class Aws::SageMaker::Types::CreateTrainingJobRequest
@note When making an API call, you may pass CreateTrainingJobRequest
data as a hash: { 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 }, }
@!attribute [rw] 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. @return [String]
@!attribute [rw] 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 @return [Hash<String,String>]
@!attribute [rw] 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 @return [Types::AlgorithmSpecification]
@!attribute [rw] 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 @return [String]
@!attribute [rw] 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. @return [Array<Types::Channel>]
@!attribute [rw] output_data_config
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts. @return [Types::OutputDataConfig]
@!attribute [rw] 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. @return [Types::ResourceConfig]
@!attribute [rw] 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 @return [Types::VpcConfig]
@!attribute [rw] 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. @return [Types::StoppingCondition]
@!attribute [rw] 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 [Array<Types::Tag>]
@!attribute [rw] 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. @return [Boolean]
@!attribute [rw] 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 @return [Boolean]
@!attribute [rw] 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. @return [Boolean]
@!attribute [rw] checkpoint_config
Contains information about the output location for managed spot training checkpoint data. @return [Types::CheckpointConfig]
@!attribute [rw] 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 @return [Types::DebugHookConfig]
@!attribute [rw] debug_rule_configurations
Configuration information for Debugger rules for debugging output tensors. @return [Array<Types::DebugRuleConfiguration>]
@!attribute [rw] tensor_board_output_config
Configuration of storage locations for the Debugger TensorBoard output data. @return [Types::TensorBoardOutputConfig]
@!attribute [rw] 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::ExperimentConfig]
@!attribute [rw] profiler_config
Configuration information for Debugger system monitoring, framework profiling, and storage paths. @return [Types::ProfilerConfig]
@!attribute [rw] profiler_rule_configurations
Configuration information for Debugger rules for profiling system and framework metrics. @return [Array<Types::ProfilerRuleConfiguration>]
@!attribute [rw] environment
The environment variables to set in the Docker container. @return [Hash<String,String>]
@!attribute [rw] retry_strategy
The number of times to retry the job when the job fails due to an `InternalServerError`. @return [Types::RetryStrategy]
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateTrainingJobRequest AWS API Documentation
Constants
- SENSITIVE