AWS SDK for C++  1.9.20
AWS SDK for C++
Public Member Functions | List of all members
Aws::SageMaker::Model::CreateTrainingJobRequest Class Reference

#include <CreateTrainingJobRequest.h>

+ Inheritance diagram for Aws::SageMaker::Model::CreateTrainingJobRequest:

Public Member Functions

 CreateTrainingJobRequest ()
 
virtual const char * GetServiceRequestName () const override
 
Aws::String SerializePayload () const override
 
Aws::Http::HeaderValueCollection GetRequestSpecificHeaders () const override
 
const Aws::StringGetTrainingJobName () const
 
bool TrainingJobNameHasBeenSet () const
 
void SetTrainingJobName (const Aws::String &value)
 
void SetTrainingJobName (Aws::String &&value)
 
void SetTrainingJobName (const char *value)
 
CreateTrainingJobRequestWithTrainingJobName (const Aws::String &value)
 
CreateTrainingJobRequestWithTrainingJobName (Aws::String &&value)
 
CreateTrainingJobRequestWithTrainingJobName (const char *value)
 
const Aws::Map< Aws::String, Aws::String > & GetHyperParameters () const
 
bool HyperParametersHasBeenSet () const
 
void SetHyperParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetHyperParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateTrainingJobRequestWithHyperParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
CreateTrainingJobRequestWithHyperParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateTrainingJobRequestAddHyperParameters (const Aws::String &key, const Aws::String &value)
 
CreateTrainingJobRequestAddHyperParameters (Aws::String &&key, const Aws::String &value)
 
CreateTrainingJobRequestAddHyperParameters (const Aws::String &key, Aws::String &&value)
 
CreateTrainingJobRequestAddHyperParameters (Aws::String &&key, Aws::String &&value)
 
CreateTrainingJobRequestAddHyperParameters (const char *key, Aws::String &&value)
 
CreateTrainingJobRequestAddHyperParameters (Aws::String &&key, const char *value)
 
CreateTrainingJobRequestAddHyperParameters (const char *key, const char *value)
 
const AlgorithmSpecificationGetAlgorithmSpecification () const
 
bool AlgorithmSpecificationHasBeenSet () const
 
void SetAlgorithmSpecification (const AlgorithmSpecification &value)
 
void SetAlgorithmSpecification (AlgorithmSpecification &&value)
 
CreateTrainingJobRequestWithAlgorithmSpecification (const AlgorithmSpecification &value)
 
CreateTrainingJobRequestWithAlgorithmSpecification (AlgorithmSpecification &&value)
 
const Aws::StringGetRoleArn () const
 
bool RoleArnHasBeenSet () const
 
void SetRoleArn (const Aws::String &value)
 
void SetRoleArn (Aws::String &&value)
 
void SetRoleArn (const char *value)
 
CreateTrainingJobRequestWithRoleArn (const Aws::String &value)
 
CreateTrainingJobRequestWithRoleArn (Aws::String &&value)
 
CreateTrainingJobRequestWithRoleArn (const char *value)
 
const Aws::Vector< Channel > & GetInputDataConfig () const
 
bool InputDataConfigHasBeenSet () const
 
void SetInputDataConfig (const Aws::Vector< Channel > &value)
 
void SetInputDataConfig (Aws::Vector< Channel > &&value)
 
CreateTrainingJobRequestWithInputDataConfig (const Aws::Vector< Channel > &value)
 
CreateTrainingJobRequestWithInputDataConfig (Aws::Vector< Channel > &&value)
 
CreateTrainingJobRequestAddInputDataConfig (const Channel &value)
 
CreateTrainingJobRequestAddInputDataConfig (Channel &&value)
 
const OutputDataConfigGetOutputDataConfig () const
 
bool OutputDataConfigHasBeenSet () const
 
void SetOutputDataConfig (const OutputDataConfig &value)
 
void SetOutputDataConfig (OutputDataConfig &&value)
 
CreateTrainingJobRequestWithOutputDataConfig (const OutputDataConfig &value)
 
CreateTrainingJobRequestWithOutputDataConfig (OutputDataConfig &&value)
 
const ResourceConfigGetResourceConfig () const
 
bool ResourceConfigHasBeenSet () const
 
void SetResourceConfig (const ResourceConfig &value)
 
void SetResourceConfig (ResourceConfig &&value)
 
CreateTrainingJobRequestWithResourceConfig (const ResourceConfig &value)
 
CreateTrainingJobRequestWithResourceConfig (ResourceConfig &&value)
 
const VpcConfigGetVpcConfig () const
 
bool VpcConfigHasBeenSet () const
 
void SetVpcConfig (const VpcConfig &value)
 
void SetVpcConfig (VpcConfig &&value)
 
CreateTrainingJobRequestWithVpcConfig (const VpcConfig &value)
 
CreateTrainingJobRequestWithVpcConfig (VpcConfig &&value)
 
const StoppingConditionGetStoppingCondition () const
 
bool StoppingConditionHasBeenSet () const
 
void SetStoppingCondition (const StoppingCondition &value)
 
void SetStoppingCondition (StoppingCondition &&value)
 
CreateTrainingJobRequestWithStoppingCondition (const StoppingCondition &value)
 
