Class: Aws::SageMaker::Types::CreateTrainingJobRequest

Inherits:

Struct

Object
Struct
Aws::SageMaker::Types::CreateTrainingJobRequest

show all

Includes:: Aws::Structure

Defined in:: lib/aws-sdk-sagemaker/types.rb

Overview

Note:

When making an API call, you may pass CreateTrainingJobRequest data as a hash:

{
  training_job_name: "TrainingJobName", # required
  hyper_parameters: {
    "ParameterKey" => "ParameterValue",
  },
  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.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.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",
  },
  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
      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: "ExperimentConfigName",
    trial_name: "ExperimentConfigName",
    trial_component_display_name: "ExperimentConfigName",
  },
}

Instance Attribute Summary collapse

#algorithm_specification ⇒ Types::AlgorithmSpecification

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.
#checkpoint_config ⇒ Types::CheckpointConfig

Contains information about the output location for managed spot training checkpoint data.
#debug_hook_config ⇒ Types::DebugHookConfig

Configuration information for the debug hook parameters, collection configuration, and storage paths.
#debug_rule_configurations ⇒ Array<Types::DebugRuleConfiguration>

Configuration information for debugging rules.
#enable_inter_container_traffic_encryption ⇒ Boolean

To encrypt all communications between ML compute instances in distributed training, choose ‘True`.
#enable_managed_spot_training ⇒ Boolean

To train models using managed spot training, choose ‘True`.
#enable_network_isolation ⇒ Boolean

Isolates the training container.
#experiment_config ⇒ Types::ExperimentConfig

Configuration for the experiment.
#hyper_parameters ⇒ Hash<String,String>

Algorithm-specific parameters that influence the quality of the model.
#input_data_config ⇒ Array<Types::Channel>

An array of ‘Channel` objects.
#output_data_config ⇒ Types::OutputDataConfig

Specifies the path to the S3 location where you want to store model artifacts.
#resource_config ⇒ Types::ResourceConfig

The resources, including the ML compute instances and ML storage volumes, to use for model training.
#role_arn ⇒ String

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
#stopping_condition ⇒ Types::StoppingCondition

Specifies a limit to how long a model training job can run.
#tags ⇒ Array<Types::Tag>

An array of key-value pairs.
#tensor_board_output_config ⇒ Types::TensorBoardOutputConfig

Configuration of storage locations for TensorBoard output.
#training_job_name ⇒ String

The name of the training job.
#vpc_config ⇒ Types::VpcConfig

A VpcConfig object that specifies the VPC that you want your training job to connect to.

Instance Attribute Details

#algorithm_specification ⇒ `Types::AlgorithmSpecification`

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]. For information about providing your own algorithms, see [Using Your Own Algorithms with Amazon SageMaker].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html [2]: docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#checkpoint_config ⇒ `Types::CheckpointConfig`

Contains information about the output location for managed spot training checkpoint data.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#debug_hook_config ⇒ `Types::DebugHookConfig`

Configuration information for the debug hook parameters, collection configuration, and storage paths.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#debug_rule_configurations ⇒ `Array<Types::DebugRuleConfiguration>`

Configuration information for debugging rules.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#enable_inter_container_traffic_encryption ⇒ `Boolean`

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]: docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#enable_managed_spot_training ⇒ `Boolean`

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.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#enable_network_isolation ⇒ `Boolean`

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.

<note markdown=“1”> The Semantic Segmentation built-in algorithm does not support network isolation.

</note>

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#experiment_config ⇒ `Types::ExperimentConfig`

Configuration for the experiment.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#hyper_parameters ⇒ `Hash<String,String>`

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].

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]: docs.aws.amazon.com/sagemaker/latest/dg/algos.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#input_data_config ⇒ `Array<Types::Channel>`

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.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#output_data_config ⇒ `Types::OutputDataConfig`

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#resource_config ⇒ `Types::ResourceConfig`

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.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#role_arn ⇒ `String`

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].

<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]: docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#stopping_condition ⇒ `Types::StoppingCondition`

Specifies a limit to how long a model 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.

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.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#tags ⇒ `Array<Types::Tag>`

An array of key-value pairs. For more information, see [Using Cost Allocation Tags] in the *AWS Billing and Cost Management User Guide*.

[1]: docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#tensor_board_output_config ⇒ `Types::TensorBoardOutputConfig`

Configuration of storage locations for TensorBoard output.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#training_job_name ⇒ `String`

The name of the training job. The name must be unique within an AWS Region in an AWS account.

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

#vpc_config ⇒ `Types::VpcConfig`

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]: docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 4673

class CreateTrainingJobRequest < Struct.new(
  :training_job_name,
  :hyper_parameters,
  :algorithm_specification,
  :role_arn,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :vpc_config,
  :stopping_condition,
  :tags,
  :enable_network_isolation,
  :enable_inter_container_traffic_encryption,
  :enable_managed_spot_training,
  :checkpoint_config,
  :debug_hook_config,
  :debug_rule_configurations,
  :tensor_board_output_config,
  :experiment_config)
  include Aws::Structure
end

Class: Aws::SageMaker::Types::CreateTrainingJobRequest

Overview

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specification ⇒ Types::AlgorithmSpecification

#checkpoint_config ⇒ Types::CheckpointConfig

#debug_hook_config ⇒ Types::DebugHookConfig

#debug_rule_configurations ⇒ Array<Types::DebugRuleConfiguration>

#enable_inter_container_traffic_encryption ⇒ Boolean

#enable_managed_spot_training ⇒ Boolean

#enable_network_isolation ⇒ Boolean

#experiment_config ⇒ Types::ExperimentConfig

#hyper_parameters ⇒ Hash<String,String>

#input_data_config ⇒ Array<Types::Channel>

#output_data_config ⇒ Types::OutputDataConfig

#resource_config ⇒ Types::ResourceConfig

#role_arn ⇒ String

#stopping_condition ⇒ Types::StoppingCondition

#tags ⇒ Array<Types::Tag>

#tensor_board_output_config ⇒ Types::TensorBoardOutputConfig

#training_job_name ⇒ String

#vpc_config ⇒ Types::VpcConfig