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Class: Aws::SageMaker::Types::TrainingSpecification

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing TrainingSpecification as input to an Aws::Client method, you can use a vanilla 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
    },
  ],
}

Defines how the algorithm is used for a training job.

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#metric_definitions ⇒ Array<Types::MetricDefinition>

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

Returns:

  • (Array<Types::MetricDefinition>) —

    A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

#supported_hyper_parameters ⇒ Array<Types::HyperParameterSpecification>

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

Returns:

#supported_training_instance_types ⇒ Array<String>

A list of the instance types that this algorithm can use for training.

Returns:

  • (Array<String>) —

    A list of the instance types that this algorithm can use for training.

#supported_tuning_job_objective_metrics ⇒ Array<Types::HyperParameterTuningJobObjective>

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

Returns:

#supports_distributed_training ⇒ Boolean

Indicates whether the algorithm supports distributed training. If set to false, buyers can\'t request more than one instance during training.

Returns:

  • (Boolean) —

    Indicates whether the algorithm supports distributed training.

#training_channels ⇒ Array<Types::ChannelSpecification>

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

Returns:

  • (Array<Types::ChannelSpecification>) —

    A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

#training_image ⇒ String

The Amazon ECR registry path of the Docker image that contains the training algorithm.

Returns:

  • (String) —

    The Amazon ECR registry path of the Docker image that contains the training algorithm.

#training_image_digest ⇒ String

An MD5 hash of the training algorithm that identifies the Docker image used for training.

Returns:

  • (String) —

    An MD5 hash of the training algorithm that identifies the Docker image used for training.