CreateTrainingJobRequestWithStoppingCondition (StoppingCondition &&value)
 
const Aws::Vector< Tag > & GetTags () const
 
bool TagsHasBeenSet () const
 
void SetTags (const Aws::Vector< Tag > &value)
 
void SetTags (Aws::Vector< Tag > &&value)
 
CreateTrainingJobRequestWithTags (const Aws::Vector< Tag > &value)
 
CreateTrainingJobRequestWithTags (Aws::Vector< Tag > &&value)
 
CreateTrainingJobRequestAddTags (const Tag &value)
 
CreateTrainingJobRequestAddTags (Tag &&value)
 
bool GetEnableNetworkIsolation () const
 
bool EnableNetworkIsolationHasBeenSet () const
 
void SetEnableNetworkIsolation (bool value)
 
CreateTrainingJobRequestWithEnableNetworkIsolation (bool value)
 
bool GetEnableInterContainerTrafficEncryption () const
 
bool EnableInterContainerTrafficEncryptionHasBeenSet () const
 
void SetEnableInterContainerTrafficEncryption (bool value)
 
CreateTrainingJobRequestWithEnableInterContainerTrafficEncryption (bool value)
 
bool GetEnableManagedSpotTraining () const
 
bool EnableManagedSpotTrainingHasBeenSet () const
 
void SetEnableManagedSpotTraining (bool value)
 
CreateTrainingJobRequestWithEnableManagedSpotTraining (bool value)
 
const CheckpointConfigGetCheckpointConfig () const
 
bool CheckpointConfigHasBeenSet () const
 
void SetCheckpointConfig (const CheckpointConfig &value)
 
void SetCheckpointConfig (CheckpointConfig &&value)
 
CreateTrainingJobRequestWithCheckpointConfig (const CheckpointConfig &value)
 
CreateTrainingJobRequestWithCheckpointConfig (CheckpointConfig &&value)
 
const DebugHookConfigGetDebugHookConfig () const
 
bool DebugHookConfigHasBeenSet () const
 
void SetDebugHookConfig (const DebugHookConfig &value)
 
void SetDebugHookConfig (DebugHookConfig &&value)
 
CreateTrainingJobRequestWithDebugHookConfig (const DebugHookConfig &value)
 
CreateTrainingJobRequestWithDebugHookConfig (DebugHookConfig &&value)
 
const Aws::Vector< DebugRuleConfiguration > & GetDebugRuleConfigurations () const
 
bool DebugRuleConfigurationsHasBeenSet () const
 
void SetDebugRuleConfigurations (const Aws::Vector< DebugRuleConfiguration > &value)
 
void SetDebugRuleConfigurations (Aws::Vector< DebugRuleConfiguration > &&value)
 
CreateTrainingJobRequestWithDebugRuleConfigurations (const Aws::Vector< DebugRuleConfiguration > &value)
 
CreateTrainingJobRequestWithDebugRuleConfigurations (Aws::Vector< DebugRuleConfiguration > &&value)
 
CreateTrainingJobRequestAddDebugRuleConfigurations (const DebugRuleConfiguration &value)
 
CreateTrainingJobRequestAddDebugRuleConfigurations (DebugRuleConfiguration &&value)
 
const TensorBoardOutputConfigGetTensorBoardOutputConfig () const
 
bool TensorBoardOutputConfigHasBeenSet () const
 
void SetTensorBoardOutputConfig (const TensorBoardOutputConfig &value)
 
void SetTensorBoardOutputConfig (TensorBoardOutputConfig &&value)
 
CreateTrainingJobRequestWithTensorBoardOutputConfig (const TensorBoardOutputConfig &value)
 
CreateTrainingJobRequestWithTensorBoardOutputConfig (TensorBoardOutputConfig &&value)
 
const ExperimentConfigGetExperimentConfig () const
 
bool ExperimentConfigHasBeenSet () const
 
void SetExperimentConfig (const ExperimentConfig &value)
 
void SetExperimentConfig (ExperimentConfig &&value)
 
CreateTrainingJobRequestWithExperimentConfig (const ExperimentConfig &value)
 
CreateTrainingJobRequestWithExperimentConfig (ExperimentConfig &&value)
 
const ProfilerConfigGetProfilerConfig () const
 
bool ProfilerConfigHasBeenSet () const
 
void SetProfilerConfig (const ProfilerConfig &value)
 
void SetProfilerConfig (ProfilerConfig &&value)
 
CreateTrainingJobRequestWithProfilerConfig (const ProfilerConfig &value)
 
CreateTrainingJobRequestWithProfilerConfig (ProfilerConfig &&value)
 
const Aws::Vector< ProfilerRuleConfiguration > & GetProfilerRuleConfigurations () const
 
bool ProfilerRuleConfigurationsHasBeenSet () const
 
void SetProfilerRuleConfigurations (const Aws::Vector< ProfilerRuleConfiguration > &value)
 
void SetProfilerRuleConfigurations (Aws::Vector< ProfilerRuleConfiguration > &&value)
 
CreateTrainingJobRequestWithProfilerRuleConfigurations (const Aws::Vector< ProfilerRuleConfiguration > &value)
 
CreateTrainingJobRequestWithProfilerRuleConfigurations (Aws::Vector< ProfilerRuleConfiguration > &&value)
 
