserProfilesResponse#user_profiles #user_profiles} => Array<Types::UserProfileDetails> * {Types::ListUserProfilesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_user_profiles({ next_token: "NextToken", max_results: 1, sort_order: "Ascending", # accepts Ascending, Descending sort_by: "CreationTime", # accepts CreationTime, LastModifiedTime domain_id_equals: "DomainId", user_profile_name_contains: "UserProfileName", })
@example Response structure
resp.user_profiles #=> Array resp.user_profiles[0].domain_id #=> String resp.user_profiles[0].user_profile_name #=> String resp.user_profiles[0].status #=> String, one of "Deleting", "Failed", "InService", "Pending", "Updating", "Update_Failed", "Delete_Failed" resp.user_profiles[0].creation_time #=> Time resp.user_profiles[0].last_modified_time #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListUserProfiles AWS API Documentation
@overload list_user_profiles
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 14941 def list_user_profiles(params = {}, options = {}) req = build_request(:list_user_profiles, params) req.send_request(options) end
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
@option params [String] :sort_by
Sort workforces using the workforce name or creation date.
@option params [String] :sort_order
Sort workforces in ascending or descending order.
@option params [String] :name_contains
A filter you can use to search for workforces using part of the workforce name.
@option params [String] :next_token
A token to resume pagination.
@option params [Integer] :max_results
The maximum number of workforces returned in the response.
@return [Types::ListWorkforcesResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListWorkforcesResponse#workforces #workforces} => Array<Types::Workforce> * {Types::ListWorkforcesResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_workforces({ sort_by: "Name", # accepts Name, CreateDate sort_order: "Ascending", # accepts Ascending, Descending name_contains: "WorkforceName", next_token: "NextToken", max_results: 1, })
@example Response structure
resp.workforces #=> Array resp.workforces[0].workforce_name #=> String resp.workforces[0].workforce_arn #=> String resp.workforces[0].last_updated_date #=> Time resp.workforces[0].source_ip_config.cidrs #=> Array resp.workforces[0].source_ip_config.cidrs[0] #=> String resp.workforces[0].sub_domain #=> String resp.workforces[0].cognito_config.user_pool #=> String resp.workforces[0].cognito_config.client_id #=> String resp.workforces[0].oidc_config.client_id #=> String resp.workforces[0].oidc_config.issuer #=> String resp.workforces[0].oidc_config.authorization_endpoint #=> String resp.workforces[0].oidc_config.token_endpoint #=> String resp.workforces[0].oidc_config.user_info_endpoint #=> String resp.workforces[0].oidc_config.logout_endpoint #=> String resp.workforces[0].oidc_config.jwks_uri #=> String resp.workforces[0].create_date #=> Time resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListWorkforces AWS API Documentation
@overload list_workforces
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15008 def list_workforces(params = {}, options = {}) req = build_request(:list_workforces, params) req.send_request(options) end
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the `NameContains` parameter.
@option params [String] :sort_by
The field to sort results by. The default is `CreationTime`.
@option params [String] :sort_order
The sort order for results. The default is `Ascending`.
@option params [String] :name_contains
A string in the work team's name. This filter returns only work teams whose name contains the specified string.
@option params [String] :next_token
If the result of the previous `ListWorkteams` request was truncated, the response includes a `NextToken`. To retrieve the next set of labeling jobs, use the token in the next request.
@option params [Integer] :max_results
The maximum number of work teams to return in each page of the response.
@return [Types::ListWorkteamsResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::ListWorkteamsResponse#workteams #workteams} => Array<Types::Workteam> * {Types::ListWorkteamsResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.list_workteams({ sort_by: "Name", # accepts Name, CreateDate sort_order: "Ascending", # accepts Ascending, Descending name_contains: "WorkteamName", next_token: "NextToken", max_results: 1, })
@example Response structure
resp.workteams #=> Array resp.workteams[0].workteam_name #=> String resp.workteams[0].member_definitions #=> Array resp.workteams[0].member_definitions[0].cognito_member_definition.user_pool #=> String resp.workteams[0].member_definitions[0].cognito_member_definition.user_group #=> String resp.workteams[0].member_definitions[0].cognito_member_definition.client_id #=> String resp.workteams[0].member_definitions[0].oidc_member_definition.groups #=> Array resp.workteams[0].member_definitions[0].oidc_member_definition.groups[0] #=> String resp.workteams[0].workteam_arn #=> String resp.workteams[0].workforce_arn #=> String resp.workteams[0].product_listing_ids #=> Array resp.workteams[0].product_listing_ids[0] #=> String resp.workteams[0].description #=> String resp.workteams[0].sub_domain #=> String resp.workteams[0].create_date #=> Time resp.workteams[0].last_updated_date #=> Time resp.workteams[0].notification_configuration.notification_topic_arn #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ListWorkteams AWS API Documentation
@overload list_workteams
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15078 def list_workteams(params = {}, options = {}) req = build_request(:list_workteams, params) req.send_request(options) end
Adds a resouce policy to control access to a model group. For information about resoure policies, see [Identity-based policies and resource-based policies] in the *Amazon Web Services Identity and Access Management User Guide.*.
[1]: docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html
@option params [required, String] :model_package_group_name
The name of the model group to add a resource policy to.
@option params [required, String] :resource_policy
The resource policy for the model group.
@return [Types::PutModelPackageGroupPolicyOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::PutModelPackageGroupPolicyOutput#model_package_group_arn #model_package_group_arn} => String
@example Request syntax with placeholder values
resp = client.put_model_package_group_policy({ model_package_group_name: "EntityName", # required resource_policy: "PolicyString", # required })
@example Response structure
resp.model_package_group_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/PutModelPackageGroupPolicy AWS API Documentation
@overload put_model_package_group_policy
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15117 def put_model_package_group_policy(params = {}, options = {}) req = build_request(:put_model_package_group_policy, params) req.send_request(options) end
Register devices.
@option params [required, String] :device_fleet_name
The name of the fleet.
@option params [required, Array<Types::Device>] :devices
A list of devices to register with SageMaker Edge Manager.
@option params [Array<Types::Tag>] :tags
The tags associated with devices.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.register_devices({ device_fleet_name: "EntityName", # required devices: [ # required { device_name: "DeviceName", # required description: "DeviceDescription", iot_thing_name: "ThingName", }, ], tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/RegisterDevices AWS API Documentation
@overload register_devices
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15158 def register_devices(params = {}, options = {}) req = build_request(:register_devices, params) req.send_request(options) end
Renders the UI template so that you can preview the worker's experience.
@option params [Types::UiTemplate] :ui_template
A `Template` object containing the worker UI template to render.
@option params [required, Types::RenderableTask] :task
A `RenderableTask` object containing a representative task to render.
@option params [required, String] :role_arn
The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
@option params [String] :human_task_ui_arn
The `HumanTaskUiArn` of the worker UI that you want to render. Do not provide a `HumanTaskUiArn` if you use the `UiTemplate` parameter. See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
@return [Types::RenderUiTemplateResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::RenderUiTemplateResponse#rendered_content #rendered_content} => String * {Types::RenderUiTemplateResponse#errors #errors} => Array<Types::RenderingError>
@example Request syntax with placeholder values
resp = client.render_ui_template({ ui_template: { content: "TemplateContent", # required }, task: { # required input: "TaskInput", # required }, role_arn: "RoleArn", # required human_task_ui_arn: "HumanTaskUiArn", })
@example Response structure
resp.rendered_content #=> String resp.errors #=> Array resp.errors[0].code #=> String resp.errors[0].message #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/RenderUiTemplate AWS API Documentation
@overload render_ui_template
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15212 def render_ui_template(params = {}, options = {}) req = build_request(:render_ui_template, params) req.send_request(options) end
Finds Amazon SageMaker
resources that match a search query. Matching resources are returned as a list of `SearchRecord` objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
@option params [required, String] :resource
The name of the Amazon SageMaker resource to search for.
@option params [Types::SearchExpression] :search_expression
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive `SubExpressions`, `NestedFilters`, and `Filters` that can be included in a `SearchExpression` object is 50.
@option params [String] :sort_by
The name of the resource property used to sort the `SearchResults`. The default is `LastModifiedTime`.
@option params [String] :sort_order
How `SearchResults` are ordered. Valid values are `Ascending` or `Descending`. The default is `Descending`.
@option params [String] :next_token
If more than `MaxResults` resources match the specified `SearchExpression`, the response includes a `NextToken`. The `NextToken` can be passed to the next `SearchRequest` to continue retrieving results.
@option params [Integer] :max_results
The maximum number of results to return.
@return [Types::SearchResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::SearchResponse#results #results} => Array<Types::SearchRecord> * {Types::SearchResponse#next_token #next_token} => String
The returned {Seahorse::Client::Response response} is a pageable response and is Enumerable. For details on usage see {Aws::PageableResponse PageableResponse}.
