Container for the parameters to the CreateTrainingJob operation. Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to influence
the quality of the final model. For a list of hyperparameters for each training algorithm
provided by Amazon SageMaker, see Algorithms.
InputDataConfig - Describes the training dataset and the Amazon S3 location
where it is stored.
OutputDataConfig - Identifies the Amazon S3 location where you want
Amazon 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.
RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes
to perform tasks on your behalf during model training. You must grant this role the
necessary permissions so that Amazon SageMaker can successfully complete model training.
StoppingCondition - Sets a duration for training. Use this parameter
to cap model training costs.
For more information about Amazon SageMaker, see How It Works.
public class CreateTrainingJobRequest : AmazonSageMakerRequest IAmazonWebServiceRequest
The CreateTrainingJobRequest type exposes the following members
Gets and sets the property 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.
Gets and sets the property 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.
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
Gets and sets the property InputDataConfig.
An array of
Algorithms can accept input data from one or more channels. For example, an algorithm
might have two channels of input data,
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.
Gets and sets the property OutputDataConfig.
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
Gets and sets the property 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
Gets and sets the property RoleArn.
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
Gets and sets the property StoppingCondition.
Sets a duration for training. Use this parameter to cap model training costs. To stop
a job, Amazon SageMaker sends the algorithm the
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
Gets and sets the property Tags.
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Gets and sets the property TrainingJobName.
The name of the training job. The name must be unique within an AWS Region in an AWS account.
Gets and sets the property VpcConfig.
Supported in: 1.3
Supported in: 4.5, 4.0, 3.5
Supported in: Windows Store Apps
Supported in: Windows Phone 8.1
Supported in: Xamarin Android
Supported in: Xamarin iOS (Unified)
Supported in: Xamarin.Forms