CreateTrainingJobRequestAddProfilerRuleConfigurations (const ProfilerRuleConfiguration &value)
 
CreateTrainingJobRequestAddProfilerRuleConfigurations (ProfilerRuleConfiguration &&value)
 
const Aws::Map< Aws::String, Aws::String > & GetEnvironment () const
 
bool EnvironmentHasBeenSet () const
 
void SetEnvironment (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetEnvironment (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateTrainingJobRequestWithEnvironment (const Aws::Map< Aws::String, Aws::String > &value)
 
CreateTrainingJobRequestWithEnvironment (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateTrainingJobRequestAddEnvironment (const Aws::String &key, const Aws::String &value)
 
CreateTrainingJobRequestAddEnvironment (Aws::String &&key, const Aws::String &value)
 
CreateTrainingJobRequestAddEnvironment (const Aws::String &key, Aws::String &&value)
 
CreateTrainingJobRequestAddEnvironment (Aws::String &&key, Aws::String &&value)
 
CreateTrainingJobRequestAddEnvironment (const char *key, Aws::String &&value)
 
CreateTrainingJobRequestAddEnvironment (Aws::String &&key, const char *value)
 
CreateTrainingJobRequestAddEnvironment (const char *key, const char *value)
 
const RetryStrategyGetRetryStrategy () const
 
bool RetryStrategyHasBeenSet () const
 
void SetRetryStrategy (const RetryStrategy &value)
 
void SetRetryStrategy (RetryStrategy &&value)
 
CreateTrainingJobRequestWithRetryStrategy (const RetryStrategy &value)
 
CreateTrainingJobRequestWithRetryStrategy (RetryStrategy &&value)
 
- Public Member Functions inherited from Aws::SageMaker::SageMakerRequest
virtual ~SageMakerRequest ()
 
void AddParametersToRequest (Aws::Http::HttpRequest &httpRequest) const
 
Aws::Http::HeaderValueCollection GetHeaders () const override
 
- Public Member Functions inherited from Aws::AmazonSerializableWebServiceRequest
 AmazonSerializableWebServiceRequest ()
 
virtual ~AmazonSerializableWebServiceRequest ()
 
std::shared_ptr< Aws::IOStreamGetBody () const override
 
- Public Member Functions inherited from Aws::AmazonWebServiceRequest
 AmazonWebServiceRequest ()
 
virtual ~AmazonWebServiceRequest ()=default
 
virtual void AddQueryStringParameters (Aws::Http::URI &uri) const
 
virtual void PutToPresignedUrl (Aws::Http::URI &uri) const
 
virtual bool IsStreaming () const
 
virtual bool IsEventStreamRequest () const
 
virtual bool SignBody () const
 
virtual bool IsChunked () const
 
virtual void SetRequestSignedHandler (const RequestSignedHandler &handler)
 
virtual const RequestSignedHandlerGetRequestSignedHandler () const
 
const Aws::IOStreamFactoryGetResponseStreamFactory () const
 
void SetResponseStreamFactory (const Aws::IOStreamFactory &factory)
 
virtual void SetDataReceivedEventHandler (const Aws::Http::DataReceivedEventHandler &dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (const Aws::Http::DataSentEventHandler &dataSentEventHandler)
 
virtual void SetContinueRequestHandler (const Aws::Http::ContinueRequestHandler &continueRequestHandler)
 
virtual void SetDataReceivedEventHandler (Aws::Http::DataReceivedEventHandler &&dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (Aws::Http::DataSentEventHandler &&dataSentEventHandler)
 
virtual void SetContinueRequestHandler (Aws::Http::ContinueRequestHandler &&continueRequestHandler)
 
virtual void SetRequestRetryHandler (const RequestRetryHandler &handler)
 
virtual void SetRequestRetryHandler (RequestRetryHandler &&handler)
 
virtual const Aws::Http::DataReceivedEventHandlerGetDataReceivedEventHandler () const
 
virtual const Aws::Http::DataSentEventHandlerGetDataSentEventHandler () const
 
virtual const Aws::Http::ContinueRequestHandlerGetContinueRequestHandler () const
 
virtual const RequestRetryHandlerGetRequestRetryHandler () const
 
virtual bool ShouldComputeContentMd5 () const
 

Additional Inherited Members

- Protected Member Functions inherited from Aws::AmazonWebServiceRequest
virtual void DumpBodyToUrl (Aws::Http::URI &uri) const
 

Detailed Description

Definition at line 38 of file CreateTrainingJobRequest.h.

Constructor & Destructor Documentation

◆ CreateTrainingJobRequest()

Aws::SageMaker::Model::CreateTrainingJobRequest::CreateTrainingJobRequest ( )

Member Function Documentation

◆ AddDebugRuleConfigurations() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddDebugRuleConfigurations ( const DebugRuleConfiguration value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1110 of file CreateTrainingJobRequest.h.

◆ AddDebugRuleConfigurations() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddDebugRuleConfigurations ( DebugRuleConfiguration &&  value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1116 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [1/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( Aws::String &&  key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1273 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [2/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( Aws::String &&  key,
const Aws::String value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1263 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [3/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( Aws::String &&  key,
const char *  value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1283 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [4/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( const Aws::String key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1268 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [5/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( const Aws::String key,
const Aws::String value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1258 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [6/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( const char *  key,
Aws::String &&  value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1278 of file CreateTrainingJobRequest.h.