@example Request syntax with placeholder values
resp = client.search({ resource: "TrainingJob", # required, accepts TrainingJob, Experiment, ExperimentTrial, ExperimentTrialComponent, Endpoint, ModelPackage, ModelPackageGroup, Pipeline, PipelineExecution, FeatureGroup search_expression: { filters: [ { name: "ResourcePropertyName", # required operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains, Exists, NotExists, In value: "FilterValue", }, ], nested_filters: [ { nested_property_name: "ResourcePropertyName", # required filters: [ # required { name: "ResourcePropertyName", # required operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains, Exists, NotExists, In value: "FilterValue", }, ], }, ], sub_expressions: [ { # recursive SearchExpression }, ], operator: "And", # accepts And, Or }, sort_by: "ResourcePropertyName", sort_order: "Ascending", # accepts Ascending, Descending next_token: "NextToken", max_results: 1, })
@example Response structure
resp.results #=> Array resp.results[0].training_job.training_job_name #=> String resp.results[0].training_job.training_job_arn #=> String resp.results[0].training_job.tuning_job_arn #=> String resp.results[0].training_job.labeling_job_arn #=> String resp.results[0].training_job.auto_ml_job_arn #=> String resp.results[0].training_job.model_artifacts.s3_model_artifacts #=> String resp.results[0].training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.results[0].training_job.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.results[0].training_job.failure_reason #=> String resp.results[0].training_job.hyper_parameters #=> Hash resp.results[0].training_job.hyper_parameters["HyperParameterKey"] #=> String resp.results[0].training_job.algorithm_specification.training_image #=> String resp.results[0].training_job.algorithm_specification.algorithm_name #=> String resp.results[0].training_job.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File" resp.results[0].training_job.algorithm_specification.metric_definitions #=> Array resp.results[0].training_job.algorithm_specification.metric_definitions[0].name #=> String resp.results[0].training_job.algorithm_specification.metric_definitions[0].regex #=> String resp.results[0].training_job.algorithm_specification.enable_sage_maker_metrics_time_series #=> Boolean resp.results[0].training_job.role_arn #=> String resp.results[0].training_job.input_data_config #=> Array resp.results[0].training_job.input_data_config[0].channel_name #=> String resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.results[0].training_job.input_data_config[0].content_type #=> String resp.results[0].training_job.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.results[0].training_job.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.results[0].training_job.input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.results[0].training_job.input_data_config[0].shuffle_config.seed #=> Integer resp.results[0].training_job.output_data_config.kms_key_id #=> String resp.results[0].training_job.output_data_config.s3_output_path #=> String resp.results[0].training_job.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.results[0].training_job.resource_config.instance_count #=> Integer resp.results[0].training_job.resource_config.volume_size_in_gb #=> Integer resp.results[0].training_job.resource_config.volume_kms_key_id #=> String resp.results[0].training_job.vpc_config.security_group_ids #=> Array resp.results[0].training_job.vpc_config.security_group_ids[0] #=> String resp.results[0].training_job.vpc_config.subnets #=> Array resp.results[0].training_job.vpc_config.subnets[0] #=> String resp.results[0].training_job.stopping_condition.max_runtime_in_seconds #=> Integer resp.results[0].training_job.stopping_condition.max_wait_time_in_seconds #=> Integer resp.results[0].training_job.creation_time #=> Time resp.results[0].training_job.training_start_time #=> Time resp.results[0].training_job.training_end_time #=> Time resp.results[0].training_job.last_modified_time #=> Time resp.results[0].training_job.secondary_status_transitions #=> Array resp.results[0].training_job.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.results[0].training_job.secondary_status_transitions[0].start_time #=> Time resp.results[0].training_job.secondary_status_transitions[0].end_time #=> Time resp.results[0].training_job.secondary_status_transitions[0].status_message #=> String resp.results[0].training_job.final_metric_data_list #=> Array resp.results[0].training_job.final_metric_data_list[0].metric_name #=> String resp.results[0].training_job.final_metric_data_list[0].value #=> Float resp.results[0].training_job.final_metric_data_list[0].timestamp #=> Time resp.results[0].training_job.enable_network_isolation #=> Boolean resp.results[0].training_job.enable_inter_container_traffic_encryption #=> Boolean resp.results[0].training_job.enable_managed_spot_training #=> Boolean resp.results[0].training_job.checkpoint_config.s3_uri #=> String resp.results[0].training_job.checkpoint_config.local_path #=> String resp.results[0].training_job.training_time_in_seconds #=> Integer resp.results[0].training_job.billable_time_in_seconds #=> Integer resp.results[0].training_job.debug_hook_config.local_path #=> String resp.results[0].training_job.debug_hook_config.s3_output_path #=> String resp.results[0].training_job.debug_hook_config.hook_parameters #=> Hash resp.results[0].training_job.debug_hook_config.hook_parameters["ConfigKey"] #=> String resp.results[0].training_job.debug_hook_config.collection_configurations #=> Array resp.results[0].training_job.debug_hook_config.collection_configurations[0].collection_name #=> String resp.results[0].training_job.debug_hook_config.collection_configurations[0].collection_parameters #=> Hash resp.results[0].training_job.debug_hook_config.collection_configurations[0].collection_parameters["ConfigKey"] #=> String resp.results[0].training_job.experiment_config.experiment_name #=> String resp.results[0].training_job.experiment_config.trial_name #=> String resp.results[0].training_job.experiment_config.trial_component_display_name #=> String resp.results[0].training_job.debug_rule_configurations #=> Array resp.results[0].training_job.debug_rule_configurations[0].rule_configuration_name #=> String resp.results[0].training_job.debug_rule_configurations[0].local_path #=> String resp.results[0].training_job.debug_rule_configurations[0].s3_output_path #=> String resp.results[0].training_job.debug_rule_configurations[0].rule_evaluator_image #=> String resp.results[0].training_job.debug_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].training_job.debug_rule_configurations[0].volume_size_in_gb #=> Integer resp.results[0].training_job.debug_rule_configurations[0].rule_parameters #=> Hash resp.results[0].training_job.debug_rule_configurations[0].rule_parameters["ConfigKey"] #=> String resp.results[0].training_job.tensor_board_output_config.local_path #=> String resp.results[0].training_job.tensor_board_output_config.s3_output_path #=> String resp.results[0].training_job.debug_rule_evaluation_statuses #=> Array resp.results[0].training_job.debug_rule_evaluation_statuses[0].rule_configuration_name #=> String resp.results[0].training_job.debug_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String resp.results[0].training_job.debug_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped" resp.results[0].training_job.debug_rule_evaluation_statuses[0].status_details #=> String resp.results[0].training_job.debug_rule_evaluation_statuses[0].last_modified_time #=> Time resp.results[0].training_job.environment #=> Hash resp.results[0].training_job.environment["TrainingEnvironmentKey"] #=> String resp.results[0].training_job.retry_strategy.maximum_retry_attempts #=> Integer resp.results[0].training_job.tags #=> Array resp.results[0].training_job.tags[0].key #=> String resp.results[0].training_job.tags[0].value #=> String resp.results[0].experiment.experiment_name #=> String resp.results[0].experiment.experiment_arn #=> String resp.results[0].experiment.display_name #=> String resp.results[0].experiment.source.source_arn #=> String resp.results[0].experiment.source.source_type #=> String resp.results[0].experiment.description #=> String resp.results[0].experiment.creation_time #=> Time resp.results[0].experiment.created_by.user_profile_arn #=> String resp.results[0].experiment.created_by.user_profile_name #=> String resp.results[0].experiment.created_by.domain_id #=> String resp.results[0].experiment.last_modified_time #=> Time resp.results[0].experiment.last_modified_by.user_profile_arn #=> String resp.results[0].experiment.last_modified_by.user_profile_name #=> String resp.results[0].experiment.last_modified_by.domain_id #=> String resp.results[0].experiment.tags #=> Array resp.results[0].experiment.tags[0].key #=> String resp.results[0].experiment.tags[0].value #=> String resp.results[0].trial.trial_name #=> String resp.results[0].trial.trial_arn #=> String resp.results[0].trial.display_name #=> String resp.results[0].trial.experiment_name #=> String resp.results[0].trial.source.source_arn #=> String resp.results[0].trial.source.source_type #=> String resp.results[0].trial.creation_time #=> Time resp.results[0].trial.created_by.user_profile_arn #=> String resp.results[0].trial.created_by.user_profile_name #=> String resp.results[0].trial.created_by.domain_id #=> String resp.results[0].trial.last_modified_time #=> Time resp.results[0].trial.last_modified_by.user_profile_arn #=> String resp.results[0].trial.last_modified_by.user_profile_name #=> String resp.results[0].trial.last_modified_by.domain_id #=> String resp.results[0].trial.metadata_properties.commit_id #=> String resp.results[0].trial.metadata_properties.repository #=> String resp.results[0].trial.metadata_properties.generated_by #=> String resp.results[0].trial.metadata_properties.project_id #=> String resp.results[0].trial.tags #=> Array resp.results[0].trial.tags[0].key #=> String resp.results[0].trial.tags[0].value #=> String resp.results[0].trial.trial_component_summaries #=> Array resp.results[0].trial.trial_component_summaries[0].trial_component_name #=> String resp.results[0].trial.trial_component_summaries[0].trial_component_arn #=> String resp.results[0].trial.trial_component_summaries[0].trial_component_source.source_arn #=> String resp.results[0].trial.trial_component_summaries[0].trial_component_source.source_type #=> String resp.results[0].trial.trial_component_summaries[0].creation_time #=> Time resp.results[0].trial.trial_component_summaries[0].created_by.user_profile_arn #=> String resp.results[0].trial.trial_component_summaries[0].created_by.user_profile_name #=> String resp.results[0].trial.trial_component_summaries[0].created_by.domain_id #=> String resp.results[0].trial_component.trial_component_name #=> String resp.results[0].trial_component.display_name #=> String resp.results[0].trial_component.trial_component_arn #=> String resp.results[0].trial_component.source.source_arn #=> String resp.results[0].trial_component.source.source_type #=> String resp.results[0].trial_component.status.primary_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.results[0].trial_component.status.message #=> String resp.results[0].trial_component.start_time #=> Time resp.results[0].trial_component.end_time #=> Time resp.results[0].trial_component.creation_time #=> Time resp.results[0].trial_component.created_by.user_profile_arn #=> String resp.results[0].trial_component.created_by.user_profile_name #=> String resp.results[0].trial_component.created_by.domain_id #=> String resp.results[0].trial_component.last_modified_time #=> Time resp.results[0].trial_component.last_modified_by.user_profile_arn #=> String resp.results[0].trial_component.last_modified_by.user_profile_name #=> String resp.results[0].trial_component.last_modified_by.domain_id #=> String resp.results[0].trial_component.parameters #=> Hash resp.results[0].trial_component.parameters["TrialComponentKey256"].string_value #=> String resp.results[0].trial_component.parameters["TrialComponentKey256"].number_value #=> Float resp.results[0].trial_component.input_artifacts #=> Hash resp.results[0].trial_component.input_artifacts["TrialComponentKey64"].media_type #=> String resp.results[0].trial_component.input_artifacts["TrialComponentKey64"].value #=> String resp.results[0].trial_component.output_artifacts #=> Hash resp.results[0].trial_component.output_artifacts["TrialComponentKey64"].media_type #=> String resp.results[0].trial_component.output_artifacts["TrialComponentKey64"].value #=> String resp.results[0].trial_component.metrics #=> Array resp.results[0].trial_component.metrics[0].metric_name #=> String resp.results[0].trial_component.metrics[0].source_arn #=> String resp.results[0].trial_component.metrics[0].time_stamp #=> Time resp.results[0].trial_component.metrics[0].max #=> Float resp.results[0].trial_component.metrics[0].min #=> Float resp.results[0].trial_component.metrics[0].last #=> Float resp.results[0].trial_component.metrics[0].count #=> Integer resp.results[0].trial_component.metrics[0].avg #=> Float resp.results[0].trial_component.metrics[0].std_dev #=> Float resp.results[0].trial_component.metadata_properties.commit_id #=> String resp.results[0].trial_component.metadata_properties.repository #=> String resp.results[0].trial_component.metadata_properties.generated_by #=> String resp.results[0].trial_component.metadata_properties.project_id #=> String resp.results[0].trial_component.source_detail.source_arn #=> String resp.results[0].trial_component.source_detail.training_job.training_job_name #=> String resp.results[0].trial_component.source_detail.training_job.training_job_arn #=> String resp.results[0].trial_component.source_detail.training_job.tuning_job_arn #=> String resp.results[0].trial_component.source_detail.training_job.labeling_job_arn #=> String resp.results[0].trial_component.source_detail.training_job.auto_ml_job_arn #=> String resp.results[0].trial_component.source_detail.training_job.model_artifacts.s3_model_artifacts #=> String resp.results[0].trial_component.source_detail.training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.results[0].trial_component.source_detail.training_job.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.results[0].trial_component.source_detail.training_job.failure_reason #=> String resp.results[0].trial_component.source_detail.training_job.hyper_parameters #=> Hash resp.results[0].trial_component.source_detail.training_job.hyper_parameters["HyperParameterKey"] #=> String resp.results[0].trial_component.source_detail.training_job.algorithm_specification.training_image #=> String resp.results[0].trial_component.source_detail.training_job.algorithm_specification.algorithm_name #=> String resp.results[0].trial_component.source_detail.training_job.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File" resp.results[0].trial_component.source_detail.training_job.algorithm_specification.metric_definitions #=> Array resp.results[0].trial_component.source_detail.training_job.algorithm_specification.metric_definitions[0].name #=> String resp.results[0].trial_component.source_detail.training_job.algorithm_specification.metric_definitions[0].regex #=> String resp.results[0].trial_component.source_detail.training_job.algorithm_specification.enable_sage_maker_metrics_time_series #=> Boolean resp.results[0].trial_component.source_detail.training_job.role_arn #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config #=> Array resp.results[0].trial_component.source_detail.training_job.input_data_config[0].channel_name #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.s3_data_source.s3_uri #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].data_source.file_system_data_source.directory_path #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].content_type #=> String resp.results[0].trial_component.source_detail.training_job.input_data_config[0].compression_type #=> String, one of "None", "Gzip" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].input_mode #=> String, one of "Pipe", "File" resp.results[0].trial_component.source_detail.training_job.input_data_config[0].shuffle_config.seed #=> Integer resp.results[0].trial_component.source_detail.training_job.output_data_config.kms_key_id #=> String resp.results[0].trial_component.source_detail.training_job.output_data_config.s3_output_path #=> String resp.results[0].trial_component.source_detail.training_job.resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4d.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge" resp.results[0].trial_component.source_detail.training_job.resource_config.instance_count #=> Integer resp.results[0].trial_component.source_detail.training_job.resource_config.volume_size_in_gb #=> Integer resp.results[0].trial_component.source_detail.training_job.resource_config.volume_kms_key_id #=> String resp.results[0].trial_component.source_detail.training_job.vpc_config.security_group_ids #=> Array resp.results[0].trial_component.source_detail.training_job.vpc_config.security_group_ids[0] #=> String resp.results[0].trial_component.source_detail.training_job.vpc_config.subnets #=> Array resp.results[0].trial_component.source_detail.training_job.vpc_config.subnets[0] #=> String resp.results[0].trial_component.source_detail.training_job.stopping_condition.max_runtime_in_seconds #=> Integer resp.results[0].trial_component.source_detail.training_job.stopping_condition.max_wait_time_in_seconds #=> Integer resp.results[0].trial_component.source_detail.training_job.creation_time #=> Time resp.results[0].trial_component.source_detail.training_job.training_start_time #=> Time resp.results[0].trial_component.source_detail.training_job.training_end_time #=> Time resp.results[0].trial_component.source_detail.training_job.last_modified_time #=> Time resp.results[0].trial_component.source_detail.training_job.secondary_status_transitions #=> Array resp.results[0].trial_component.source_detail.training_job.