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CreateTrainingJobCommand

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.

In the request body, you provide the following:

AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training .
RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training.You must grant this role the necessary permissions so that SageMaker can successfully complete model training.
StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
Environment - The environment variables to set in the Docker container.
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

For more information about SageMaker, see How It Works .

Example Syntax

Use a bare-bones client and the command you need to make an API call.

import { SageMakerClient, CreateTrainingJobCommand } from "@aws-sdk/client-sagemaker"; // ES Modules import
// const { SageMakerClient, CreateTrainingJobCommand } = require("@aws-sdk/client-sagemaker"); // CommonJS import
const client = new SageMakerClient(config);
const input = { // CreateTrainingJobRequest
  TrainingJobName: "STRING_VALUE", // required
  HyperParameters: { // HyperParameters
    "<keys>": "STRING_VALUE",
  },
  AlgorithmSpecification: { // AlgorithmSpecification
    TrainingImage: "STRING_VALUE",
    AlgorithmName: "STRING_VALUE",
    TrainingInputMode: "Pipe" || "File" || "FastFile", // required
    MetricDefinitions: [ // MetricDefinitionList
      { // MetricDefinition
        Name: "STRING_VALUE", // required
        Regex: "STRING_VALUE", // required
      },
    ],
    EnableSageMakerMetricsTimeSeries: true || false,
    ContainerEntrypoint: [ // TrainingContainerEntrypoint
      "STRING_VALUE",
    ],
    ContainerArguments: [ // TrainingContainerArguments
      "STRING_VALUE",
    ],
    TrainingImageConfig: { // TrainingImageConfig
      TrainingRepositoryAccessMode: "Platform" || "Vpc", // required
      TrainingRepositoryAuthConfig: { // TrainingRepositoryAuthConfig
        TrainingRepositoryCredentialsProviderArn: "STRING_VALUE", // required
      },
    },
  },
  RoleArn: "STRING_VALUE", // required
  InputDataConfig: [ // InputDataConfig
    { // Channel
      ChannelName: "STRING_VALUE", // required
      DataSource: { // DataSource
        S3DataSource: { // S3DataSource
          S3DataType: "ManifestFile" || "S3Prefix" || "AugmentedManifestFile", // required
          S3Uri: "STRING_VALUE", // required
          S3DataDistributionType: "FullyReplicated" || "ShardedByS3Key",
          AttributeNames: [ // AttributeNames
            "STRING_VALUE",
          ],
          InstanceGroupNames: [ // InstanceGroupNames
            "STRING_VALUE",
          ],
          ModelAccessConfig: { // ModelAccessConfig
            AcceptEula: true || false, // required
          },
          HubAccessConfig: { // HubAccessConfig
            HubContentArn: "STRING_VALUE", // required
          },
        },
        FileSystemDataSource: { // FileSystemDataSource
          FileSystemId: "STRING_VALUE", // required
          FileSystemAccessMode: "rw" || "ro", // required