◆ AddEnvironment() [7/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddEnvironment ( const char *  key,
const char *  value 
)
inline

The environment variables to set in the Docker container.

Definition at line 1288 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [1/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( Aws::String &&  key,
Aws::String &&  value 
)
inline

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 Length Constraint.

Definition at line 211 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [2/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( Aws::String &&  key,
const Aws::String value 
)
inline

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 Length Constraint.

Definition at line 189 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [3/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( Aws::String &&  key,
const char *  value 
)
inline

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 Length Constraint.

Definition at line 233 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [4/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( const Aws::String key,
Aws::String &&  value 
)
inline

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 Length Constraint.

Definition at line 200 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [5/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( const Aws::String key,
const Aws::String value 
)
inline

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 Length Constraint.

Definition at line 178 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [6/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( const char *  key,
Aws::String &&  value 
)
inline

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 Length Constraint.

Definition at line 222 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [7/7]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddHyperParameters ( const char *  key,
const char *  value 
)
inline

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 Length Constraint.

Definition at line 244 of file CreateTrainingJobRequest.h.

◆ AddInputDataConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddInputDataConfig ( Channel &&  value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 569 of file CreateTrainingJobRequest.h.

◆ AddInputDataConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddInputDataConfig ( const Channel value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 552 of file CreateTrainingJobRequest.h.

◆ AddProfilerRuleConfigurations() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddProfilerRuleConfigurations ( const ProfilerRuleConfiguration value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1216 of file CreateTrainingJobRequest.h.

◆ AddProfilerRuleConfigurations() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddProfilerRuleConfigurations ( ProfilerRuleConfiguration &&  value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1222 of file CreateTrainingJobRequest.h.

◆ AddTags() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddTags ( const Tag value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 859 of file CreateTrainingJobRequest.h.

◆ AddTags() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::AddTags ( Tag &&  value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 868 of file CreateTrainingJobRequest.h.

◆ AlgorithmSpecificationHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::AlgorithmSpecificationHasBeenSet ( ) const
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 267 of file CreateTrainingJobRequest.h.

◆ CheckpointConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::CheckpointConfigHasBeenSet ( ) const
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1024 of file CreateTrainingJobRequest.h.

◆ DebugHookConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::DebugHookConfigHasBeenSet ( ) const
inline

Definition at line 1055 of file CreateTrainingJobRequest.h.

◆ DebugRuleConfigurationsHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::DebugRuleConfigurationsHasBeenSet ( ) const
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1080 of file CreateTrainingJobRequest.h.

◆ EnableInterContainerTrafficEncryptionHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::EnableInterContainerTrafficEncryptionHasBeenSet ( ) const
inline

To encrypt all communications between ML compute instances in distributed training, choose True. 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 Protect Communications Between ML Compute Instances in a Distributed Training Job.

Definition at line 934 of file CreateTrainingJobRequest.h.

◆ EnableManagedSpotTrainingHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::EnableManagedSpotTrainingHasBeenSet ( ) const
inline

To train models using managed spot training, choose True. 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.

Definition at line 985 of file CreateTrainingJobRequest.h.

◆ EnableNetworkIsolationHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::EnableNetworkIsolationHasBeenSet ( ) const
inline

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Definition at line 889 of file CreateTrainingJobRequest.h.

◆ EnvironmentHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::EnvironmentHasBeenSet ( ) const
inline

The environment variables to set in the Docker container.

Definition at line 1233 of file CreateTrainingJobRequest.h.

◆ ExperimentConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::ExperimentConfigHasBeenSet ( ) const
inline

Definition at line 1142 of file CreateTrainingJobRequest.h.

◆ GetAlgorithmSpecification()

const AlgorithmSpecification& Aws::SageMaker::Model::CreateTrainingJobRequest::GetAlgorithmSpecification ( ) const
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 256 of file CreateTrainingJobRequest.h.

◆ GetCheckpointConfig()

const CheckpointConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetCheckpointConfig ( ) const
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1018 of file CreateTrainingJobRequest.h.

◆ GetDebugHookConfig()

const DebugHookConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetDebugHookConfig ( ) const
inline

Definition at line 1052 of file CreateTrainingJobRequest.h.

◆ GetDebugRuleConfigurations()

const Aws::Vector<DebugRuleConfiguration>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetDebugRuleConfigurations ( ) const
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1074 of file CreateTrainingJobRequest.h.

◆ GetEnableInterContainerTrafficEncryption()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::GetEnableInterContainerTrafficEncryption ( ) const
inline

To encrypt all communications between ML compute instances in distributed training, choose True. 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 Protect Communications Between ML Compute Instances in a Distributed Training Job.

Definition at line 922 of file CreateTrainingJobRequest.h.

◆ GetEnableManagedSpotTraining()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::GetEnableManagedSpotTraining ( ) const
inline

To train models using managed spot training, choose True. 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.

Definition at line 972 of file CreateTrainingJobRequest.h.

◆ GetEnableNetworkIsolation()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::GetEnableNetworkIsolation ( ) const
inline

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Definition at line 879 of file CreateTrainingJobRequest.h.