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" resp.results[0].trial_component.source_detail.training_job.secondary_status_transitions[0].start_time #=> Time resp.results[0].trial_component.source_detail.training_job.secondary_status_transitions[0].end_time #=> Time resp.results[0].trial_component.source_detail.training_job.secondary_status_transitions[0].status_message #=> String resp.results[0].trial_component.source_detail.training_job.final_metric_data_list #=> Array resp.results[0].trial_component.source_detail.training_job.final_metric_data_list[0].metric_name #=> String resp.results[0].trial_component.source_detail.training_job.final_metric_data_list[0].value #=> Float resp.results[0].trial_component.source_detail.training_job.final_metric_data_list[0].timestamp #=> Time resp.results[0].trial_component.source_detail.training_job.enable_network_isolation #=> Boolean resp.results[0].trial_component.source_detail.training_job.enable_inter_container_traffic_encryption #=> Boolean resp.results[0].trial_component.source_detail.training_job.enable_managed_spot_training #=> Boolean resp.results[0].trial_component.source_detail.training_job.checkpoint_config.s3_uri #=> String resp.results[0].trial_component.source_detail.training_job.checkpoint_config.local_path #=> String resp.results[0].trial_component.source_detail.training_job.training_time_in_seconds #=> Integer resp.results[0].trial_component.source_detail.training_job.billable_time_in_seconds #=> Integer resp.results[0].trial_component.source_detail.training_job.debug_hook_config.local_path #=> String resp.results[0].trial_component.source_detail.training_job.debug_hook_config.s3_output_path #=> String resp.results[0].trial_component.source_detail.training_job.debug_hook_config.hook_parameters #=> Hash resp.results[0].trial_component.source_detail.training_job.debug_hook_config.hook_parameters["ConfigKey"] #=> String resp.results[0].trial_component.source_detail.training_job.debug_hook_config.collection_configurations #=> Array resp.results[0].trial_component.source_detail.training_job.debug_hook_config.collection_configurations[0].collection_name #=> String resp.results[0].trial_component.source_detail.training_job.debug_hook_config.collection_configurations[0].collection_parameters #=> Hash resp.results[0].trial_component.source_detail.training_job.debug_hook_config.collection_configurations[0].collection_parameters["ConfigKey"] #=> String resp.results[0].trial_component.source_detail.training_job.experiment_config.experiment_name #=> String resp.results[0].trial_component.source_detail.training_job.experiment_config.trial_name #=> String resp.results[0].trial_component.source_detail.training_job.experiment_config.trial_component_display_name #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations #=> Array resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].rule_configuration_name #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].local_path #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].s3_output_path #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].rule_evaluator_image #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].volume_size_in_gb #=> Integer resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].rule_parameters #=> Hash resp.results[0].trial_component.source_detail.training_job.debug_rule_configurations[0].rule_parameters["ConfigKey"] #=> String resp.results[0].trial_component.source_detail.training_job.tensor_board_output_config.local_path #=> String resp.results[0].trial_component.source_detail.training_job.tensor_board_output_config.s3_output_path #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses #=> Array resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses[0].rule_configuration_name #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses[0].rule_evaluation_job_arn #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses[0].rule_evaluation_status #=> String, one of "InProgress", "NoIssuesFound", "IssuesFound", "Error", "Stopping", "Stopped" resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses[0].status_details #=> String resp.results[0].trial_component.source_detail.training_job.debug_rule_evaluation_statuses[0].last_modified_time #=> Time resp.results[0].trial_component.source_detail.training_job.environment #=> Hash resp.results[0].trial_component.source_detail.training_job.environment["TrainingEnvironmentKey"] #=> String resp.results[0].trial_component.source_detail.training_job.retry_strategy.maximum_retry_attempts #=> Integer resp.results[0].trial_component.source_detail.training_job.tags #=> Array resp.results[0].trial_component.source_detail.training_job.tags[0].key #=> String resp.results[0].trial_component.source_detail.training_job.tags[0].value #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs #=> Array resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].input_name #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].app_managed #=> Boolean resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.s3_uri #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.local_path #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.s3_data_type #=> String, one of "ManifestFile", "S3Prefix" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.s3_input_mode #=> String, one of "Pipe", "File" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].s3_input.s3_compression_type #=> String, one of "None", "Gzip" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.catalog #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.database #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.query_string #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.work_group #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.output_s3_uri #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.kms_key_id #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.output_format #=> String, one of "PARQUET", "ORC", "AVRO", "JSON", "TEXTFILE" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.athena_dataset_definition.output_compression #=> String, one of "GZIP", "SNAPPY", "ZLIB" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_id #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.database #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.db_user #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.query_string #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.cluster_role_arn #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_s3_uri #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.kms_key_id #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_format #=> String, one of "PARQUET", "CSV" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.redshift_dataset_definition.output_compression #=> String, one of "None", "GZIP", "BZIP2", "ZSTD", "SNAPPY" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.local_path #=> String resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.results[0].trial_component.source_detail.processing_job.processing_inputs[0].dataset_definition.input_mode #=> String, one of "Pipe", "File" resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs #=> Array resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].output_name #=> String resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].s3_output.s3_uri #=> String resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].s3_output.local_path #=> String resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].feature_store_output.feature_group_name #=> String resp.results[0].trial_component.source_detail.processing_job.processing_output_config.outputs[0].app_managed #=> Boolean resp.results[0].trial_component.source_detail.processing_job.processing_output_config.kms_key_id #=> String resp.results[0].trial_component.source_detail.processing_job.processing_job_name #=> String resp.results[0].trial_component.source_detail.processing_job.processing_resources.cluster_config.instance_count #=> Integer resp.results[0].trial_component.source_detail.processing_job.processing_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].trial_component.source_detail.processing_job.processing_resources.cluster_config.volume_size_in_gb #=> Integer resp.results[0].trial_component.source_detail.processing_job.processing_resources.cluster_config.volume_kms_key_id #=> String resp.results[0].trial_component.source_detail.processing_job.stopping_condition.max_runtime_in_seconds #=> Integer resp.results[0].trial_component.source_detail.processing_job.app_specification.image_uri #=> String resp.results[0].trial_component.source_detail.processing_job.app_specification.container_entrypoint #=> Array resp.results[0].trial_component.source_detail.processing_job.app_specification.container_entrypoint[0] #=> String resp.results[0].trial_component.source_detail.processing_job.app_specification.container_arguments #=> Array resp.results[0].trial_component.source_detail.processing_job.app_specification.container_arguments[0] #=> String resp.results[0].trial_component.source_detail.processing_job.environment #=> Hash resp.results[0].trial_component.source_detail.processing_job.environment["ProcessingEnvironmentKey"] #=> String resp.results[0].trial_component.source_detail.processing_job.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.results[0].trial_component.source_detail.processing_job.network_config.enable_network_isolation #=> Boolean resp.results[0].trial_component.source_detail.processing_job.network_config.vpc_config.security_group_ids #=> Array resp.results[0].trial_component.source_detail.processing_job.network_config.vpc_config.security_group_ids[0] #=> String resp.results[0].trial_component.source_detail.processing_job.network_config.vpc_config.subnets #=> Array resp.results[0].trial_component.source_detail.processing_job.network_config.vpc_config.subnets[0] #=> String resp.results[0].trial_component.source_detail.processing_job.role_arn #=> String resp.results[0].trial_component.source_detail.processing_job.experiment_config.experiment_name #=> String resp.results[0].trial_component.source_detail.processing_job.experiment_config.trial_name #=> String resp.results[0].trial_component.source_detail.processing_job.experiment_config.trial_component_display_name #=> String resp.results[0].trial_component.source_detail.processing_job.processing_job_arn #=> String resp.results[0].trial_component.source_detail.processing_job.processing_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.results[0].trial_component.source_detail.processing_job.exit_message #=> String resp.results[0].trial_component.source_detail.processing_job.failure_reason #=> String resp.results[0].trial_component.source_detail.processing_job.processing_end_time #=> Time resp.results[0].trial_component.source_detail.processing_job.processing_start_time #=> Time resp.results[0].trial_component.source_detail.processing_job.last_modified_time #=> Time resp.results[0].trial_component.source_detail.processing_job.creation_time #=> Time resp.results[0].trial_component.source_detail.processing_job.monitoring_schedule_arn #=> String resp.results[0].trial_component.source_detail.processing_job.auto_ml_job_arn #=> String resp.results[0].trial_component.source_detail.processing_job.training_job_arn #=> String resp.results[0].trial_component.source_detail.processing_job.tags #=> Array resp.results[0].trial_component.source_detail.processing_job.tags[0].key #=> String resp.results[0].trial_component.source_detail.processing_job.tags[0].value #=> String resp.results[0].trial_component.source_detail.transform_job.transform_job_name #=> String resp.results[0].trial_component.source_detail.transform_job.transform_job_arn #=> String resp.results[0].trial_component.source_detail.transform_job.transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped" resp.results[0].trial_component.source_detail.transform_job.failure_reason #=> String resp.results[0].trial_component.source_detail.transform_job.model_name #=> String resp.results[0].trial_component.source_detail.transform_job.max_concurrent_transforms #=> Integer resp.results[0].trial_component.source_detail.transform_job.model_client_config.invocations_timeout_in_seconds #=> Integer resp.results[0].trial_component.source_detail.transform_job.model_client_config.invocations_max_retries #=> Integer resp.results[0].trial_component.source_detail.transform_job.max_payload_in_mb #=> Integer resp.results[0].trial_component.source_detail.transform_job.batch_strategy #=> String, one of "MultiRecord", "SingleRecord" resp.results[0].trial_component.source_detail.transform_job.environment #=> Hash resp.results[0].trial_component.source_detail.transform_job.environment["TransformEnvironmentKey"] #=> String resp.results[0].trial_component.source_detail.transform_job.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.results[0].trial_component.source_detail.transform_job.transform_input.data_source.s3_data_source.s3_uri #=> String resp.results[0].trial_component.source_detail.transform_job.transform_input.content_type #=> String resp.results[0].trial_component.source_detail.transform_job.transform_input.compression_type #=> String, one of "None", "Gzip" resp.results[0].trial_component.source_detail.transform_job.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord" resp.results[0].trial_component.source_detail.transform_job.transform_output.s3_output_path #=> String resp.results[0].trial_component.source_detail.transform_job.transform_output.accept #=> String resp.results[0].trial_component.source_detail.transform_job.transform_output.assemble_with #=> String, one of "None", "Line" resp.results[0].trial_component.source_detail.transform_job.transform_output.kms_key_id #=> String resp.results[0].trial_component.source_detail.transform_job.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].trial_component.source_detail.transform_job.transform_resources.instance_count #=> Integer resp.results[0].trial_component.source_detail.transform_job.transform_resources.volume_kms_key_id #=> String resp.results[0].trial_component.source_detail.transform_job.creation_time #=> Time resp.results[0].trial_component.source_detail.transform_job.transform_start_time #=> Time resp.results[0].trial_component.source_detail.transform_job.transform_end_time #=> Time resp.results[0].trial_component.source_detail.transform_job.labeling_job_arn #=> String resp.results[0].trial_component.source_detail.transform_job.auto_ml_job_arn #=> String resp.results[0].trial_component.source_detail.transform_job.data_processing.input_filter #=> String resp.results[0].trial_component.source_detail.transform_job.data_processing.output_filter #=> String resp.results[0].trial_component.source_detail.transform_job.data_processing.join_source #=> String, one of "Input", "None" resp.results[0].trial_component.source_detail.transform_job.experiment_config.experiment_name #=> String resp.results[0].trial_component.source_detail.transform_job.experiment_config.trial_name #=> String resp.results[0].trial_component.source_detail.transform_job.experiment_config.trial_component_display_name #=> String resp.results[0].trial_component.source_detail.transform_job.tags #=> Array resp.results[0].trial_component.source_detail.transform_job.tags[0].key #=> String resp.results[0].trial_component.source_detail.transform_job.tags[0].value #=> String resp.results[0].trial_component.tags #=> Array resp.results[0].trial_component.tags[0].key #=> String resp.results[0].trial_component.tags[0].value #=> String resp.results[0].trial_component.parents #=> Array resp.results[0].trial_component.parents[0].trial_name #=> String resp.results[0].trial_component.parents[0].experiment_name #=> String resp.results[0].endpoint.endpoint_name #=> String resp.results[0].endpoint.endpoint_arn #=> String resp.results[0].endpoint.endpoint_config_name #=> String resp.results[0].endpoint.production_variants #=> Array resp.results[0].endpoint.production_variants[0].variant_name #=> String resp.results[0].endpoint.production_variants[0].deployed_images #=> Array resp.results[0].endpoint.production_variants[0].deployed_images[0].specified_image #=> String resp.results[0].endpoint.production_variants[0].deployed_images[0].resolved_image #=> String resp.results[0].endpoint.production_variants[0].deployed_images[0].resolution_time #=> Time resp.results[0].endpoint.production_variants[0].current_weight #=> Float resp.results[0].endpoint.production_variants[0].desired_weight #=> Float resp.results[0].endpoint.production_variants[0].current_instance_count #=> Integer resp.results[0].endpoint.production_variants[0].desired_instance_count #=> Integer resp.results[0].endpoint.data_capture_config.enable_capture #=> Boolean resp.results[0].endpoint.data_capture_config.capture_status #=> String, one of "Started", "Stopped" resp.results[0].endpoint.data_capture_config.current_sampling_percentage #=> Integer resp.results[0].endpoint.data_capture_config.destination_s3_uri #=> String resp.results[0].endpoint.data_capture_config.kms_key_id #=> String