          FileSystemType: "EFS" || "FSxLustre", // required
          DirectoryPath: "STRING_VALUE", // required
        },
      },
      ContentType: "STRING_VALUE",
      CompressionType: "None" || "Gzip",
      RecordWrapperType: "None" || "RecordIO",
      InputMode: "Pipe" || "File" || "FastFile",
      ShuffleConfig: { // ShuffleConfig
        Seed: Number("long"), // required
      },
    },
  ],
  OutputDataConfig: { // OutputDataConfig
    KmsKeyId: "STRING_VALUE",
    S3OutputPath: "STRING_VALUE", // required
    CompressionType: "GZIP" || "NONE",
  },
  ResourceConfig: { // ResourceConfig
    InstanceType: "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.p4de.24xlarge" || "ml.p5.48xlarge" || "ml.p5e.48xlarge" || "ml.p5en.48xlarge" || "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" || "ml.g5.xlarge" || "ml.g5.2xlarge" || "ml.g5.4xlarge" || "ml.g5.8xlarge" || "ml.g5.16xlarge" || "ml.g5.12xlarge" || "ml.g5.24xlarge" || "ml.g5.48xlarge" || "ml.g6.xlarge" || "ml.g6.2xlarge" || "ml.g6.4xlarge" || "ml.g6.8xlarge" || "ml.g6.16xlarge" || "ml.g6.12xlarge" || "ml.g6.24xlarge" || "ml.g6.48xlarge" || "ml.g6e.xlarge" || "ml.g6e.2xlarge" || "ml.g6e.4xlarge" || "ml.g6e.8xlarge" || "ml.g6e.16xlarge" || "ml.g6e.12xlarge" || "ml.g6e.24xlarge" || "ml.g6e.48xlarge" || "ml.trn1.2xlarge" || "ml.trn1.32xlarge" || "ml.trn1n.32xlarge" || "ml.trn2.48xlarge" || "ml.m6i.large" || "ml.m6i.xlarge" || "ml.m6i.2xlarge" || "ml.m6i.4xlarge" || "ml.m6i.8xlarge" || "ml.m6i.12xlarge" || "ml.m6i.16xlarge" || "ml.m6i.24xlarge" || "ml.m6i.32xlarge" || "ml.c6i.xlarge" || "ml.c6i.2xlarge" || "ml.c6i.8xlarge" || "ml.c6i.4xlarge" || "ml.c6i.12xlarge" || "ml.c6i.16xlarge" || "ml.c6i.24xlarge" || "ml.c6i.32xlarge" || "ml.r5d.large" || "ml.r5d.xlarge" || "ml.r5d.2xlarge" || "ml.r5d.4xlarge" || "ml.r5d.8xlarge" || "ml.r5d.12xlarge" || "ml.r5d.16xlarge" || "ml.r5d.24xlarge" || "ml.t3.medium" || "ml.t3.large" || "ml.t3.xlarge" || "ml.t3.2xlarge" || "ml.r5.large" || "ml.r5.xlarge" || "ml.r5.2xlarge" || "ml.r5.4xlarge" || "ml.r5.8xlarge" || "ml.r5.12xlarge" || "ml.r5.16xlarge" || "ml.r5.24xlarge",
    InstanceCount: Number("int"),
    VolumeSizeInGB: Number("int"), // required
    VolumeKmsKeyId: "STRING_VALUE",
    KeepAlivePeriodInSeconds: Number("int"),
    InstanceGroups: [ // InstanceGroups
      { // InstanceGroup
        InstanceType: "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.p4de.24xlarge" || "ml.p5.48xlarge" || "ml.p5e.48xlarge" || "ml.p5en.48xlarge" || "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" || "ml.g5.xlarge" || "ml.g5.2xlarge" || "ml.g5.4xlarge" || "ml.g5.8xlarge" || "ml.g5.16xlarge" || "ml.g5.12xlarge" || "ml.g5.24xlarge" || "ml.g5.48xlarge" || "ml.g6.xlarge" || "ml.g6.2xlarge" || "ml.g6.4xlarge" || "ml.g6.8xlarge" || "ml.g6.16xlarge" || "ml.g6.12xlarge" || "ml.g6.24xlarge" || "ml.g6.48xlarge" || "ml.g6e.xlarge" || "ml.g6e.2xlarge" || "ml.g6e.4xlarge" || "ml.g6e.8xlarge" || "ml.g6e.16xlarge" || "ml.g6e.12xlarge" || "ml.g6e.24xlarge" || "ml.g6e.48xlarge" || "ml.trn1.2xlarge" || "ml.trn1.32xlarge" || "ml.trn1n.32xlarge" || "ml.trn2.48xlarge" || "ml.m6i.large" || "ml.m6i.xlarge" || "ml.m6i.2xlarge" || "ml.m6i.4xlarge" || "ml.m6i.8xlarge" || "ml.m6i.12xlarge" || "ml.m6i.16xlarge" || "ml.m6i.24xlarge" || "ml.m6i.32xlarge" || "ml.c6i.xlarge" || "ml.c6i.2xlarge" || "ml.c6i.8xlarge" || "ml.c6i.4xlarge" || "ml.c6i.12xlarge" || "ml.c6i.16xlarge" || "ml.c6i.24xlarge" || "ml.c6i.32xlarge" || "ml.r5d.large" || "ml.r5d.xlarge" || "ml.r5d.2xlarge" || "ml.r5d.4xlarge" || "ml.r5d.8xlarge" || "ml.r5d.12xlarge" || "ml.r5d.16xlarge" || "ml.r5d.24xlarge" || "ml.t3.medium" || "ml.t3.large" || "ml.t3.xlarge" || "ml.t3.2xlarge" || "ml.r5.large" || "ml.r5.xlarge" || "ml.r5.2xlarge" || "ml.r5.4xlarge" || "ml.r5.8xlarge" || "ml.r5.12xlarge" || "ml.r5.16xlarge" || "ml.r5.24xlarge", // required
        InstanceCount: Number("int"), // required
        InstanceGroupName: "STRING_VALUE", // required
      },
    ],
    TrainingPlanArn: "STRING_VALUE",
  },
  VpcConfig: { // VpcConfig
    SecurityGroupIds: [ // VpcSecurityGroupIds // required
      "STRING_VALUE",
    ],
    Subnets: [ // Subnets // required
      "STRING_VALUE",
    ],
  },
  StoppingCondition: { // StoppingCondition
    MaxRuntimeInSeconds: Number("int"),
    MaxWaitTimeInSeconds: Number("int"),
    MaxPendingTimeInSeconds: Number("int"),
  },
  Tags: [ // TagList
    { // Tag
      Key: "STRING_VALUE", // required
      Value: "STRING_VALUE", // required
    },
  ],
  EnableNetworkIsolation: true || false,
  EnableInterContainerTrafficEncryption: true || false,
  EnableManagedSpotTraining: true || false,
  CheckpointConfig: { // CheckpointConfig
    S3Uri: "STRING_VALUE", // required
    LocalPath: "STRING_VALUE",
  },
  DebugHookConfig: { // DebugHookConfig
    LocalPath: "STRING_VALUE",
    S3OutputPath: "STRING_VALUE", // required
    HookParameters: { // HookParameters
      "<keys>": "STRING_VALUE",
    },
    CollectionConfigurations: [ // CollectionConfigurations
      { // CollectionConfiguration
        CollectionName: "STRING_VALUE",
        CollectionParameters: { // CollectionParameters
          "<keys>": "STRING_VALUE",
        },
      },
    ],
  },
  DebugRuleConfigurations: [ // DebugRuleConfigurations
    { // DebugRuleConfiguration
      RuleConfigurationName: "STRING_VALUE", // required
      LocalPath: "STRING_VALUE",
      S3OutputPath: "STRING_VALUE",
      RuleEvaluatorImage: "STRING_VALUE", // required
      InstanceType: "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" || "ml.g5.xlarge" || "ml.g5.2xlarge" || "ml.g5.4xlarge" || "ml.g5.8xlarge" || "ml.g5.16xlarge" || "ml.g5.12xlarge" || "ml.g5.24xlarge" || "ml.g5.48xlarge" || "ml.r5d.large" || "ml.r5d.xlarge" || "ml.r5d.2xlarge" || "ml.r5d.4xlarge" || "ml.r5d.8xlarge" || "ml.r5d.12xlarge" || "ml.r5d.16xlarge" || "ml.r5d.24xlarge" || "ml.g6.xlarge" || "ml.g6.2xlarge" || "ml.g6.4xlarge" || "ml.g6.8xlarge" || "ml.g6.12xlarge" || "ml.g6.16xlarge" || "ml.g6.24xlarge" || "ml.g6.48xlarge" || "ml.g6e.xlarge" || "ml.g6e.2xlarge" || "ml.g6e.4xlarge" || "ml.g6e.8xlarge" || "ml.g6e.12xlarge" || "ml.g6e.16xlarge" || "ml.g6e.24xlarge" || "ml.g6e.48xlarge" || "ml.m6i.large" || "ml.m6i.xlarge" || "ml.m6i.2xlarge" || "ml.m6i.4xlarge" || "ml.m6i.8xlarge" || "ml.m6i.12xlarge" || "ml.m6i.16xlarge" || "ml.m6i.24xlarge" || "ml.m6i.32xlarge" || "ml.c6i.xlarge" || "ml.c6i.2xlarge" || "ml.c6i.4xlarge" || "ml.c6i.8xlarge" || "ml.c6i.12xlarge" || "ml.c6i.16xlarge" || "ml.c6i.24xlarge" || "ml.c6i.32xlarge",
      VolumeSizeInGB: Number("int"),
      RuleParameters: { // RuleParameters
        "<keys>": "STRING_VALUE",
      },
    },
  ],
  TensorBoardOutputConfig: { // TensorBoardOutputConfig
    LocalPath: "STRING_VALUE",
    S3OutputPath: "STRING_VALUE", // required
  },
  ExperimentConfig: { // ExperimentConfig
    ExperimentName: "STRING_VALUE",
    TrialName: "STRING_VALUE",
    TrialComponentDisplayName: "STRING_VALUE",
    RunName: "STRING_VALUE",
  },
  ProfilerConfig: { // ProfilerConfig
    S3OutputPath: "STRING_VALUE",
    ProfilingIntervalInMilliseconds: Number("long"),
    ProfilingParameters: { // ProfilingParameters
      "<keys>": "STRING_VALUE",
    },
    DisableProfiler: true || false,
  },
  ProfilerRuleConfigurations: [ // ProfilerRuleConfigurations
    { // ProfilerRuleConfiguration
      RuleConfigurationName: "STRING_VALUE", // required
      LocalPath: "STRING_VALUE",
      S3OutputPath: "STRING_VALUE",
      RuleEvaluatorImage: "STRING_VALUE", // required
      InstanceType: "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" || "ml.g5.xlarge" || "ml.g5.2xlarge" || "ml.g5.4xlarge" || "ml.g5.8xlarge" || "ml.g5.16xlarge" || "ml.g5.12xlarge" || "ml.g5.24xlarge" || "ml.g5.48xlarge" || "ml.r5d.large" || "ml.r5d.xlarge" || "ml.r5d.2xlarge" || "ml.r5d.4xlarge" || "ml.r5d.8xlarge" || "ml.r5d.12xlarge" || "ml.r5d.16xlarge" || "ml.r5d.24xlarge" || "ml.g6.xlarge" || "ml.g6.2xlarge" || "ml.g6.4xlarge" || "ml.g6.8xlarge" || "ml.g6.12xlarge" || "ml.g6.16xlarge" || "ml.g6.24xlarge" || "ml.g6.48xlarge" || "ml.g6e.xlarge" || "ml.g6e.2xlarge" || "ml.g6e.4xlarge" || "ml.g6e.8xlarge" || "ml.g6e.12xlarge" || "ml.g6e.16xlarge" || "ml.g6e.24xlarge" || "ml.g6e.48xlarge" || "ml.m6i.large" || "ml.m6i.xlarge" || "ml.m6i.2xlarge" || "ml.m6i.4xlarge" || "ml.m6i.8xlarge" || "ml.m6i.12xlarge" || "ml.m6i.16xlarge" || "ml.m6i.24xlarge" || "ml.m6i.32xlarge" || "ml.c6i.xlarge" || "ml.c6i.2xlarge" || "ml.c6i.4xlarge" || "ml.c6i.8xlarge" || "ml.c6i.12xlarge" || "ml.c6i.16xlarge" || "ml.c6i.24xlarge" || "ml.c6i.32xlarge",
      VolumeSizeInGB: Number("int"),
      RuleParameters: {
        "<keys>": "STRING_VALUE",
      },
    },
  ],
  Environment: { // TrainingEnvironmentMap
    "<keys>": "STRING_VALUE",
  },
  RetryStrategy: { // RetryStrategy
    MaximumRetryAttempts: Number("int"), // required
  },
  RemoteDebugConfig: { // RemoteDebugConfig
    EnableRemoteDebug: true || false,
  },
  InfraCheckConfig: { // InfraCheckConfig
    EnableInfraCheck: true || false,
  },
  SessionChainingConfig: { // SessionChainingConfig
    EnableSessionTagChaining: true || false,
  },
};
const command = new CreateTrainingJobCommand(input);
const response = await client.send(command);
// { // CreateTrainingJobResponse
//   TrainingJobArn: "STRING_VALUE", // required
// };