◆ GetEnvironment()

const Aws::Map<Aws::String, Aws::String>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetEnvironment ( ) const
inline

The environment variables to set in the Docker container.

Definition at line 1228 of file CreateTrainingJobRequest.h.

◆ GetExperimentConfig()

const ExperimentConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetExperimentConfig ( ) const
inline

Definition at line 1139 of file CreateTrainingJobRequest.h.

◆ GetHyperParameters()

const Aws::Map<Aws::String, Aws::String>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetHyperParameters ( ) const
inline

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 Length Constraint.

Definition at line 112 of file CreateTrainingJobRequest.h.

◆ GetInputDataConfig()

const Aws::Vector<Channel>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetInputDataConfig ( ) const
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 450 of file CreateTrainingJobRequest.h.

◆ GetOutputDataConfig()

const OutputDataConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetOutputDataConfig ( ) const
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 576 of file CreateTrainingJobRequest.h.

◆ GetProfilerConfig()

const ProfilerConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetProfilerConfig ( ) const
inline

Definition at line 1158 of file CreateTrainingJobRequest.h.

◆ GetProfilerRuleConfigurations()

const Aws::Vector<ProfilerRuleConfiguration>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetProfilerRuleConfigurations ( ) const
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1180 of file CreateTrainingJobRequest.h.

◆ GetRequestSpecificHeaders()

Aws::Http::HeaderValueCollection Aws::SageMaker::Model::CreateTrainingJobRequest::GetRequestSpecificHeaders ( ) const
overridevirtual

Reimplemented from Aws::SageMaker::SageMakerRequest.

◆ GetResourceConfig()

const ResourceConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetResourceConfig ( ) const
inline

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.

Definition at line 618 of file CreateTrainingJobRequest.h.

◆ GetRetryStrategy()

const RetryStrategy& Aws::SageMaker::Model::CreateTrainingJobRequest::GetRetryStrategy ( ) const
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1295 of file CreateTrainingJobRequest.h.

◆ GetRoleArn()

const Aws::String& Aws::SageMaker::Model::CreateTrainingJobRequest::GetRoleArn ( ) const
inline

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.

Definition at line 327 of file CreateTrainingJobRequest.h.

◆ GetServiceRequestName()

virtual const char* Aws::SageMaker::Model::CreateTrainingJobRequest::GetServiceRequestName ( ) const
inlineoverridevirtual

Implements Aws::AmazonWebServiceRequest.

Definition at line 47 of file CreateTrainingJobRequest.h.

◆ GetStoppingCondition()

const StoppingCondition& Aws::SageMaker::Model::CreateTrainingJobRequest::GetStoppingCondition ( ) const
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 740 of file CreateTrainingJobRequest.h.

◆ GetTags()

const Aws::Vector<Tag>& Aws::SageMaker::Model::CreateTrainingJobRequest::GetTags ( ) const
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 805 of file CreateTrainingJobRequest.h.

◆ GetTensorBoardOutputConfig()

const TensorBoardOutputConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetTensorBoardOutputConfig ( ) const
inline

Definition at line 1120 of file CreateTrainingJobRequest.h.

◆ GetTrainingJobName()

const Aws::String& Aws::SageMaker::Model::CreateTrainingJobRequest::GetTrainingJobName ( ) const
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 58 of file CreateTrainingJobRequest.h.

◆ GetVpcConfig()

const VpcConfig& Aws::SageMaker::Model::CreateTrainingJobRequest::GetVpcConfig ( ) const
inline

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.

Definition at line 683 of file CreateTrainingJobRequest.h.

◆ HyperParametersHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::HyperParametersHasBeenSet ( ) const
inline

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 Length Constraint.

Definition at line 123 of file CreateTrainingJobRequest.h.

◆ InputDataConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::InputDataConfigHasBeenSet ( ) const
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 467 of file CreateTrainingJobRequest.h.

◆ OutputDataConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::OutputDataConfigHasBeenSet ( ) const
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 582 of file CreateTrainingJobRequest.h.

◆ ProfilerConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::ProfilerConfigHasBeenSet ( ) const
inline

Definition at line 1161 of file CreateTrainingJobRequest.h.

◆ ProfilerRuleConfigurationsHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::ProfilerRuleConfigurationsHasBeenSet ( ) const
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1186 of file CreateTrainingJobRequest.h.

◆ ResourceConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::ResourceConfigHasBeenSet ( ) const
inline

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.

Definition at line 629 of file CreateTrainingJobRequest.h.

◆ RetryStrategyHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::RetryStrategyHasBeenSet ( ) const
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1301 of file CreateTrainingJobRequest.h.

◆ RoleArnHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::RoleArnHasBeenSet ( ) const
inline

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.

Definition at line 342 of file CreateTrainingJobRequest.h.

◆ SerializePayload()

Aws::String Aws::SageMaker::Model::CreateTrainingJobRequest::SerializePayload ( ) const
overridevirtual

Convert payload into String.

Implements Aws::AmazonSerializableWebServiceRequest.

◆ SetAlgorithmSpecification() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetAlgorithmSpecification ( AlgorithmSpecification &&  value)
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 289 of file CreateTrainingJobRequest.h.