resp.results[0].endpoint.endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed" resp.results[0].endpoint.failure_reason #=> String resp.results[0].endpoint.creation_time #=> Time resp.results[0].endpoint.last_modified_time #=> Time resp.results[0].endpoint.monitoring_schedules #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_arn #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_name #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_status #=> String, one of "Pending", "Failed", "Scheduled", "Stopped" resp.results[0].endpoint.monitoring_schedules[0].monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.results[0].endpoint.monitoring_schedules[0].failure_reason #=> String resp.results[0].endpoint.monitoring_schedules[0].creation_time #=> Time resp.results[0].endpoint.monitoring_schedules[0].last_modified_time #=> Time resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.schedule_config.schedule_expression #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.baseline_config.baselining_job_name #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.baseline_config.constraints_resource.s3_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.baseline_config.statistics_resource.s3_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.endpoint_name #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.local_path #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_input_mode #=> String, one of "Pipe", "File" resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key" resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.features_attribute #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.inference_attribute #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_attribute #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.probability_threshold_attribute #=> Float resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.start_time_offset #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[0].endpoint_input.end_time_offset #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.local_path #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[0].s3_output.s3_upload_mode #=> String, one of "Continuous", "EndOfJob" resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.kms_key_id #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_count #=> Integer resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_type #=> String, one of "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.8xlarge", "ml.r5.12xlarge", "ml.r5.16xlarge", "ml.r5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_size_in_gb #=> Integer resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_kms_key_id #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.image_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint[0] #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments[0] #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.record_preprocessor_source_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.post_analytics_processor_source_uri #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.environment #=> Hash resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.environment["ProcessingEnvironmentKey"] #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.enable_inter_container_traffic_encryption #=> Boolean resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.enable_network_isolation #=> Boolean resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids[0] #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets #=> Array resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets[0] #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition.role_arn #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_job_definition_name #=> String resp.results[0].endpoint.monitoring_schedules[0].monitoring_schedule_config.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.results[0].endpoint.monitoring_schedules[0].endpoint_name #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.monitoring_schedule_name #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.scheduled_time #=> Time resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.creation_time #=> Time resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.last_modified_time #=> Time resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.monitoring_execution_status #=> String, one of "Pending", "Completed", "CompletedWithViolations", "InProgress", "Failed", "Stopping", "Stopped" resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.processing_job_arn #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.endpoint_name #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.failure_reason #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.monitoring_job_definition_name #=> String resp.results[0].endpoint.monitoring_schedules[0].last_monitoring_execution_summary.monitoring_type #=> String, one of "DataQuality", "ModelQuality", "ModelBias", "ModelExplainability" resp.results[0].endpoint.monitoring_schedules[0].tags #=> Array resp.results[0].endpoint.monitoring_schedules[0].tags[0].key #=> String resp.results[0].endpoint.monitoring_schedules[0].tags[0].value #=> String resp.results[0].endpoint.tags #=> Array resp.results[0].endpoint.tags[0].key #=> String resp.results[0].endpoint.tags[0].value #=> String resp.results[0].model_package.model_package_name #=> String resp.results[0].model_package.model_package_group_name #=> String resp.results[0].model_package.model_package_version #=> Integer resp.results[0].model_package.model_package_arn #=> String resp.results[0].model_package.model_package_description #=> String resp.results[0].model_package.creation_time #=> Time resp.results[0].model_package.inference_specification.containers #=> Array resp.results[0].model_package.inference_specification.containers[0].container_hostname #=> String resp.results[0].model_package.inference_specification.containers[0].image #=> String resp.results[0].model_package.inference_specification.containers[0].image_digest #=> String resp.results[0].model_package.inference_specification.containers[0].model_data_url #=> String resp.results[0].model_package.inference_specification.containers[0].product_id #=> String resp.results[0].model_package.inference_specification.containers[0].environment #=> Hash resp.results[0].model_package.inference_specification.containers[0].environment["EnvironmentKey"] #=> String resp.results[0].model_package.inference_specification.supported_transform_instance_types #=> Array resp.results[0].model_package.inference_specification.supported_transform_instance_types[0] #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].model_package.inference_specification.supported_realtime_inference_instance_types #=> Array resp.results[0].model_package.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.24xlarge", "ml.c4.large", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge" resp.results[0].model_package.inference_specification.supported_content_types #=> Array resp.results[0].model_package.inference_specification.supported_content_types[0] #=> String resp.results[0].model_package.inference_specification.supported_response_mime_types #=> Array resp.results[0].model_package.inference_specification.supported_response_mime_types[0] #=> String resp.results[0].model_package.source_algorithm_specification.source_algorithms #=> Array resp.results[0].model_package.source_algorithm_specification.source_algorithms[0].model_data_url #=> String resp.results[0].model_package.source_algorithm_specification.source_algorithms[0].algorithm_name #=> String resp.results[0].model_package.validation_specification.validation_role #=> String resp.results[0].model_package.validation_specification.validation_profiles #=> Array resp.results[0].model_package.validation_specification.validation_profiles[0].profile_name #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.c4.xlarge", "ml.c4.2xlarge", "ml.c4.4xlarge", "ml.c4.8xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.12xlarge", "ml.m5.24xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge" resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer resp.results[0].model_package.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String resp.results[0].model_package.model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting" resp.results[0].model_package.model_package_status_details.validation_statuses #=> Array resp.results[0].model_package.model_package_status_details.validation_statuses[0].name #=> String resp.results[0].model_package.model_package_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.results[0].model_package.model_package_status_details.validation_statuses[0].failure_reason #=> String resp.results[0].model_package.model_package_status_details.image_scan_statuses #=> Array resp.results[0].model_package.model_package_status_details.image_scan_statuses[0].name #=> String resp.results[0].model_package.model_package_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed" resp.results[0].model_package.model_package_status_details.image_scan_statuses[0].failure_reason #=> String resp.results[0].model_package.certify_for_marketplace #=> Boolean resp.results[0].model_package.model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval" resp.results[0].model_package.created_by.user_profile_arn #=> String resp.results[0].model_package.created_by.user_profile_name #=> String resp.results[0].model_package.created_by.domain_id #=> String resp.results[0].model_package.metadata_properties.commit_id #=> String resp.results[0].model_package.metadata_properties.repository #=> String resp.results[0].model_package.metadata_properties.generated_by #=> String resp.results[0].model_package.metadata_properties.project_id #=> String resp.results[0].model_package.model_metrics.model_quality.statistics.content_type #=> String resp.results[0].model_package.model_metrics.model_quality.statistics.content_digest #=> String resp.results[0].model_package.model_metrics.model_quality.statistics.s3_uri #=> String resp.results[0].model_package.model_metrics.model_quality.constraints.content_type #=> String resp.results[0].model_package.model_metrics.model_quality.constraints.content_digest #=> String resp.results[0].model_package.model_metrics.model_quality.constraints.s3_uri #=> String resp.results[0].model_package.model_metrics.model_data_quality.statistics.content_type #=> String resp.results[0].model_package.model_metrics.model_data_quality.statistics.content_digest #=> String resp.results[0].model_package.model_metrics.model_data_quality.statistics.s3_uri #=> String resp.results[0].model_package.model_metrics.model_data_quality.constraints.content_type #=> String resp.results[0].model_package.model_metrics.model_data_quality.constraints.content_digest #=> String resp.results[0].model_package.model_metrics.model_data_quality.constraints.s3_uri #=> String resp.results[0].model_package.model_metrics.bias.report.content_type #=> String resp.results[0].model_package.model_metrics.bias.report.content_digest #=> String resp.results[0].model_package.model_metrics.bias.report.s3_uri #=> String resp.results[0].model_package.model_metrics.explainability.report.content_type #=> String resp.results[0].model_package.model_metrics.explainability.report.content_digest #=> String resp.results[0].model_package.model_metrics.explainability.report.s3_uri #=> String resp.results[0].model_package.last_modified_time #=> Time resp.results[0].model_package.last_modified_by.user_profile_arn #=> String resp.results[0].model_package.last_modified_by.user_profile_name #=> String resp.results[0].model_package.last_modified_by.domain_id #=> String resp.results[0].model_package.approval_description #=> String resp.results[0].model_package.tags #=> Array resp.results[0].model_package.tags[0].key #=> String resp.results[0].model_package.tags[0].value #=> String resp.results[0].model_package_group.model_package_group_name #=> String resp.results[0].model_package_group.model_package_group_arn #=> String resp.results[0].model_package_group.model_package_group_description #=> String resp.results[0].model_package_group.creation_time #=> Time resp.results[0].model_package_group.created_by.user_profile_arn #=> String resp.results[0].model_package_group.created_by.user_profile_name #=> String resp.results[0].model_package_group.created_by.domain_id #=> String resp.results[0].model_package_group.model_package_group_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting", "DeleteFailed" resp.results[0].model_package_group.tags #=> Array resp.results[0].model_package_group.tags[0].key #=> String resp.results[0].model_package_group.tags[0].value #=> String resp.results[0].pipeline.pipeline_arn #=> String resp.results[0].pipeline.pipeline_name #=> String resp.results[0].pipeline.pipeline_display_name #=> String resp.results[0].pipeline.pipeline_description #=> String resp.results[0].pipeline.role_arn #=> String resp.results[0].pipeline.pipeline_status #=> String, one of "Active" resp.results[0].pipeline.creation_time #=> Time resp.results[0].pipeline.last_modified_time #=> Time resp.results[0].pipeline.last_run_time #=> Time resp.results[0].pipeline.created_by.user_profile_arn #=> String resp.results[0].pipeline.created_by.user_profile_name #=> String resp.results[0].pipeline.created_by.domain_id #=> String resp.results[0].pipeline.last_modified_by.user_profile_arn #=> String resp.results[0].pipeline.last_modified_by.user_profile_name #=> String resp.results[0].pipeline.last_modified_by.domain_id #=> String resp.results[0].pipeline.tags #=> Array resp.results[0].pipeline.tags[0].key #=> String resp.results[0].pipeline.tags[0].value #=> String resp.results[0].pipeline_execution.pipeline_arn #=> String resp.results[0].pipeline_execution.pipeline_execution_arn #=> String resp.results[0].pipeline_execution.pipeline_execution_display_name #=> String resp.results[0].pipeline_execution.pipeline_execution_status #=> String, one of "Executing", "Stopping", "Stopped", "Failed", "Succeeded" resp.results[0].pipeline_execution.pipeline_execution_description #=> String resp.results[0].pipeline_execution.pipeline_experiment_config.experiment_name #=> String resp.results[0].pipeline_execution.pipeline_experiment_config.trial_name #=> String resp.results[0].pipeline_execution.failure_reason #=> String resp.results[0].pipeline_execution.creation_time #=> Time resp.results[0].pipeline_execution.last_modified_time #=> Time resp.results[0].pipeline_execution.created_by.user_profile_arn #=> String resp.results[0].pipeline_execution.created_by.user_profile_name #=> String resp.results[0].pipeline_execution.created_by.domain_id #=> String resp.results[0].pipeline_execution.last_modified_by.user_profile_arn #=> String resp.results[0].pipeline_execution.last_modified_by.user_profile_name #=> String resp.results[0].pipeline_execution.last_modified_by.domain_id #=> String resp.results[0].pipeline_execution.pipeline_parameters #=> Array resp.results[0].pipeline_execution.pipeline_parameters[0].name #=> String resp.results[0].pipeline_execution.pipeline_parameters[0].value #=> String resp.results[0].feature_group.feature_group_arn #=> String resp.results[0].feature_group.feature_group_name #=> String resp.results[0].feature_group.record_identifier_feature_name #=> String resp.results[0].feature_group.event_time_feature_name #=> String resp.results[0].feature_group.feature_definitions #=> Array resp.results[0].feature_group.feature_definitions[0].feature_name #=> String resp.results[0].feature_group.feature_definitions[0].feature_type #=> String, one of "Integral", "Fractional", "String" resp.results[0].feature_group.creation_time #=> Time resp.results[0].feature_group.online_store_config.security_config.kms_key_id #=> String resp.results[0].feature_group.online_store_config.enable_online_store #=> Boolean resp.results[0].feature_group.offline_store_config.s3_storage_config.s3_uri #=> String resp.results[0].feature_group.offline_store_config.s3_storage_config.kms_key_id #=> String resp.results[0].feature_group.offline_store_config.s3_storage_config.resolved_output_s3_uri #=> String resp.results[0].feature_group.offline_store_config.disable_glue_table_creation #=> Boolean resp.results[0].feature_group.offline_store_config.data_catalog_config.table_name #=> String resp.results[0].feature_group.offline_store_config.data_catalog_config.catalog #=> String resp.results[0].feature_group.offline_store_config.data_catalog_config.database #=> String resp.results[0].feature_group.role_arn #=> String resp.results[0].feature_group.feature_group_status #=> String, one of "Creating", "Created", "CreateFailed", "Deleting", "DeleteFailed" resp.results[0].feature_group.offline_store_status.status #=> String, one of "Active", "Blocked", "Disabled" resp.results[0].feature_group.offline_store_status.blocked_reason #=> String resp.results[0].feature_group.failure_reason #=> String resp.results[0].feature_group.description #=> String resp.results[0].feature_group.tags #=> Array resp.results[0].feature_group.tags[0].key #=> String resp.results[0].feature_group.tags[0].value #=> String resp.next_token #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/Search AWS API Documentation
@overload search(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 15966 def search(params = {}, options = {}) req = build_request(:search, params) req.send_request(options) end
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
@option params [required, String] :callback_token
The pipeline generated token from the Amazon SQS queue.
