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