◆ SetAlgorithmSpecification() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetAlgorithmSpecification ( const AlgorithmSpecification value)
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 278 of file CreateTrainingJobRequest.h.

◆ SetCheckpointConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetCheckpointConfig ( CheckpointConfig &&  value)
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1036 of file CreateTrainingJobRequest.h.

◆ SetCheckpointConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetCheckpointConfig ( const CheckpointConfig value)
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1030 of file CreateTrainingJobRequest.h.

◆ SetDebugHookConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetDebugHookConfig ( const DebugHookConfig value)
inline

Definition at line 1058 of file CreateTrainingJobRequest.h.

◆ SetDebugHookConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetDebugHookConfig ( DebugHookConfig &&  value)
inline

Definition at line 1061 of file CreateTrainingJobRequest.h.

◆ SetDebugRuleConfigurations() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetDebugRuleConfigurations ( Aws::Vector< DebugRuleConfiguration > &&  value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1092 of file CreateTrainingJobRequest.h.

◆ SetDebugRuleConfigurations() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetDebugRuleConfigurations ( const Aws::Vector< DebugRuleConfiguration > &  value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1086 of file CreateTrainingJobRequest.h.

◆ SetEnableInterContainerTrafficEncryption()

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetEnableInterContainerTrafficEncryption ( bool  value)
inline

To encrypt all communications between ML compute instances in distributed training, choose True. 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 Protect Communications Between ML Compute Instances in a Distributed Training Job.

Definition at line 946 of file CreateTrainingJobRequest.h.

◆ SetEnableManagedSpotTraining()

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetEnableManagedSpotTraining ( bool  value)
inline

To train models using managed spot training, choose True. 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.

Definition at line 998 of file CreateTrainingJobRequest.h.

◆ SetEnableNetworkIsolation()

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetEnableNetworkIsolation ( bool  value)
inline

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Definition at line 899 of file CreateTrainingJobRequest.h.

◆ SetEnvironment() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetEnvironment ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

The environment variables to set in the Docker container.

Definition at line 1243 of file CreateTrainingJobRequest.h.

◆ SetEnvironment() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetEnvironment ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

The environment variables to set in the Docker container.

Definition at line 1238 of file CreateTrainingJobRequest.h.

◆ SetExperimentConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetExperimentConfig ( const ExperimentConfig value)
inline

Definition at line 1145 of file CreateTrainingJobRequest.h.

◆ SetExperimentConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetExperimentConfig ( ExperimentConfig &&  value)
inline

Definition at line 1148 of file CreateTrainingJobRequest.h.

◆ SetHyperParameters() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetHyperParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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 Length Constraint.

Definition at line 145 of file CreateTrainingJobRequest.h.

◆ SetHyperParameters() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetHyperParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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 Length Constraint.

Definition at line 134 of file CreateTrainingJobRequest.h.

◆ SetInputDataConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetInputDataConfig ( Aws::Vector< Channel > &&  value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 501 of file CreateTrainingJobRequest.h.

◆ SetInputDataConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetInputDataConfig ( const Aws::Vector< Channel > &  value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 484 of file CreateTrainingJobRequest.h.

◆ SetOutputDataConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetOutputDataConfig ( const OutputDataConfig value)
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 588 of file CreateTrainingJobRequest.h.

◆ SetOutputDataConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetOutputDataConfig ( OutputDataConfig &&  value)
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 594 of file CreateTrainingJobRequest.h.

◆ SetProfilerConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetProfilerConfig ( const ProfilerConfig value)
inline

Definition at line 1164 of file CreateTrainingJobRequest.h.

◆ SetProfilerConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetProfilerConfig ( ProfilerConfig &&  value)
inline

Definition at line 1167 of file CreateTrainingJobRequest.h.

◆ SetProfilerRuleConfigurations() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetProfilerRuleConfigurations ( Aws::Vector< ProfilerRuleConfiguration > &&  value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1198 of file CreateTrainingJobRequest.h.

◆ SetProfilerRuleConfigurations() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetProfilerRuleConfigurations ( const Aws::Vector< ProfilerRuleConfiguration > &  value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1192 of file CreateTrainingJobRequest.h.

◆ SetResourceConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetResourceConfig ( const ResourceConfig value)
inline

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.

Definition at line 640 of file CreateTrainingJobRequest.h.

◆ SetResourceConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetResourceConfig ( ResourceConfig &&  value)
inline

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.

Definition at line 651 of file CreateTrainingJobRequest.h.

◆ SetRetryStrategy() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetRetryStrategy ( const RetryStrategy value)
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1307 of file CreateTrainingJobRequest.h.

◆ SetRetryStrategy() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetRetryStrategy ( RetryStrategy &&  value)
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1313 of file CreateTrainingJobRequest.h.

◆ SetRoleArn() [1/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetRoleArn ( Aws::String &&  value)
inline

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.

Definition at line 372 of file CreateTrainingJobRequest.h.

◆ SetRoleArn() [2/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetRoleArn ( const Aws::String value)
inline

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.

Definition at line 357 of file CreateTrainingJobRequest.h.

◆ SetRoleArn() [3/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetRoleArn ( const char *  value)
inline

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.

Definition at line 387 of file CreateTrainingJobRequest.h.