@option params [String] :failure_reason
A message describing why the step failed.
@option params [String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::SendPipelineExecutionStepFailureResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::SendPipelineExecutionStepFailureResponse#pipeline_execution_arn #pipeline_execution_arn} => String
@example Request syntax with placeholder values
resp = client.send_pipeline_execution_step_failure({ callback_token: "CallbackToken", # required failure_reason: "String256", client_request_token: "IdempotencyToken", })
@example Response structure
resp.pipeline_execution_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/SendPipelineExecutionStepFailure AWS API Documentation
@overload send_pipeline_execution_step_failure
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16010 def send_pipeline_execution_step_failure(params = {}, options = {}) req = build_request(:send_pipeline_execution_step_failure, params) req.send_request(options) end
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
@option params [required, String] :callback_token
The pipeline generated token from the Amazon SQS queue.
@option params [Array<Types::OutputParameter>] :output_parameters
A list of the output parameters of the callback step.
@option params [String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::SendPipelineExecutionStepSuccessResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::SendPipelineExecutionStepSuccessResponse#pipeline_execution_arn #pipeline_execution_arn} => String
@example Request syntax with placeholder values
resp = client.send_pipeline_execution_step_success({ callback_token: "CallbackToken", # required output_parameters: [ { name: "String256", # required value: "String1024", # required }, ], client_request_token: "IdempotencyToken", })
@example Response structure
resp.pipeline_execution_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/SendPipelineExecutionStepSuccess AWS API Documentation
@overload send_pipeline_execution_step_success
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16059 def send_pipeline_execution_step_success(params = {}, options = {}) req = build_request(:send_pipeline_execution_step_success, params) req.send_request(options) end
Starts a previously stopped monitoring schedule.
<note markdown=“1”> By default, when you successfully create a new schedule, the status of a monitoring schedule is `scheduled`.
</note>
@option params [required, String] :monitoring_schedule_name
The name of the schedule to start.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.start_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StartMonitoringSchedule AWS API Documentation
@overload start_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16086 def start_monitoring_schedule(params = {}, options = {}) req = build_request(:start_monitoring_schedule, params) req.send_request(options) end
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker
sets the notebook instance status to `InService`. A notebook instance's status must be `InService` before you can connect to your Jupyter notebook.
@option params [required, String] :notebook_instance_name
The name of the notebook instance to start.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.start_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StartNotebookInstance AWS API Documentation
@overload start_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16112 def start_notebook_instance(params = {}, options = {}) req = build_request(:start_notebook_instance, params) req.send_request(options) end
Starts a pipeline execution.
@option params [required, String] :pipeline_name
The name of the pipeline.
@option params [String] :pipeline_execution_display_name
The display name of the pipeline execution.
@option params [Array<Types::Parameter>] :pipeline_parameters
Contains a list of pipeline parameters. This list can be empty.
@option params [String] :pipeline_execution_description
The description of the pipeline execution.
@option params [required, String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::StartPipelineExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::StartPipelineExecutionResponse#pipeline_execution_arn #pipeline_execution_arn} => String
@example Request syntax with placeholder values
resp = client.start_pipeline_execution({ pipeline_name: "PipelineName", # required pipeline_execution_display_name: "PipelineExecutionName", pipeline_parameters: [ { name: "PipelineParameterName", # required value: "String1024", # required }, ], pipeline_execution_description: "PipelineExecutionDescription", client_request_token: "IdempotencyToken", # required })
@example Response structure
resp.pipeline_execution_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StartPipelineExecution AWS API Documentation
@overload start_pipeline_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16166 def start_pipeline_execution(params = {}, options = {}) req = build_request(:start_pipeline_execution, params) req.send_request(options) end
A method for forcing the termination of a running job.
@option params [required, String] :auto_ml_job_name
The name of the object you are requesting.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_auto_ml_job({ auto_ml_job_name: "AutoMLJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopAutoMLJob AWS API Documentation
@overload stop_auto_ml_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16188 def stop_auto_ml_job(params = {}, options = {}) req = build_request(:stop_auto_ml_job, params) req.send_request(options) end
Stops a model compilation job.
To stop a job, Amazon SageMaker
sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a `StopCompilationJob` request, Amazon SageMaker
changes the CompilationJobSummary$CompilationJobStatus of the job to `Stopping`. After Amazon SageMaker
stops the job, it sets the CompilationJobSummary$CompilationJobStatus to `Stopped`.
@option params [required, String] :compilation_job_name
The name of the model compilation job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_compilation_job({ compilation_job_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopCompilationJob AWS API Documentation
@overload stop_compilation_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16219 def stop_compilation_job(params = {}, options = {}) req = build_request(:stop_compilation_job, params) req.send_request(options) end
Request to stop an edge packaging job.
@option params [required, String] :edge_packaging_job_name
The name of the edge packaging job.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_edge_packaging_job({ edge_packaging_job_name: "EntityName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopEdgePackagingJob AWS API Documentation
@overload stop_edge_packaging_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16241 def stop_edge_packaging_job(params = {}, options = {}) req = build_request(:stop_edge_packaging_job, params) req.send_request(options) end
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the `Stopped` state, it releases all reserved resources for the tuning job.
@option params [required, String] :hyper_parameter_tuning_job_name
The name of the tuning job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_hyper_parameter_tuning_job({ hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopHyperParameterTuningJob AWS API Documentation
@overload stop_hyper_parameter_tuning_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16270 def stop_hyper_parameter_tuning_job(params = {}, options = {}) req = build_request(:stop_hyper_parameter_tuning_job, params) req.send_request(options) end
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
@option params [required, String] :labeling_job_name
The name of the labeling job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_labeling_job({ labeling_job_name: "LabelingJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopLabelingJob AWS API Documentation
@overload stop_labeling_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16294 def stop_labeling_job(params = {}, options = {}) req = build_request(:stop_labeling_job, params) req.send_request(options) end
Stops a previously started monitoring schedule.
@option params [required, String] :monitoring_schedule_name
The name of the schedule to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopMonitoringSchedule AWS API Documentation
@overload stop_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16316 def stop_monitoring_schedule(params = {}, options = {}) req = build_request(:stop_monitoring_schedule, params) req.send_request(options) end
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker
disconnects the ML storage volume from it. Amazon SageMaker
preserves the ML storage volume. Amazon SageMaker
stops charging you for the ML compute instance when you call `StopNotebookInstance`.
To access data on the ML storage volume for a notebook instance that has been terminated, call the `StartNotebookInstance` API. `StartNotebookInstance` launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.
@option params [required, String] :notebook_instance_name
The name of the notebook instance to terminate.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopNotebookInstance AWS API Documentation
@overload stop_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16348 def stop_notebook_instance(params = {}, options = {}) req = build_request(:stop_notebook_instance, params) req.send_request(options) end
Stops a pipeline execution.
**Callback Step**
A pipeline execution won't stop while a callback step is running. When you call `StopPipelineExecution` on a pipeline execution with a running callback step, SageMaker
Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a “Status” field which is set to “Stopping”.
You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to `SendPipelineExecutionStepSuccess` or `SendPipelineExecutionStepFailure`.
Only when SageMaker
Pipelines receives one of these calls will it stop the pipeline execution.
**Lambda Step**
A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is `Stopped`. If the timeout is hit the pipeline execution status is `Failed`.
@option params [required, String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@option params [required, String] :client_request_token
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time. **A suitable default value is auto-generated.** You should normally not need to pass this option.**
@return [Types::StopPipelineExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::StopPipelineExecutionResponse#pipeline_execution_arn #pipeline_execution_arn} => String
@example Request syntax with placeholder values
resp = client.stop_pipeline_execution({ pipeline_execution_arn: "PipelineExecutionArn", # required client_request_token: "IdempotencyToken", # required })
@example Response structure
resp.pipeline_execution_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopPipelineExecution AWS API Documentation
@overload stop_pipeline_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16412 def stop_pipeline_execution(params = {}, options = {}) req = build_request(:stop_pipeline_execution, params) req.send_request(options) end
Stops a processing job.
@option params [required, String] :processing_job_name
The name of the processing job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_processing_job({ processing_job_name: "ProcessingJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopProcessingJob AWS API Documentation
@overload stop_processing_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16434 def stop_processing_job(params = {}, options = {}) req = build_request(:stop_processing_job, params) req.send_request(options) end
Stops a training job. To stop a job, Amazon SageMaker
sends the algorithm the `SIGTERM` signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.
When it receives a `StopTrainingJob` request, Amazon SageMaker
changes the status of the job to `Stopping`. After Amazon SageMaker
stops the job, it sets the status to `Stopped`.
@option params [required, String] :training_job_name
The name of the training job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_training_job({ training_job_name: "TrainingJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopTrainingJob AWS API Documentation
@overload stop_training_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16463 def stop_training_job(params = {}, options = {}) req = build_request(:stop_training_job, params) req.send_request(options) end
Stops a transform job.