CreateTrainingJobCommand Input

See CreateTrainingJobCommandInput for more details

Parameter	Type	Description
`AlgorithmSpecification` Required	`AlgorithmSpecification \| undefined`	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 SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .
`OutputDataConfig` Required	`OutputDataConfig \| undefined`	Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
`ResourceConfig` Required	`ResourceConfig \| undefined`	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 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.
`RoleArn` Required	`string \| undefined`	The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. During model training, 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 SageMaker Roles . To be able to pass this role to SageMaker, the caller of this API must have the `iam:PassRole` permission.
`StoppingCondition` Required	`StoppingCondition \| undefined`	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, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, 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.
`TrainingJobName` Required	`string \| undefined`	The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
`CheckpointConfig`	`CheckpointConfig \| undefined`	Contains information about the output location for managed spot training checkpoint data.
`DebugHookConfig`	`DebugHookConfig \| undefined`	Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the `DebugHookConfig` parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
`DebugRuleConfigurations`	`DebugRuleConfiguration[] \| undefined`	Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
`EnableInterContainerTrafficEncryption`	`boolean \| undefined`	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 .
`EnableManagedSpotTraining`	`boolean \| undefined`	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`	`boolean \| undefined`	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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
`Environment`	`Record<string, string> \| undefined`	The environment variables to set in the Docker container.
`ExperimentConfig`	`ExperimentConfig \| undefined`	Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob
`HyperParameters`	`Record<string, string> \| undefined`	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 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`. Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
`InfraCheckConfig`	`InfraCheckConfig \| undefined`	Contains information about the infrastructure health check configuration for the training job.
`InputDataConfig`	`Channel[] \| undefined`	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, 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 are available as input streams. They do not need to be downloaded. Your input must be in the same Amazon Web Services region as your training job.
`ProfilerConfig`	`ProfilerConfig \| undefined`	Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
`ProfilerRuleConfigurations`	`ProfilerRuleConfiguration[] \| undefined`	Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
`RemoteDebugConfig`	`RemoteDebugConfig \| undefined`	Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging .
`RetryStrategy`	`RetryStrategy \| undefined`	The number of times to retry the job when the job fails due to an `InternalServerError`.
`SessionChainingConfig`	`SessionChainingConfig \| undefined`	Contains information about attribute-based access control (ABAC) for the training job.
`Tags`	`Tag[] \| undefined`	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 .
`TensorBoardOutputConfig`	`TensorBoardOutputConfig \| undefined`	Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
`VpcConfig`	`VpcConfig \| undefined`	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 .

CreateTrainingJobCommand Output

See CreateTrainingJobCommandOutput for details

Parameter	Type	Description
`$metadata` Required	`ResponseMetadata`	Metadata pertaining to this request.
`TrainingJobArn` Required	`string \| undefined`	The Amazon Resource Name (ARN) of the training job.

Throws

Name	Fault	Details
ResourceInUse	client	Resource being accessed is in use.
ResourceLimitExceeded	client	You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
ResourceNotFound	client	Resource being access is not found.
SageMakerServiceException		Base exception class for all service exceptions from SageMaker service.

Getting Started

References

CreateTrainingJobCommand

Example Syntax

CreateTrainingJobCommand Input

CreateTrainingJobCommand Output

Throws

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