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Container for the parameters to the CreateTrainingJob operation. 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.
Namespace: Amazon.SageMaker.Model
Assembly: AWSSDK.SageMaker.dll
Version: 3.x.y.z
public class CreateTrainingJobRequest : AmazonSageMakerRequest IAmazonWebServiceRequest
The CreateTrainingJobRequest type exposes the following members
Name | Description | |
---|---|---|
CreateTrainingJobRequest() |
Name | Type | Description | |
---|---|---|---|
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 SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker. |
|
CheckpointConfig | Amazon.SageMaker.Model.CheckpointConfig |
Gets and sets the property CheckpointConfig. Contains information about the output location for managed spot training checkpoint data. |
|
DebugHookConfig | Amazon.SageMaker.Model.DebugHookConfig |
Gets and sets the property DebugHookConfig. |
|
DebugRuleConfigurations | System.Collections.Generic.List<Amazon.SageMaker.Model.DebugRuleConfiguration> |
Gets and sets the property DebugRuleConfigurations. Configuration information for Amazon SageMaker Debugger rules for debugging output tensors. |
|
EnableInterContainerTrafficEncryption | System.Boolean |
Gets and sets the property EnableInterContainerTrafficEncryption.
To encrypt all communications between ML compute instances in distributed training,
choose |
|
EnableManagedSpotTraining | System.Boolean |
Gets and sets the property EnableManagedSpotTraining.
To train models using managed spot training, choose 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 | System.Boolean |
Gets and sets the property EnableNetworkIsolation. 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 | System.Collections.Generic.Dictionary<System.String, System.String> |
Gets and sets the property Environment. The environment variables to set in the Docker container. |
|
ExperimentConfig | Amazon.SageMaker.Model.ExperimentConfig |
Gets and sets the property ExperimentConfig. |
|
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 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 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 | Amazon.SageMaker.Model.InfraCheckConfig |
Gets and sets the property InfraCheckConfig. Contains information about the infrastructure health check configuration for the training job. |
|
InputDataConfig | System.Collections.Generic.List<Amazon.SageMaker.Model.Channel> |
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, 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. |
|
OutputDataConfig | Amazon.SageMaker.Model.OutputDataConfig |
Gets and sets the property OutputDataConfig. Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts. |
|
ProfilerConfig | Amazon.SageMaker.Model.ProfilerConfig |
Gets and sets the property ProfilerConfig. |
|
ProfilerRuleConfigurations | System.Collections.Generic.List<Amazon.SageMaker.Model.ProfilerRuleConfiguration> |
Gets and sets the property ProfilerRuleConfigurations. Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics. |
|
RemoteDebugConfig | Amazon.SageMaker.Model.RemoteDebugConfig |
Gets and sets the property RemoteDebugConfig. 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. |
|
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 SageMaker to use
the ML storage volume to store the training data, choose |
|
RetryStrategy | Amazon.SageMaker.Model.RetryStrategy |
Gets and sets the property RetryStrategy.
The number of times to retry the job when the job fails due to an |
|
RoleArn | System.String |
Gets and sets the property RoleArn. 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 |
|
SessionChainingConfig | Amazon.SageMaker.Model.SessionChainingConfig |
Gets and sets the property SessionChainingConfig. Contains information about attribute-based access control (ABAC) for the training job. |
|
StoppingCondition | Amazon.SageMaker.Model.StoppingCondition |
Gets and sets the property StoppingCondition. 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 |
|
Tags | System.Collections.Generic.List<Amazon.SageMaker.Model.Tag> |
Gets and sets the property Tags. 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 | Amazon.SageMaker.Model.TensorBoardOutputConfig |
Gets and sets the property TensorBoardOutputConfig. |
|
TrainingJobName | System.String |
Gets and sets the property TrainingJobName. The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. |
|
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 Protect Training Jobs by Using an Amazon Virtual Private Cloud. |
.NET:
Supported in: 8.0 and newer, Core 3.1
.NET Standard:
Supported in: 2.0
.NET Framework:
Supported in: 4.5 and newer, 3.5