When Amazon SageMaker
receives a `StopTransformJob` request, the status of the job changes to `Stopping`. After Amazon SageMaker
stops the job, the status is set to `Stopped`. When you stop a transform job before it is completed, Amazon SageMaker
doesn't store the job's output in Amazon S3.
@option params [required, String] :transform_job_name
The name of the transform job to stop.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.stop_transform_job({ transform_job_name: "TransformJobName", # required })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/StopTransformJob AWS API Documentation
@overload stop_transform_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16491 def stop_transform_job(params = {}, options = {}) req = build_request(:stop_transform_job, params) req.send_request(options) end
Updates an action.
@option params [required, String] :action_name
The name of the action to update.
@option params [String] :description
The new description for the action.
@option params [String] :status
The new status for the action.
@option params [Hash<String,String>] :properties
The new list of properties. Overwrites the current property list.
@option params [Array<String>] :properties_to_remove
A list of properties to remove.
@return [Types::UpdateActionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateActionResponse#action_arn #action_arn} => String
@example Request syntax with placeholder values
resp = client.update_action({ action_name: "ExperimentEntityName", # required description: "ExperimentDescription", status: "Unknown", # accepts Unknown, InProgress, Completed, Failed, Stopping, Stopped properties: { "StringParameterValue" => "StringParameterValue", }, properties_to_remove: ["StringParameterValue"], })
@example Response structure
resp.action_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateAction AWS API Documentation
@overload update_action
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16537 def update_action(params = {}, options = {}) req = build_request(:update_action, params) req.send_request(options) end
Updates the properties of an AppImageConfig.
@option params [required, String] :app_image_config_name
The name of the AppImageConfig to update.
@option params [Types::KernelGatewayImageConfig] :kernel_gateway_image_config
The new KernelGateway app to run on the image.
@return [Types::UpdateAppImageConfigResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateAppImageConfigResponse#app_image_config_arn #app_image_config_arn} => String
@example Request syntax with placeholder values
resp = client.update_app_image_config({ app_image_config_name: "AppImageConfigName", # required kernel_gateway_image_config: { kernel_specs: [ # required { name: "KernelName", # required display_name: "KernelDisplayName", }, ], file_system_config: { mount_path: "MountPath", default_uid: 1, default_gid: 1, }, }, })
@example Response structure
resp.app_image_config_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateAppImageConfig AWS API Documentation
@overload update_app_image_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16581 def update_app_image_config(params = {}, options = {}) req = build_request(:update_app_image_config, params) req.send_request(options) end
Updates an artifact.
@option params [required, String] :artifact_arn
The Amazon Resource Name (ARN) of the artifact to update.
@option params [String] :artifact_name
The new name for the artifact.
@option params [Hash<String,String>] :properties
The new list of properties. Overwrites the current property list.
@option params [Array<String>] :properties_to_remove
A list of properties to remove.
@return [Types::UpdateArtifactResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateArtifactResponse#artifact_arn #artifact_arn} => String
@example Request syntax with placeholder values
resp = client.update_artifact({ artifact_arn: "ArtifactArn", # required artifact_name: "ExperimentEntityName", properties: { "StringParameterValue" => "StringParameterValue", }, properties_to_remove: ["StringParameterValue"], })
@example Response structure
resp.artifact_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateArtifact AWS API Documentation
@overload update_artifact
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16623 def update_artifact(params = {}, options = {}) req = build_request(:update_artifact, params) req.send_request(options) end
Updates the specified Git repository with the specified values.
@option params [required, String] :code_repository_name
The name of the Git repository to update.
@option params [Types::GitConfigForUpdate] :git_config
The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of `AWSCURRENT` and must be in the following format: `\{"username": UserName, "password": Password\}`
@return [Types::UpdateCodeRepositoryOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateCodeRepositoryOutput#code_repository_arn #code_repository_arn} => String
@example Request syntax with placeholder values
resp = client.update_code_repository({ code_repository_name: "EntityName", # required git_config: { secret_arn: "SecretArn", }, })
@example Response structure
resp.code_repository_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateCodeRepository AWS API Documentation
@overload update_code_repository
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16663 def update_code_repository(params = {}, options = {}) req = build_request(:update_code_repository, params) req.send_request(options) end
Updates a context.
@option params [required, String] :context_name
The name of the context to update.
@option params [String] :description
The new description for the context.
@option params [Hash<String,String>] :properties
The new list of properties. Overwrites the current property list.
@option params [Array<String>] :properties_to_remove
A list of properties to remove.
@return [Types::UpdateContextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateContextResponse#context_arn #context_arn} => String
@example Request syntax with placeholder values
resp = client.update_context({ context_name: "ExperimentEntityName", # required description: "ExperimentDescription", properties: { "StringParameterValue" => "StringParameterValue", }, properties_to_remove: ["StringParameterValue"], })
@example Response structure
resp.context_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateContext AWS API Documentation
@overload update_context
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16705 def update_context(params = {}, options = {}) req = build_request(:update_context, params) req.send_request(options) end
Updates a fleet of devices.
@option params [required, String] :device_fleet_name
The name of the fleet.
@option params [String] :role_arn
The Amazon Resource Name (ARN) of the device.
@option params [String] :description
Description of the fleet.
@option params [required, Types::EdgeOutputConfig] :output_config
Output configuration for storing sample data collected by the fleet.
@option params [Boolean] :enable_iot_role_alias
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-\\\{DeviceFleetName\\}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.update_device_fleet({ device_fleet_name: "EntityName", # required role_arn: "RoleArn", description: "DeviceFleetDescription", output_config: { # required s3_output_location: "S3Uri", # required kms_key_id: "KmsKeyId", preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component preset_deployment_config: "String", }, enable_iot_role_alias: false, })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateDeviceFleet AWS API Documentation
@overload update_device_fleet
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16753 def update_device_fleet(params = {}, options = {}) req = build_request(:update_device_fleet, params) req.send_request(options) end
Updates one or more devices in a fleet.
@option params [required, String] :device_fleet_name
The name of the fleet the devices belong to.
@option params [required, Array<Types::Device>] :devices
List of devices to register with Edge Manager agent.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.update_devices({ device_fleet_name: "EntityName", # required devices: [ # required { device_name: "DeviceName", # required description: "DeviceDescription", iot_thing_name: "ThingName", }, ], })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateDevices AWS API Documentation
@overload update_devices
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16785 def update_devices(params = {}, options = {}) req = build_request(:update_devices, params) req.send_request(options) end
Updates the default settings for new user profiles in the domain.
@option params [required, String] :domain_id
The ID of the domain to be updated.
@option params [Types::UserSettings] :default_user_settings
A collection of settings.
@return [Types::UpdateDomainResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateDomainResponse#domain_arn #domain_arn} => String
@example Request syntax with placeholder values
resp = client.update_domain({ domain_id: "DomainId", # required default_user_settings: { execution_role: "RoleArn", security_groups: ["SecurityGroupId"], sharing_settings: { notebook_output_option: "Allowed", # accepts Allowed, Disabled s3_output_path: "S3Uri", s3_kms_key_id: "KmsKeyId", }, jupyter_server_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, kernel_gateway_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, custom_images: [ { image_name: "ImageName", # required image_version_number: 1, app_image_config_name: "AppImageConfigName", # required }, ], lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, tensor_board_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, }, }, })
@example Response structure
resp.domain_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateDomain AWS API Documentation
@overload update_domain
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16858 def update_domain(params = {}, options = {}) req = build_request(:update_domain, params) req.send_request(options) end
Deploys the new `EndpointConfig` specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous `EndpointConfig` (there is no availability loss).
When Amazon SageMaker
receives the request, it sets the endpoint status to `Updating`. After updating the endpoint, it sets the status to `InService`. To check the status of an endpoint, use the DescribeEndpoint API.
<note markdown=“1”> You must not delete an `EndpointConfig` in use by an endpoint that is live or while the `UpdateEndpoint` or `CreateEndpoint` operations are being performed on the endpoint. To update an endpoint, you must create a new `EndpointConfig`.
If you delete the `EndpointConfig` of an endpoint that is active or
being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
</note>
@option params [required, String] :endpoint_name
The name of the endpoint whose configuration you want to update.
@option params [required, String] :endpoint_config_name
The name of the new endpoint configuration.
@option params [Boolean] :retain_all_variant_properties
When updating endpoint resources, enables or disables the retention of [variant properties][1], such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set `RetainAllVariantProperties` to `true`. To use the variant properties specified in a new `EndpointConfig` call when updating an endpoint, set `RetainAllVariantProperties` to `false`. The default is `false`. [1]: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VariantProperty.html
@option params [Array<Types::VariantProperty>] :exclude_retained_variant_properties
When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties, whose value is set to `true`, `ExcludeRetainedVariantProperties` specifies the list of type VariantProperty to override with the values provided by `EndpointConfig`. If you don't specify a value for `ExcludeAllVariantProperties`, no variant properties are overridden.
@option params [Types::DeploymentConfig] :deployment_config
The deployment configuration for the endpoint to be updated.
@return [Types::UpdateEndpointOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateEndpointOutput#endpoint_arn #endpoint_arn} => String
@example Request syntax with placeholder values
resp = client.update_endpoint({ endpoint_name: "EndpointName", # required endpoint_config_name: "EndpointConfigName", # required retain_all_variant_properties: false, exclude_retained_variant_properties: [ { variant_property_type: "DesiredInstanceCount", # required, accepts DesiredInstanceCount, DesiredWeight, DataCaptureConfig }, ], deployment_config: { blue_green_update_policy: { # required traffic_routing_configuration: { # required type: "ALL_AT_ONCE", # required, accepts ALL_AT_ONCE, CANARY wait_interval_in_seconds: 1, # required canary_size: { type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT value: 1, # required }, }, termination_wait_in_seconds: 1, maximum_execution_timeout_in_seconds: 1, }, auto_rollback_configuration: { alarms: [ { alarm_name: "AlarmName", }, ], }, }, })
@example Response structure
resp.endpoint_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateEndpoint AWS API Documentation
@overload update_endpoint
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 16961 def update_endpoint(params = {}, options = {}) req = build_request(:update_endpoint, params) req.send_request(options) end
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker
sets the endpoint status to `Updating`. After updating the endpoint, it sets the status to `InService`. To check the status of an endpoint, use the DescribeEndpoint API.
@option params [required, String] :endpoint_name
The name of an existing Amazon SageMaker endpoint.
@option params [required, Array<Types::DesiredWeightAndCapacity>] :desired_weights_and_capacities
An object that provides new capacity and weight values for a variant.
@return [Types::UpdateEndpointWeightsAndCapacitiesOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateEndpointWeightsAndCapacitiesOutput#endpoint_arn #endpoint_arn} => String
@example Request syntax with placeholder values
resp = client.update_endpoint_weights_and_capacities({ endpoint_name: "EndpointName", # required desired_weights_and_capacities: [ # required { variant_name: "VariantName", # required desired_weight: 1.0, desired_instance_count: 1, }, ], })
@example Response structure
resp.endpoint_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateEndpointWeightsAndCapacities AWS API Documentation
@overload update_endpoint_weights_and_capacities
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17004 def update_endpoint_weights_and_capacities(params = {}, options = {}) req = build_request(:update_endpoint_weights_and_capacities, params) req.send_request(options) end
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
@option params [required, String] :experiment_name
The name of the experiment to update.
@option params [String] :display_name
The name of the experiment as displayed. The name doesn't need to be unique. If `DisplayName` isn't specified, `ExperimentName` is displayed.
@option params [String] :description
The description of the experiment.
@return [Types::UpdateExperimentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateExperimentResponse#experiment_arn #experiment_arn} => String
@example Request syntax with placeholder values
resp = client.update_experiment({ experiment_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", description: "ExperimentDescription", })
@example Response structure
resp.experiment_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateExperiment AWS API Documentation
@overload update_experiment
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17043 def update_experiment(params = {}, options = {}) req = build_request(:update_experiment, params) req.send_request(options) end
Updates the properties of a SageMaker
image. To change the image's tags, use the AddTags and DeleteTags APIs.
@option params [Array<String>] :delete_properties
A list of properties to delete. Only the `Description` and `DisplayName` properties can be deleted.
@option params [String] :description
The new description for the image.
@option params [String] :display_name
The new display name for the image.