◆ SetStoppingCondition() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetStoppingCondition ( const StoppingCondition value)
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 762 of file CreateTrainingJobRequest.h.

◆ SetStoppingCondition() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetStoppingCondition ( StoppingCondition &&  value)
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 773 of file CreateTrainingJobRequest.h.

◆ SetTags() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTags ( Aws::Vector< Tag > &&  value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 832 of file CreateTrainingJobRequest.h.

◆ SetTags() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTags ( const Aws::Vector< Tag > &  value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 823 of file CreateTrainingJobRequest.h.

◆ SetTensorBoardOutputConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTensorBoardOutputConfig ( const TensorBoardOutputConfig value)
inline

Definition at line 1126 of file CreateTrainingJobRequest.h.

◆ SetTensorBoardOutputConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTensorBoardOutputConfig ( TensorBoardOutputConfig &&  value)
inline

Definition at line 1129 of file CreateTrainingJobRequest.h.

◆ SetTrainingJobName() [1/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTrainingJobName ( Aws::String &&  value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 76 of file CreateTrainingJobRequest.h.

◆ SetTrainingJobName() [2/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTrainingJobName ( const Aws::String value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 70 of file CreateTrainingJobRequest.h.

◆ SetTrainingJobName() [3/3]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetTrainingJobName ( const char *  value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 82 of file CreateTrainingJobRequest.h.

◆ SetVpcConfig() [1/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetVpcConfig ( const VpcConfig value)
inline

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.

Definition at line 701 of file CreateTrainingJobRequest.h.

◆ SetVpcConfig() [2/2]

void Aws::SageMaker::Model::CreateTrainingJobRequest::SetVpcConfig ( VpcConfig &&  value)
inline

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.

Definition at line 710 of file CreateTrainingJobRequest.h.

◆ StoppingConditionHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::StoppingConditionHasBeenSet ( ) const
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 751 of file CreateTrainingJobRequest.h.

◆ TagsHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::TagsHasBeenSet ( ) const
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 814 of file CreateTrainingJobRequest.h.

◆ TensorBoardOutputConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::TensorBoardOutputConfigHasBeenSet ( ) const
inline

Definition at line 1123 of file CreateTrainingJobRequest.h.

◆ TrainingJobNameHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::TrainingJobNameHasBeenSet ( ) const
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 64 of file CreateTrainingJobRequest.h.

◆ VpcConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateTrainingJobRequest::VpcConfigHasBeenSet ( ) const
inline

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.

Definition at line 692 of file CreateTrainingJobRequest.h.

◆ WithAlgorithmSpecification() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithAlgorithmSpecification ( AlgorithmSpecification &&  value)
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 311 of file CreateTrainingJobRequest.h.

◆ WithAlgorithmSpecification() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithAlgorithmSpecification ( const AlgorithmSpecification value)
inline

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 Using Your Own Algorithms with Amazon SageMaker.

Definition at line 300 of file CreateTrainingJobRequest.h.

◆ WithCheckpointConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithCheckpointConfig ( CheckpointConfig &&  value)
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1048 of file CreateTrainingJobRequest.h.

◆ WithCheckpointConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithCheckpointConfig ( const CheckpointConfig value)
inline

Contains information about the output location for managed spot training checkpoint data.

Definition at line 1042 of file CreateTrainingJobRequest.h.

◆ WithDebugHookConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithDebugHookConfig ( const DebugHookConfig value)
inline

Definition at line 1064 of file CreateTrainingJobRequest.h.

◆ WithDebugHookConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithDebugHookConfig ( DebugHookConfig &&  value)
inline

Definition at line 1067 of file CreateTrainingJobRequest.h.

◆ WithDebugRuleConfigurations() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithDebugRuleConfigurations ( Aws::Vector< DebugRuleConfiguration > &&  value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1104 of file CreateTrainingJobRequest.h.

◆ WithDebugRuleConfigurations() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithDebugRuleConfigurations ( const Aws::Vector< DebugRuleConfiguration > &  value)
inline

Configuration information for Debugger rules for debugging output tensors.

Definition at line 1098 of file CreateTrainingJobRequest.h.

◆ WithEnableInterContainerTrafficEncryption()

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithEnableInterContainerTrafficEncryption ( bool  value)
inline

To encrypt all communications between ML compute instances in distributed training, choose True. 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 Protect Communications Between ML Compute Instances in a Distributed Training Job.

Definition at line 958 of file CreateTrainingJobRequest.h.

◆ WithEnableManagedSpotTraining()

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithEnableManagedSpotTraining ( bool  value)
inline

To train models using managed spot training, choose True. 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.

Definition at line 1011 of file CreateTrainingJobRequest.h.

◆ WithEnableNetworkIsolation()

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithEnableNetworkIsolation ( bool  value)
inline

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

Definition at line 909 of file CreateTrainingJobRequest.h.

◆ WithEnvironment() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithEnvironment ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

The environment variables to set in the Docker container.

Definition at line 1253 of file CreateTrainingJobRequest.h.

◆ WithEnvironment() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithEnvironment ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

The environment variables to set in the Docker container.

Definition at line 1248 of file CreateTrainingJobRequest.h.

