AWS SDK Version 3 for .NET
API Reference

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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:

For more information about Amazon SageMaker, see How It Works.

Inheritance Hierarchy


Namespace: Amazon.SageMaker.Model
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z


public class CreateTrainingJobRequest : AmazonSageMakerRequest

The CreateTrainingJobRequest type exposes the following members


Public Method CreateTrainingJobRequest()


Public Property AlgorithmSpecification Amazon.SageMaker.Model.AlgorithmSpecification

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.

Public Property HyperParameters System.Collections.Generic.Dictionary<System.String, System.String>

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


Public Property InputDataConfig System.Collections.Generic.List<Amazon.SageMaker.Model.Channel>

Gets and sets the property InputDataConfig.

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 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.

Public Property OutputDataConfig Amazon.SageMaker.Model.OutputDataConfig

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.

Public Property ResourceConfig Amazon.SageMaker.Model.ResourceConfig

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 File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

Public Property RoleArn System.String

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 the iam:PassRole permission.

Public Property StoppingCondition Amazon.SageMaker.Model.StoppingCondition

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 SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

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 CreateModel API.

Public Property Tags System.Collections.Generic.List<Amazon.SageMaker.Model.Tag>

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.

Public Property TrainingJobName System.String

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.

Public Property VpcConfig Amazon.SageMaker.Model.VpcConfig

Gets and sets the property 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 train-vpc

Version Information

.NET Standard:
Supported in: 1.3

.NET Framework:
Supported in: 4.5, 4.0, 3.5

Portable Class Library:
Supported in: Windows Store Apps
Supported in: Windows Phone 8.1
Supported in: Xamarin Android
Supported in: Xamarin iOS (Unified)
Supported in: Xamarin.Forms