@option params [required, String] :image_name
The name of the image to update.
@option params [String] :role_arn
The new Amazon Resource Name (ARN) for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
@return [Types::UpdateImageResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateImageResponse#image_arn #image_arn} => String
@example Request syntax with placeholder values
resp = client.update_image({ delete_properties: ["ImageDeleteProperty"], description: "ImageDescription", display_name: "ImageDisplayName", image_name: "ImageName", # required role_arn: "RoleArn", })
@example Response structure
resp.image_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateImage AWS API Documentation
@overload update_image
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17090 def update_image(params = {}, options = {}) req = build_request(:update_image, params) req.send_request(options) end
Updates a versioned model.
@option params [required, String] :model_package_arn
The Amazon Resource Name (ARN) of the model.
@option params [required, String] :model_approval_status
The approval status of the model.
@option params [String] :approval_description
A description for the approval status of the model.
@return [Types::UpdateModelPackageOutput] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateModelPackageOutput#model_package_arn #model_package_arn} => String
@example Request syntax with placeholder values
resp = client.update_model_package({ model_package_arn: "ModelPackageArn", # required model_approval_status: "Approved", # required, accepts Approved, Rejected, PendingManualApproval approval_description: "ApprovalDescription", })
@example Response structure
resp.model_package_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateModelPackage AWS API Documentation
@overload update_model_package
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17126 def update_model_package(params = {}, options = {}) req = build_request(:update_model_package, params) req.send_request(options) end
Updates a previously created schedule.
@option params [required, String] :monitoring_schedule_name
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
@option params [required, Types::MonitoringScheduleConfig] :monitoring_schedule_config
The configuration object that specifies the monitoring schedule and defines the monitoring job.
@return [Types::UpdateMonitoringScheduleResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateMonitoringScheduleResponse#monitoring_schedule_arn #monitoring_schedule_arn} => String
@example Request syntax with placeholder values
resp = client.update_monitoring_schedule({ monitoring_schedule_name: "MonitoringScheduleName", # required monitoring_schedule_config: { # required schedule_config: { schedule_expression: "ScheduleExpression", # required }, monitoring_job_definition: { baseline_config: { baselining_job_name: "ProcessingJobName", constraints_resource: { s3_uri: "S3Uri", }, statistics_resource: { s3_uri: "S3Uri", }, }, monitoring_inputs: [ # required { endpoint_input: { # required endpoint_name: "EndpointName", # required local_path: "ProcessingLocalPath", # required s3_input_mode: "Pipe", # accepts Pipe, File s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key features_attribute: "String", inference_attribute: "String", probability_attribute: "String", probability_threshold_attribute: 1.0, start_time_offset: "MonitoringTimeOffsetString", end_time_offset: "MonitoringTimeOffsetString", }, }, ], monitoring_output_config: { # required monitoring_outputs: [ # required { s3_output: { # required s3_uri: "MonitoringS3Uri", # required local_path: "ProcessingLocalPath", # required s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob }, }, ], kms_key_id: "KmsKeyId", }, monitoring_resources: { # required cluster_config: { # required instance_count: 1, # required instance_type: "ml.t3.medium", # required, accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, # required volume_kms_key_id: "KmsKeyId", }, }, monitoring_app_specification: { # required image_uri: "ImageUri", # required container_entrypoint: ["ContainerEntrypointString"], container_arguments: ["ContainerArgument"], record_preprocessor_source_uri: "S3Uri", post_analytics_processor_source_uri: "S3Uri", }, stopping_condition: { max_runtime_in_seconds: 1, # required }, environment: { "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue", }, network_config: { enable_inter_container_traffic_encryption: false, enable_network_isolation: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, role_arn: "RoleArn", # required }, monitoring_job_definition_name: "MonitoringJobDefinitionName", monitoring_type: "DataQuality", # accepts DataQuality, ModelQuality, ModelBias, ModelExplainability }, })
@example Response structure
resp.monitoring_schedule_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateMonitoringSchedule AWS API Documentation
@overload update_monitoring_schedule
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17235 def update_monitoring_schedule(params = {}, options = {}) req = build_request(:update_monitoring_schedule, params) req.send_request(options) end
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
@option params [required, String] :notebook_instance_name
The name of the notebook instance to update.
@option params [String] :instance_type
The Amazon ML compute instance type.
@option params [String] :role_arn
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see [Amazon SageMaker Roles][1]. <note markdown="1"> To be able to pass this role to Amazon SageMaker, the caller of this API must have the `iam:PassRole` permission. </note> [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html
@option params [String] :lifecycle_config_name
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see [Step 2.1: (Optional) Customize a Notebook Instance][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html
@option params [Boolean] :disassociate_lifecycle_config
Set to `true` to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.
@option params [Integer] :volume_size_in_gb
The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
@option params [String] :default_code_repository
The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in [Amazon Web Services CodeCommit][1] or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see [Associating Git Repositories with Amazon SageMaker Notebook Instances][2]. [1]: https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html
@option params [Array<String>] :additional_code_repositories
An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in [Amazon Web Services CodeCommit][1] or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see [Associating Git Repositories with Amazon SageMaker Notebook Instances][2]. [1]: https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html [2]: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html
@option params [Array<String>] :accelerator_types
A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see [Using Elastic Inference in Amazon SageMaker][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
@option params [Boolean] :disassociate_accelerator_types
A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
@option params [Boolean] :disassociate_default_code_repository
The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
@option params [Boolean] :disassociate_additional_code_repositories
A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
@option params [String] :root_access
Whether root access is enabled or disabled for users of the notebook instance. The default value is `Enabled`. <note markdown="1"> If you set this to `Disabled`, users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions. </note>
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.update_notebook_instance({ notebook_instance_name: "NotebookInstanceName", # required instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge role_arn: "RoleArn", lifecycle_config_name: "NotebookInstanceLifecycleConfigName", disassociate_lifecycle_config: false, volume_size_in_gb: 1, default_code_repository: "CodeRepositoryNameOrUrl", additional_code_repositories: ["CodeRepositoryNameOrUrl"], accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge disassociate_accelerator_types: false, disassociate_default_code_repository: false, disassociate_additional_code_repositories: false, root_access: "Enabled", # accepts Enabled, Disabled })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateNotebookInstance AWS API Documentation
@overload update_notebook_instance
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17382 def update_notebook_instance(params = {}, options = {}) req = build_request(:update_notebook_instance, params) req.send_request(options) end
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
@option params [required, String] :notebook_instance_lifecycle_config_name
The name of the lifecycle configuration.
@option params [Array<Types::NotebookInstanceLifecycleHook>] :on_create
The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
@option params [Array<Types::NotebookInstanceLifecycleHook>] :on_start
The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
@return [Struct] Returns an empty {Seahorse::Client::Response response}.
@example Request syntax with placeholder values
resp = client.update_notebook_instance_lifecycle_config({ notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required on_create: [ { content: "NotebookInstanceLifecycleConfigContent", }, ], on_start: [ { content: "NotebookInstanceLifecycleConfigContent", }, ], })
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateNotebookInstanceLifecycleConfig AWS API Documentation
@overload update_notebook_instance_lifecycle_config
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17424 def update_notebook_instance_lifecycle_config(params = {}, options = {}) req = build_request(:update_notebook_instance_lifecycle_config, params) req.send_request(options) end
Updates a pipeline.
@option params [required, String] :pipeline_name
The name of the pipeline to update.
@option params [String] :pipeline_display_name
The display name of the pipeline.
@option params [String] :pipeline_definition
The JSON pipeline definition.
@option params [String] :pipeline_description
The description of the pipeline.
@option params [String] :role_arn
The Amazon Resource Name (ARN) that the pipeline uses to execute.
@return [Types::UpdatePipelineResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdatePipelineResponse#pipeline_arn #pipeline_arn} => String
@example Request syntax with placeholder values
resp = client.update_pipeline({ pipeline_name: "PipelineName", # required pipeline_display_name: "PipelineName", pipeline_definition: "PipelineDefinition", pipeline_description: "PipelineDescription", role_arn: "RoleArn", })
@example Response structure
resp.pipeline_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdatePipeline AWS API Documentation
@overload update_pipeline
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17468 def update_pipeline(params = {}, options = {}) req = build_request(:update_pipeline, params) req.send_request(options) end
Updates a pipeline execution.
@option params [required, String] :pipeline_execution_arn
The Amazon Resource Name (ARN) of the pipeline execution.
@option params [String] :pipeline_execution_description
The description of the pipeline execution.
@option params [String] :pipeline_execution_display_name
The display name of the pipeline execution.
@return [Types::UpdatePipelineExecutionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdatePipelineExecutionResponse#pipeline_execution_arn #pipeline_execution_arn} => String
@example Request syntax with placeholder values
resp = client.update_pipeline_execution({ pipeline_execution_arn: "PipelineExecutionArn", # required pipeline_execution_description: "PipelineExecutionDescription", pipeline_execution_display_name: "PipelineExecutionName", })
@example Response structure
resp.pipeline_execution_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdatePipelineExecution AWS API Documentation
@overload update_pipeline_execution
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17504 def update_pipeline_execution(params = {}, options = {}) req = build_request(:update_pipeline_execution, params) req.send_request(options) end
Update a model training job to request a new Debugger profiling configuration.
@option params [required, String] :training_job_name
The name of a training job to update the Debugger profiling configuration.
@option params [Types::ProfilerConfigForUpdate] :profiler_config
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
@option params [Array<Types::ProfilerRuleConfiguration>] :profiler_rule_configurations
Configuration information for Debugger rules for profiling system and framework metrics.
@return [Types::UpdateTrainingJobResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateTrainingJobResponse#training_job_arn #training_job_arn} => String
@example Request syntax with placeholder values
resp = client.update_training_job({ training_job_name: "TrainingJobName", # required profiler_config: { s3_output_path: "S3Uri", profiling_interval_in_milliseconds: 1, profiling_parameters: { "ConfigKey" => "ConfigValue", }, disable_profiler: false, }, profiler_rule_configurations: [ { rule_configuration_name: "RuleConfigurationName", # required local_path: "DirectoryPath", s3_output_path: "S3Uri", rule_evaluator_image: "AlgorithmImage", # required instance_type: "ml.t3.medium", # accepts ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge volume_size_in_gb: 1, rule_parameters: { "ConfigKey" => "ConfigValue", }, }, ], })
@example Response structure
resp.training_job_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateTrainingJob AWS API Documentation
@overload update_training_job
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17563 def update_training_job(params = {}, options = {}) req = build_request(:update_training_job, params) req.send_request(options) end
Updates the display name of a trial.
@option params [required, String] :trial_name
The name of the trial to update.
@option params [String] :display_name
The name of the trial as displayed. The name doesn't need to be unique. If `DisplayName` isn't specified, `TrialName` is displayed.
@return [Types::UpdateTrialResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateTrialResponse#trial_arn #trial_arn} => String
@example Request syntax with placeholder values
resp = client.update_trial({ trial_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", })
@example Response structure
resp.trial_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateTrial AWS API Documentation
@overload update_trial
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17596 def update_trial(params = {}, options = {}) req = build_request(:update_trial, params) req.send_request(options) end
Updates one or more properties of a trial component.
@option params [required, String] :trial_component_name
The name of the component to update.
@option params [String] :display_name
The name of the component as displayed. The name doesn't need to be unique. If `DisplayName` isn't specified, `TrialComponentName` is displayed.
@option params [Types::TrialComponentStatus] :status
The new status of the component.
@option params [Time,DateTime,Date,Integer,String] :start_time
When the component started.
@option params [Time,DateTime,Date,Integer,String] :end_time
When the component ended.
@option params [Hash<String,Types::TrialComponentParameterValue>] :parameters
Replaces all of the component's hyperparameters with the specified hyperparameters.
@option params [Array<String>] :parameters_to_remove
The hyperparameters to remove from the component.
@option params [Hash<String,Types::TrialComponentArtifact>] :input_artifacts
Replaces all of the component's input artifacts with the specified artifacts.
@option params [Array<String>] :input_artifacts_to_remove
The input artifacts to remove from the component.
@option params [Hash<String,Types::TrialComponentArtifact>] :output_artifacts
Replaces all of the component's output artifacts with the specified artifacts.
@option params [Array<String>] :output_artifacts_to_remove
The output artifacts to remove from the component.
@return [Types::UpdateTrialComponentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateTrialComponentResponse#trial_component_arn #trial_component_arn} => String
@example Request syntax with placeholder values
resp = client.update_trial_component({ trial_component_name: "ExperimentEntityName", # required display_name: "ExperimentEntityName", status: { primary_status: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped message: "TrialComponentStatusMessage", }, start_time: Time.now, end_time: Time.now, parameters: { "TrialComponentKey256" => { string_value: "StringParameterValue", number_value: 1.0, }, }, parameters_to_remove: ["TrialComponentKey256"], input_artifacts: { "TrialComponentKey64" => { media_type: "MediaType", value: "TrialComponentArtifactValue", # required }, }, input_artifacts_to_remove: ["TrialComponentKey256"], output_artifacts: { "TrialComponentKey64" => { media_type: "MediaType", value: "TrialComponentArtifactValue", # required }, }, output_artifacts_to_remove: ["TrialComponentKey256"], })
@example Response structure
resp.trial_component_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateTrialComponent AWS API Documentation
@overload update_trial_component
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17687 def update_trial_component(params = {}, options = {}) req = build_request(:update_trial_component, params) req.send_request(options) end
Updates a user profile.
@option params [required, String] :domain_id
The domain ID.
@option params [required, String] :user_profile_name
The user profile name.
@option params [Types::UserSettings] :user_settings
A collection of settings.
@return [Types::UpdateUserProfileResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateUserProfileResponse#user_profile_arn #user_profile_arn} => String
@example Request syntax with placeholder values
resp = client.update_user_profile({ domain_id: "DomainId", # required user_profile_name: "UserProfileName", # required user_settings: { execution_role: "RoleArn", security_groups: ["SecurityGroupId"], sharing_settings: { notebook_output_option: "Allowed", # accepts Allowed, Disabled s3_output_path: "S3Uri", s3_kms_key_id: "KmsKeyId", }, jupyter_server_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, kernel_gateway_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, custom_images: [ { image_name: "ImageName", # required image_version_number: 1, app_image_config_name: "AppImageConfigName", # required }, ], lifecycle_config_arns: ["StudioLifecycleConfigArn"], }, tensor_board_app_settings: { default_resource_spec: { sage_maker_image_arn: "ImageArn", sage_maker_image_version_arn: "ImageVersionArn", instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge lifecycle_config_arn: "StudioLifecycleConfigArn", }, }, }, })
@example Response structure
resp.user_profile_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateUserProfile AWS API Documentation
@overload update_user_profile
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17764 def update_user_profile(params = {}, options = {}) req = build_request(:update_user_profile, params) req.send_request(options) end
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
Use `SourceIpConfig` to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten [CIDRs]. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a `Not Found` error message on the worker portal.
Use `OidcConfig` to update the configuration of a workforce created using your own OIDC IdP.
You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.
This operation only applies to private workforces.
[1]: docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html
@option params [required, String] :workforce_name
The name of the private workforce that you want to update. You can find your workforce name by using the operation.
@option params [Types::SourceIpConfig] :source_ip_config
A list of one to ten worker IP address ranges ([CIDRs][1]) that can be used to access tasks assigned to this workforce. Maximum: Ten CIDR values [1]: https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Subnets.html
@option params [Types::OidcConfig] :oidc_config
Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
@return [Types::UpdateWorkforceResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateWorkforceResponse#workforce #workforce} => Types::Workforce
@example Request syntax with placeholder values
resp = client.update_workforce({ workforce_name: "WorkforceName", # required source_ip_config: { cidrs: ["Cidr"], # required }, oidc_config: { client_id: "ClientId", # required client_secret: "ClientSecret", # required issuer: "OidcEndpoint", # required authorization_endpoint: "OidcEndpoint", # required token_endpoint: "OidcEndpoint", # required user_info_endpoint: "OidcEndpoint", # required logout_endpoint: "OidcEndpoint", # required jwks_uri: "OidcEndpoint", # required }, })
@example Response structure
resp.workforce.workforce_name #=> String resp.workforce.workforce_arn #=> String resp.workforce.last_updated_date #=> Time resp.workforce.source_ip_config.cidrs #=> Array resp.workforce.source_ip_config.cidrs[0] #=> String resp.workforce.sub_domain #=> String resp.workforce.cognito_config.user_pool #=> String resp.workforce.cognito_config.client_id #=> String resp.workforce.oidc_config.client_id #=> String resp.workforce.oidc_config.issuer #=> String resp.workforce.oidc_config.authorization_endpoint #=> String resp.workforce.oidc_config.token_endpoint #=> String resp.workforce.oidc_config.user_info_endpoint #=> String resp.workforce.oidc_config.logout_endpoint #=> String resp.workforce.oidc_config.jwks_uri #=> String resp.workforce.create_date #=> Time
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateWorkforce AWS API Documentation
@overload update_workforce
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17863 def update_workforce(params = {}, options = {}) req = build_request(:update_workforce, params) req.send_request(options) end
Updates an existing work team with new member definitions or description.
@option params [required, String] :workteam_name
The name of the work team to update.
@option params [Array<Types::MemberDefinition>] :member_definitions
A list of `MemberDefinition` objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use `CognitoMemberDefinition`. For workforces created using your own OIDC identity provider (IdP) use `OidcMemberDefinition`. You should not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito *user groups* within the user pool used to create a workforce. All of the `CognitoMemberDefinition` objects that make up the member definition must have the same `ClientId` and `UserPool` values. To add a Amazon Cognito user group to an existing worker pool, see [Adding groups to a User Pool](). For more information about user pools, see [Amazon Cognito User Pools][1]. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in `OidcMemberDefinition` by listing those groups in `Groups`. Be aware that user groups that are already in the work team must also be listed in `Groups` when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update. [1]: https://docs.aws.amazon.com/cognito/latest/developerguide/cognito-user-identity-pools.html
@option params [String] :description
An updated description for the work team.
@option params [Types::NotificationConfiguration] :notification_configuration
Configures SNS topic notifications for available or expiring work items
@return [Types::UpdateWorkteamResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::UpdateWorkteamResponse#workteam #workteam} => Types::Workteam
@example Request syntax with placeholder values
resp = client.update_workteam({ workteam_name: "WorkteamName", # required member_definitions: [ { cognito_member_definition: { user_pool: "CognitoUserPool", # required user_group: "CognitoUserGroup", # required client_id: "ClientId", # required }, oidc_member_definition: { groups: ["Group"], # required }, }, ], description: "String200", notification_configuration: { notification_topic_arn: "NotificationTopicArn", }, })
@example Response structure
resp.workteam.workteam_name #=> String resp.workteam.member_definitions #=> Array resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String resp.workteam.member_definitions[0].oidc_member_definition.groups #=> Array resp.workteam.member_definitions[0].oidc_member_definition.groups[0] #=> String resp.workteam.workteam_arn #=> String resp.workteam.workforce_arn #=> String resp.workteam.product_listing_ids #=> Array resp.workteam.product_listing_ids[0] #=> String resp.workteam.description #=> String resp.workteam.sub_domain #=> String resp.workteam.create_date #=> Time resp.workteam.last_updated_date #=> Time resp.workteam.notification_configuration.notification_topic_arn #=> String
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/UpdateWorkteam AWS API Documentation
@overload update_workteam
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-sagemaker/client.rb, line 17961 def update_workteam(params = {}, options = {}) req = build_request(:update_workteam, params) req.send_request(options) end
Polls an API operation until a resource enters a desired state.
## Basic Usage
A waiter will call an API operation until:
-
It is successful
-
It enters a terminal state
-
It makes the maximum number of attempts
In between attempts, the waiter will sleep.
# polls in a loop, sleeping between attempts client.wait_until(waiter_name, params)
## Configuration
You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.
# poll for ~25 seconds client.wait_until(waiter_name, params, { max_attempts: 5, delay: 5, })
## Callbacks
You can be notified before each polling attempt and before each delay. If you throw `:success` or `:failure` from these callbacks, it will terminate the waiter.
started_at = Time.now client.wait_until(waiter_name, params, { # disable max attempts max_attempts: nil, # poll for 1 hour, instead of a number of attempts before_wait: -> (attempts, response) do throw :failure if Time.now - started_at > 3600 end })
## Handling Errors
When a waiter is unsuccessful, it will raise an error. All of the failure errors extend from {Aws::Waiters::Errors::WaiterFailed}.
begin client.wait_until(...) rescue Aws::Waiters::Errors::WaiterFailed # resource did not enter the desired state in time end
## Valid Waiters
The following table lists the valid waiter names, the operations they call, and the default `:delay` and `:max_attempts` values.
| waiter_name | params | :delay | :max_attempts | | ———————————– | ———————————– | ——– | ————- | | endpoint_deleted | {Client#describe_endpoint} | 30 | 60 | | endpoint_in_service | {Client#describe_endpoint} | 30 | 120 | | image_created | {Client#describe_image} | 60 | 60 | | image_deleted | {Client#describe_image} | 60 | 60 | | image_updated | {Client#describe_image} | 60 | 60 | | image_version_created | {Client#describe_image_version} | 60 | 60 | | image_version_deleted | {Client#describe_image_version} | 60 | 60 | | notebook_instance_deleted | {Client#describe_notebook_instance} | 30 | 60 | | notebook_instance_in_service | {Client#describe_notebook_instance} | 30 | 60 | | notebook_instance_stopped | {Client#describe_notebook_instance} | 30 | 60 | | processing_job_completed_or_stopped | {Client#describe_processing_job} | 60 | 60 | | training_job_completed_or_stopped | {Client#describe_training_job} | 120 | 180 | | transform_job_completed_or_stopped | {Client#describe_transform_job} | 60 | 60 |
@raise [Errors::FailureStateError] Raised when the waiter terminates
because the waiter has entered a state that it will not transition out of, preventing success.
@raise [Errors::TooManyAttemptsError] Raised when the configured
maximum number of attempts have been made, and the waiter is not yet successful.
@raise [Errors::UnexpectedError] Raised when an error is encounted
while polling for a resource that is not expected.
@raise [Errors::NoSuchWaiterError] Raised when you request to wait
for an unknown state.
@return [Boolean] Returns `true` if the waiter was successful. @param [Symbol] waiter_name @param [Hash] params ({}) @param [Hash] options ({}) @option options [Integer] :max_attempts @option options [Integer] :delay @option options [Proc] :before_attempt @option options [Proc] :before_wait
# File lib/aws-sdk-sagemaker/client.rb, line 18083 def wait_until(waiter_name, params = {}, options = {}) w = waiter(waiter_name, options) yield(w.waiter) if block_given? # deprecated w.wait(params) end
@api private @deprecated
# File lib/aws-sdk-sagemaker/client.rb, line 18091 def waiter_names waiters.keys end
Private Instance Methods
@param [Symbol] waiter_name @param [Hash] options ({})
# File lib/aws-sdk-sagemaker/client.rb, line 18099 def waiter(waiter_name, options = {}) waiter_class = waiters[waiter_name] if waiter_class waiter_class.new(options.merge(client: self)) else raise Aws::Waiters::Errors::NoSuchWaiterError.new(waiter_name, waiters.keys) end end
# File lib/aws-sdk-sagemaker/client.rb, line 18108 def waiters { endpoint_deleted: Waiters::EndpointDeleted, endpoint_in_service: Waiters::EndpointInService, image_created: Waiters::ImageCreated, image_deleted: Waiters::ImageDeleted, image_updated: Waiters::ImageUpdated, image_version_created: Waiters::ImageVersionCreated, image_version_deleted: Waiters::ImageVersionDeleted, notebook_instance_deleted: Waiters::NotebookInstanceDeleted, notebook_instance_in_service: Waiters::NotebookInstanceInService, notebook_instance_stopped: Waiters::NotebookInstanceStopped, processing_job_completed_or_stopped: Waiters::ProcessingJobCompletedOrStopped, training_job_completed_or_stopped: Waiters::TrainingJobCompletedOrStopped, transform_job_completed_or_stopped: Waiters::TransformJobCompletedOrStopped } end