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

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

Overview

Note:

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

{
  training_input_mode: "Pipe", # required, accepts Pipe, File
  hyper_parameters: {
    "HyperParameterKey" => "HyperParameterValue",
  },
  input_data_config: [ # required
    {
      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.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
    instance_count: 1, # required
    volume_size_in_gb: 1, # required
    volume_kms_key_id: "KmsKeyId",
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
  },
}

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

Returned by:

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>) —

    The hyperparameters used for the training job.

#input_data_config ⇒ Array<Types::Channel>

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

Returns:

  • (Array<Types::Channel>) —

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

#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:

#resource_config ⇒ Types::ResourceConfig

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

Returns:

  • (Types::ResourceConfig) —

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

#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.

Returns:

#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.

Returns:

  • (String) —

    The input mode used by the algorithm for the training job.