Class: Aws::SageMaker::Types::TrainingJobDefinition

Inherits:
Struct
  • Object
show all
Includes:
Aws::Structure
Defined in:
lib/aws-sdk-sagemaker/types.rb

Overview

Note:

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

{
  training_input_mode: "Pipe", # required, accepts Pipe, File
  hyper_parameters: {
    "ParameterKey" => "ParameterValue",
  },
  input_data_config: [ # required
    {
      channel_name: "ChannelName", # required
      data_source: { # required
        s3_data_source: { # required
          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
      },
    },
  ],
  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,
  },
}

Defines the input needed to run a training job using the algorithm.

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#hyper_parameters ⇒ Hash<String,String>

The hyperparameters used for the training job.

Returns:

  • (Hash<String,String>) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end

#input_data_config ⇒ Array<Types::Channel>

An array of ‘Channel` objects, each of which specifies an input source.

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end

#output_data_config ⇒ Types::OutputDataConfig

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end

#resource_config ⇒ Types::ResourceConfig

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end

#stopping_condition ⇒ Types::StoppingCondition

Sets a duration for training. Use this parameter 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 might use this 120-second window to save the model artifacts.

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end

#training_input_mode ⇒ String

The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see [Algorithms].

If an algorithm supports the ‘File` input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the `Pipe` input mode, Amazon SageMaker streams data directly from S3 to the container.

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

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10465

class TrainingJobDefinition < Struct.new(
  :training_input_mode,
  :hyper_parameters,
  :input_data_config,
  :output_data_config,
  :resource_config,
  :stopping_condition)
  include Aws::Structure
end