Post x amz targetsagemaker.createtrainingjob

v1

latestOpenAPI 3.0.0Apache 2.0 License2026-07-143021,9861.6 MB

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.

post/#X-Amz-Target=SageMaker.CreateTrainingJob

Headers

X-Amz-Target'SageMaker.CreateTrainingJob' required

Request body

TrainingJobNamestring required

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

HyperParametersobject

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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>. 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 <code>Length Constraint</code>. <important> 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. </important>

RoleArnstring required

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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">SageMaker Roles</a>. <note> To be able to pass this role to SageMaker, the caller of this API must have the <code>iam:PassRole</code> permission. </note>

EnableNetworkIsolationboolean

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.

EnableInterContainerTrafficEncryptionboolean

To encrypt all communications between ML compute instances in distributed training, choose <code>True</code>. 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html">Protect Communications Between ML Compute Instances in a Distributed Training Job</a>.

EnableManagedSpotTrainingboolean

To train models using managed spot training, choose <code>True</code>. 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.

Environmentobject

The environment variables to set in the Docker container.

Response

Success

TrainingJobArnstring required

The Amazon Resource Name (ARN) of the training job.