NAME

Paws::SageMaker::CreateTrainingJob - Arguments for method CreateTrainingJob on Paws::SageMaker

DESCRIPTION

This class represents the parameters used for calling the method CreateTrainingJob on the Amazon SageMaker Service service. Use the attributes of this class as arguments to method CreateTrainingJob.

You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreateTrainingJob.

SYNOPSIS

    my $api.sagemaker = Paws->service('SageMaker');
    my $CreateTrainingJobResponse = $api . sagemaker->CreateTrainingJob(
      AlgorithmSpecification => {
        TrainingInputMode => 'Pipe',              # values: Pipe, File
        AlgorithmName     => 'MyArnOrName',       # min: 1, max: 170; OPTIONAL
        EnableSageMakerMetricsTimeSeries => 1,    # OPTIONAL
        MetricDefinitions                => [
          {
            Name  => 'MyMetricName',     # min: 1, max: 255
            Regex => 'MyMetricRegex',    # min: 1, max: 500
          },
          ...
        ],    # max: 40; OPTIONAL
        TrainingImage => 'MyAlgorithmImage',    # max: 255; OPTIONAL
      },
      OutputDataConfig => {
        S3OutputPath => 'MyS3Uri',              # max: 1024
        KmsKeyId     => 'MyKmsKeyId',           # max: 2048; OPTIONAL
      },
      ResourceConfig => {
        InstanceCount => 1,                     # min: 1
        InstanceType  => 'ml.m4.xlarge'
        , # values: 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
        VolumeSizeInGB => 1,               # min: 1
        VolumeKmsKeyId => 'MyKmsKeyId',    # max: 2048; OPTIONAL
      },
      RoleArn           => 'MyRoleArn',
      StoppingCondition => {
        MaxRuntimeInSeconds  => 1,         # min: 1; OPTIONAL
        MaxWaitTimeInSeconds => 1,         # min: 1; OPTIONAL
      },
      TrainingJobName  => 'MyTrainingJobName',
      CheckpointConfig => {
        S3Uri     => 'MyS3Uri',            # max: 1024
        LocalPath => 'MyDirectoryPath',    # max: 4096; OPTIONAL
      },    # OPTIONAL
      DebugHookConfig => {
        S3OutputPath             => 'MyS3Uri',    # max: 1024
        CollectionConfigurations => [
          {
            CollectionName => 'MyCollectionName',   # min: 1, max: 256; OPTIONAL
            CollectionParameters => {
              'MyConfigKey' =>
                'MyConfigValue',    # key: min: 1, max: 256, value: max: 256
            },    # max: 20; OPTIONAL
          },
          ...
        ],    # max: 20; OPTIONAL
        HookParameters => {
          'MyConfigKey' =>
            'MyConfigValue',    # key: min: 1, max: 256, value: max: 256
        },    # max: 20; OPTIONAL
        LocalPath => 'MyDirectoryPath',    # max: 4096; OPTIONAL
      },    # OPTIONAL
      DebugRuleConfigurations => [
        {
          RuleConfigurationName => 'MyRuleConfigurationName', # min: 1, max: 256
          RuleEvaluatorImage    => 'MyAlgorithmImage',    # max: 255; OPTIONAL
          InstanceType          => 'ml.t3.medium'
          , # values: 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, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge; OPTIONAL
          LocalPath      => 'MyDirectoryPath',    # max: 4096; OPTIONAL
          RuleParameters => {
            'MyConfigKey' =>
              'MyConfigValue',    # key: min: 1, max: 256, value: max: 256
          },    # max: 100; OPTIONAL
          S3OutputPath   => 'MyS3Uri',    # max: 1024
          VolumeSizeInGB => 1,            # OPTIONAL
        },
        ...
      ],    # OPTIONAL
      EnableInterContainerTrafficEncryption => 1,    # OPTIONAL
      EnableManagedSpotTraining             => 1,    # OPTIONAL
      EnableNetworkIsolation                => 1,    # OPTIONAL
      Environment                           => {
        'MyTrainingEnvironmentKey' =>
          'MyTrainingEnvironmentValue',    # key: max: 512, value: max: 512
      },    # OPTIONAL
      ExperimentConfig => {
        ExperimentName => 'MyExperimentEntityName', # min: 1, max: 120; OPTIONAL
        TrialComponentDisplayName =>
          'MyExperimentEntityName',                 # min: 1, max: 120; OPTIONAL
        TrialName => 'MyExperimentEntityName',      # min: 1, max: 120; OPTIONAL
      },    # OPTIONAL
      HyperParameters => {
        'MyHyperParameterKey' =>
          'MyHyperParameterValue',    # key: max: 256, value: max: 2500
      },    # OPTIONAL
      InputDataConfig => [
        {
          ChannelName => 'MyChannelName',    # min: 1, max: 64
          DataSource  => {
            FileSystemDataSource => {
              DirectoryPath        => 'MyDirectoryPath',   # max: 4096; OPTIONAL
              FileSystemAccessMode => 'rw',                # values: rw, ro
              FileSystemId         => 'MyFileSystemId',    # min: 11
              FileSystemType       => 'EFS',    # values: EFS, FSxLustre
            },    # OPTIONAL
            S3DataSource => {
              S3DataType => 'ManifestFile'
              ,    # values: ManifestFile, S3Prefix, AugmentedManifestFile
              S3Uri          => 'MyS3Uri',    # max: 1024
              AttributeNames => [
                'MyAttributeName', ...        # min: 1, max: 256
              ],    # max: 16; OPTIONAL
              S3DataDistributionType => 'FullyReplicated'
              ,     # values: FullyReplicated, ShardedByS3Key; OPTIONAL
            },    # OPTIONAL
          },
          CompressionType   => 'None',            # values: None, Gzip; OPTIONAL
          ContentType       => 'MyContentType',   # max: 256; OPTIONAL
          InputMode         => 'Pipe',            # values: Pipe, File
          RecordWrapperType => 'None',    # values: None, RecordIO; OPTIONAL
          ShuffleConfig     => {
            Seed => 1,
          },                              # OPTIONAL
        },
        ...
      ],    # OPTIONAL
      ProfilerConfig => {
        S3OutputPath                    => 'MyS3Uri',    # max: 1024
        ProfilingIntervalInMilliseconds => 1,            # OPTIONAL
        ProfilingParameters             => {
          'MyConfigKey' =>
            'MyConfigValue',    # key: min: 1, max: 256, value: max: 256
        },    # max: 20; OPTIONAL
      },    # OPTIONAL
      ProfilerRuleConfigurations => [
        {
          RuleConfigurationName => 'MyRuleConfigurationName', # min: 1, max: 256
          RuleEvaluatorImage    => 'MyAlgorithmImage',    # max: 255; OPTIONAL
          InstanceType          => 'ml.t3.medium'
          , # values: 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, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge; OPTIONAL
          LocalPath      => 'MyDirectoryPath',    # max: 4096; OPTIONAL
          RuleParameters => {
            'MyConfigKey' =>
              'MyConfigValue',    # key: min: 1, max: 256, value: max: 256
          },    # max: 100; OPTIONAL
          S3OutputPath   => 'MyS3Uri',    # max: 1024
          VolumeSizeInGB => 1,            # OPTIONAL
        },
        ...
      ],    # OPTIONAL
      RetryStrategy => {
        MaximumRetryAttempts => 1,    # min: 1, max: 30
      },    # OPTIONAL
      Tags => [
        {
          Key   => 'MyTagKey',      # min: 1, max: 128
          Value => 'MyTagValue',    # max: 256
        },
        ...
      ],    # OPTIONAL
      TensorBoardOutputConfig => {
        S3OutputPath => 'MyS3Uri',            # max: 1024
        LocalPath    => 'MyDirectoryPath',    # max: 4096; OPTIONAL
      },    # OPTIONAL
      VpcConfig => {
        SecurityGroupIds => [
          'MySecurityGroupId', ...    # max: 32
        ],    # min: 1, max: 5
        Subnets => [
          'MySubnetId', ...    # max: 32
        ],    # min: 1, max: 16
      },    # OPTIONAL
    );
    # Results:
    my $TrainingJobArn = $CreateTrainingJobResponse->TrainingJobArn;
    # Returns a L<Paws::SageMaker::CreateTrainingJobResponse> object.

Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object. For the AWS API documentation, see <https://docs.aws.amazon.com/goto/WebAPI/api.sagemaker/CreateTrainingJob>

ATTRIBUTES

REQUIRED AlgorithmSpecification => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).

CheckpointConfig => Paws::SageMaker::CheckpointConfig

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

DebugHookConfig => Paws::SageMaker::DebugHookConfig

DebugRuleConfigurations => ArrayRef[Paws::SageMaker::DebugRuleConfiguration]

Configuration information for Debugger rules for debugging output tensors.

EnableInterContainerTrafficEncryption => Bool

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html).

EnableManagedSpotTraining => Bool

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.

EnableNetworkIsolation => Bool

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.

Environment => Paws::SageMaker::TrainingEnvironmentMap

The environment variables to set in the Docker container.

ExperimentConfig => Paws::SageMaker::ExperimentConfig

HyperParameters => Paws::SageMaker::HyperParameters

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).

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".

InputDataConfig => ArrayRef[Paws::SageMaker::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.

REQUIRED OutputDataConfig => Paws::SageMaker::OutputDataConfig

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

ProfilerConfig => Paws::SageMaker::ProfilerConfig

ProfilerRuleConfigurations => ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]

Configuration information for Debugger rules for profiling system and framework metrics.

REQUIRED ResourceConfig => Paws::SageMaker::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.

RetryStrategy => Paws::SageMaker::RetryStrategy

The number of times to retry the job when the job fails due to an "InternalServerError".

REQUIRED RoleArn => Str

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).

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

REQUIRED StoppingCondition => Paws::SageMaker::StoppingCondition

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. 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.

Tags => ArrayRef[Paws::SageMaker::Tag]

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).

TensorBoardOutputConfig => Paws::SageMaker::TensorBoardOutputConfig

REQUIRED TrainingJobName => Str

The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

VpcConfig => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).

BUGS and CONTRIBUTIONS

The source code is located here: <https://github.com/pplu/aws-sdk-perl>

Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>