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Class: Aws::SageMaker::Types::HyperParameterTrainingJobDefinition

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

Overview

Note:

When passing HyperParameterTrainingJobDefinition as input to an Aws::Client method, you can use a vanilla Hash:

{
  static_hyper_parameters: {
    "ParameterKey" => "ParameterValue",
  },
  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"],
        },
      },
      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.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.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
    instance_count: 1, # required
    volume_size_in_gb: 1, # required
    volume_kms_key_id: "KmsKeyId",
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
  },
  enable_network_isolation: false,
  enable_inter_container_traffic_encryption: false,
}

Defines the training jobs launched by a hyperparameter tuning job.

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specificationTypes::HyperParameterAlgorithmSpecification

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

Returns:

#enable_inter_container_traffic_encryptionBoolean

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.

Returns:

  • (Boolean)

    To encrypt all communications between ML compute instances in distributed training, choose True.

#enable_network_isolationBoolean

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 network isolation is used 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.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

  • (Boolean)

    Isolates the training container.

#input_data_configArray<Types::Channel>

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

Returns:

  • (Array<Types::Channel>)

    An array of Channel objects that specify the input for the training jobs that the tuning job launches.

#output_data_configTypes::OutputDataConfig

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

Returns:

  • (Types::OutputDataConfig)

    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

#resource_configTypes::ResourceConfig

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the 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.

Returns:

  • (Types::ResourceConfig)

    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

#role_arnString

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

Returns:

  • (String)

    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

#static_hyper_parametersHash<String,String>

Specifies the values of hyperparameters that do not change for the tuning job.

Returns:

  • (Hash<String,String>)

    Specifies the values of hyperparameters that do not change for the tuning job.

#stopping_conditionTypes::StoppingCondition

Specifies a limit to how long a model hyperparameter training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

Returns:

#vpc_configTypes::VpcConfig

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches 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.

Returns:

  • (Types::VpcConfig)

    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.