◆ WithExperimentConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithExperimentConfig ( const ExperimentConfig value)
inline

Definition at line 1151 of file CreateTrainingJobRequest.h.

◆ WithExperimentConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithExperimentConfig ( ExperimentConfig &&  value)
inline

Definition at line 1154 of file CreateTrainingJobRequest.h.

◆ WithHyperParameters() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithHyperParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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 Length Constraint.

Definition at line 167 of file CreateTrainingJobRequest.h.

◆ WithHyperParameters() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithHyperParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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 Length Constraint.

Definition at line 156 of file CreateTrainingJobRequest.h.

◆ WithInputDataConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithInputDataConfig ( Aws::Vector< Channel > &&  value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 535 of file CreateTrainingJobRequest.h.

◆ WithInputDataConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithInputDataConfig ( const Aws::Vector< Channel > &  value)
inline

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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

Definition at line 518 of file CreateTrainingJobRequest.h.

◆ WithOutputDataConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithOutputDataConfig ( const OutputDataConfig value)
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 600 of file CreateTrainingJobRequest.h.

◆ WithOutputDataConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithOutputDataConfig ( OutputDataConfig &&  value)
inline

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

Definition at line 606 of file CreateTrainingJobRequest.h.

◆ WithProfilerConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithProfilerConfig ( const ProfilerConfig value)
inline

Definition at line 1170 of file CreateTrainingJobRequest.h.

◆ WithProfilerConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithProfilerConfig ( ProfilerConfig &&  value)
inline

Definition at line 1173 of file CreateTrainingJobRequest.h.

◆ WithProfilerRuleConfigurations() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithProfilerRuleConfigurations ( Aws::Vector< ProfilerRuleConfiguration > &&  value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1210 of file CreateTrainingJobRequest.h.

◆ WithProfilerRuleConfigurations() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithProfilerRuleConfigurations ( const Aws::Vector< ProfilerRuleConfiguration > &  value)
inline

Configuration information for Debugger rules for profiling system and framework metrics.

Definition at line 1204 of file CreateTrainingJobRequest.h.

◆ WithResourceConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithResourceConfig ( const ResourceConfig value)
inline

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.

Definition at line 662 of file CreateTrainingJobRequest.h.

◆ WithResourceConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithResourceConfig ( ResourceConfig &&  value)
inline

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.

Definition at line 673 of file CreateTrainingJobRequest.h.

◆ WithRetryStrategy() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithRetryStrategy ( const RetryStrategy value)
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1319 of file CreateTrainingJobRequest.h.

◆ WithRetryStrategy() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithRetryStrategy ( RetryStrategy &&  value)
inline

The number of times to retry the job when the job fails due to an InternalServerError.

Definition at line 1325 of file CreateTrainingJobRequest.h.

◆ WithRoleArn() [1/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithRoleArn ( Aws::String &&  value)
inline

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.

Definition at line 417 of file CreateTrainingJobRequest.h.

◆ WithRoleArn() [2/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithRoleArn ( const Aws::String value)
inline

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.

Definition at line 402 of file CreateTrainingJobRequest.h.

◆ WithRoleArn() [3/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithRoleArn ( const char *  value)
inline

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.

Definition at line 432 of file CreateTrainingJobRequest.h.

◆ WithStoppingCondition() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithStoppingCondition ( const StoppingCondition value)
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 784 of file CreateTrainingJobRequest.h.

◆ WithStoppingCondition() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithStoppingCondition ( StoppingCondition &&  value)
inline

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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

Definition at line 795 of file CreateTrainingJobRequest.h.

◆ WithTags() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTags ( Aws::Vector< Tag > &&  value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 850 of file CreateTrainingJobRequest.h.

◆ WithTags() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTags ( const Aws::Vector< Tag > &  value)
inline

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Definition at line 841 of file CreateTrainingJobRequest.h.

◆ WithTensorBoardOutputConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTensorBoardOutputConfig ( const TensorBoardOutputConfig value)
inline

Definition at line 1132 of file CreateTrainingJobRequest.h.

◆ WithTensorBoardOutputConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTensorBoardOutputConfig ( TensorBoardOutputConfig &&  value)
inline

Definition at line 1135 of file CreateTrainingJobRequest.h.

◆ WithTrainingJobName() [1/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTrainingJobName ( Aws::String &&  value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 94 of file CreateTrainingJobRequest.h.

◆ WithTrainingJobName() [2/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTrainingJobName ( const Aws::String value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 88 of file CreateTrainingJobRequest.h.

◆ WithTrainingJobName() [3/3]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithTrainingJobName ( const char *  value)
inline

The name of the training job. The name must be unique within an AWS Region in an AWS account.

Definition at line 100 of file CreateTrainingJobRequest.h.

◆ WithVpcConfig() [1/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithVpcConfig ( const VpcConfig value)
inline

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.

Definition at line 719 of file CreateTrainingJobRequest.h.

◆ WithVpcConfig() [2/2]

CreateTrainingJobRequest& Aws::SageMaker::Model::CreateTrainingJobRequest::WithVpcConfig ( VpcConfig &&  value)
inline

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.

Definition at line 728 of file CreateTrainingJobRequest.h.


The documentation for this class was generated from the following file: