class Aws::SageMaker::Types::TrainingSpecification
Defines how the algorithm is used for a training job.
@note When making an API call, you may pass TrainingSpecification
data as a hash: { 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 }, ], }
@!attribute [rw] training_image
The Amazon ECR registry path of the Docker image that contains the training algorithm. @return [String]
@!attribute [rw] training_image_digest
An MD5 hash of the training algorithm that identifies the Docker image used for training. @return [String]
@!attribute [rw] supported_hyper_parameters
A list of the `HyperParameterSpecification` objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.> @return [Array<Types::HyperParameterSpecification>]
@!attribute [rw] supported_training_instance_types
A list of the instance types that this algorithm can use for training. @return [Array<String>]
@!attribute [rw] supports_distributed_training
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training. @return [Boolean]
@!attribute [rw] metric_definitions
A list of `MetricDefinition` objects, which are used for parsing metrics generated by the algorithm. @return [Array<Types::MetricDefinition>]
@!attribute [rw] training_channels
A list of `ChannelSpecification` objects, which specify the input sources to be used by the algorithm. @return [Array<Types::ChannelSpecification>]
@!attribute [rw] supported_tuning_job_objective_metrics
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job. @return [Array<Types::HyperParameterTuningJobObjective>]
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/TrainingSpecification AWS API Documentation
Constants
- SENSITIVE