AWS services or capabilities described in AWS Documentation may vary by region/location. Click Getting Started with Amazon AWS to see specific differences applicable to the China (Beijing) Region.
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.
This is an asynchronous operation using the standard naming convention for .NET 4.5 or higher. For .NET 3.5 the operation is implemented as a pair of methods using the standard naming convention of BeginCreateTrainingJob and EndCreateTrainingJob.
Namespace: Amazon.SageMaker
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z
public virtual Task<CreateTrainingJobResponse> CreateTrainingJobAsync( CreateTrainingJobRequest request, CancellationToken cancellationToken )
Container for the necessary parameters to execute the CreateTrainingJob service method.
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
| Exception | Condition |
|---|---|
| ResourceInUseException | Resource being accessed is in use. |
| ResourceLimitExceededException | You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. |
| ResourceNotFoundException | Resource being access is not found. |
.NET:
Supported in: 8.0 and newer, Core 3.1
.NET Standard:
Supported in: 2.0
.NET Framework:
Supported in: 4.5 and newer