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

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-sagemaker/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", # required
    training_input_mode: "Pipe", # required, accepts Pipe, File
  },
  role_arn: "RoleArn", # required
  input_data_config: [ # required
    {
      channel_name: "ChannelName", # required
      data_source: { # required
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
          s3_uri: "S3Uri", # required
          s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
        },
      },
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      record_wrapper_type: "None", # accepts None, RecordIO
    },
  ],
  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",
  },
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
}

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_specificationTypes::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 your-algorithms.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#hyper_parametersHash<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.

Returns:

  • (Hash<String,String>)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#input_data_configArray<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 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.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#output_data_configTypes::OutputDataConfig

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#resource_configTypes::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.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#role_arnString

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.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#stopping_conditionTypes::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.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#tagsArray<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.

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#training_job_nameString

The name of the training job. The name must be unique within an AWS Region in an AWS account. It appears in the Amazon SageMaker console.

Returns:

  • (String)


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end

#vpc_configTypes::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 train-vpc

Returns:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 1052

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)
  include Aws::Structure
end