AWS SDK for C++  1.8.74
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)
 
- 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 35 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 debugging rules.

Definition at line 1086 of file CreateTrainingJobRequest.h.

◆ AddDebugRuleConfigurations() [2/2]

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

Configuration information for debugging rules.

Definition at line 1091 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [1/7]

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 175 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 186 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [3/7]

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 197 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [4/7]

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 208 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [5/7]

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 219 of file CreateTrainingJobRequest.h.

◆ AddHyperParameters() [6/7]

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 230 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 241 of file CreateTrainingJobRequest.h.

◆ AddInputDataConfig() [1/2]

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 549 of file CreateTrainingJobRequest.h.

◆ AddInputDataConfig() [2/2]

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 566 of file CreateTrainingJobRequest.h.

◆ AddTags() [1/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 843 of file CreateTrainingJobRequest.h.

◆ AddTags() [2/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 851 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 264 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 1007 of file CreateTrainingJobRequest.h.

◆ DebugHookConfigHasBeenSet()

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

Definition at line 1038 of file CreateTrainingJobRequest.h.

◆ DebugRuleConfigurationsHasBeenSet()

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

Configuration information for debugging rules.

Definition at line 1061 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 917 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 968 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 872 of file CreateTrainingJobRequest.h.

◆ ExperimentConfigHasBeenSet()

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

Definition at line 1117 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 253 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 1001 of file CreateTrainingJobRequest.h.

◆ GetDebugHookConfig()

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

Definition at line 1035 of file CreateTrainingJobRequest.h.

◆ GetDebugRuleConfigurations()

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

Configuration information for debugging rules.

Definition at line 1056 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 905 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 955 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 862 of file CreateTrainingJobRequest.h.

◆ GetExperimentConfig()

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

Definition at line 1114 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 109 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 447 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 573 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 615 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 324 of file CreateTrainingJobRequest.h.

◆ GetServiceRequestName()

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

Implements Aws::AmazonWebServiceRequest.

Definition at line 44 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. 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 736 of file CreateTrainingJobRequest.h.

◆ GetTags()

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 795 of file CreateTrainingJobRequest.h.

◆ GetTensorBoardOutputConfig()

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

Definition at line 1095 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 55 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 680 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 120 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 464 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 579 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 626 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 339 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 ( 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 275 of file CreateTrainingJobRequest.h.

◆ SetAlgorithmSpecification() [2/2]

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 286 of file CreateTrainingJobRequest.h.

◆ SetCheckpointConfig() [1/2]

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 1013 of file CreateTrainingJobRequest.h.

◆ SetCheckpointConfig() [2/2]

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

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

Definition at line 1019 of file CreateTrainingJobRequest.h.

◆ SetDebugHookConfig() [1/2]

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

Definition at line 1041 of file CreateTrainingJobRequest.h.

◆ SetDebugHookConfig() [2/2]

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

Definition at line 1044 of file CreateTrainingJobRequest.h.

◆ SetDebugRuleConfigurations() [1/2]

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

Configuration information for debugging rules.

Definition at line 1066 of file CreateTrainingJobRequest.h.

◆ SetDebugRuleConfigurations() [2/2]

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

Configuration information for debugging rules.

Definition at line 1071 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 929 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 981 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 882 of file CreateTrainingJobRequest.h.

◆ SetExperimentConfig() [1/2]

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

Definition at line 1120 of file CreateTrainingJobRequest.h.

◆ SetExperimentConfig() [2/2]

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

Definition at line 1123 of file CreateTrainingJobRequest.h.

◆ SetHyperParameters() [1/2]

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 131 of file CreateTrainingJobRequest.h.

◆ SetHyperParameters() [2/2]

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 142 of file CreateTrainingJobRequest.h.

◆ SetInputDataConfig() [1/2]

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 481 of file CreateTrainingJobRequest.h.

◆ SetInputDataConfig() [2/2]

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 498 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 585 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 591 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 637 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 648 of file CreateTrainingJobRequest.h.

◆ SetRoleArn() [1/3]

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 354 of file CreateTrainingJobRequest.h.

◆ SetRoleArn() [2/3]

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 369 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 384 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. 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 756 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. 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 766 of file CreateTrainingJobRequest.h.

◆ SetTags() [1/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 811 of file CreateTrainingJobRequest.h.

◆ SetTags() [2/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 819 of file CreateTrainingJobRequest.h.

◆ SetTensorBoardOutputConfig() [1/2]

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

Definition at line 1101 of file CreateTrainingJobRequest.h.

◆ SetTensorBoardOutputConfig() [2/2]

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

Definition at line 1104 of file CreateTrainingJobRequest.h.

◆ SetTrainingJobName() [1/3]

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 67 of file CreateTrainingJobRequest.h.

◆ SetTrainingJobName() [2/3]

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 73 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 79 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 698 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 707 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. 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 746 of file CreateTrainingJobRequest.h.

◆ TagsHasBeenSet()

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 803 of file CreateTrainingJobRequest.h.

◆ TensorBoardOutputConfigHasBeenSet()

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

Definition at line 1098 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 61 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 689 of file CreateTrainingJobRequest.h.

◆ WithAlgorithmSpecification() [1/2]

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 297 of file CreateTrainingJobRequest.h.

◆ WithAlgorithmSpecification() [2/2]

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 308 of file CreateTrainingJobRequest.h.

◆ WithCheckpointConfig() [1/2]

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 1025 of file CreateTrainingJobRequest.h.

◆ WithCheckpointConfig() [2/2]

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

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

Definition at line 1031 of file CreateTrainingJobRequest.h.

◆ WithDebugHookConfig() [1/2]

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

Definition at line 1047 of file CreateTrainingJobRequest.h.

◆ WithDebugHookConfig() [2/2]

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

Definition at line 1050 of file CreateTrainingJobRequest.h.

◆ WithDebugRuleConfigurations() [1/2]

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

Configuration information for debugging rules.

Definition at line 1076 of file CreateTrainingJobRequest.h.

◆ WithDebugRuleConfigurations() [2/2]

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

Configuration information for debugging rules.

Definition at line 1081 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 941 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 994 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 892 of file CreateTrainingJobRequest.h.

◆ WithExperimentConfig() [1/2]

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

Definition at line 1126 of file CreateTrainingJobRequest.h.

◆ WithExperimentConfig() [2/2]

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

Definition at line 1129 of file CreateTrainingJobRequest.h.

◆ WithHyperParameters() [1/2]

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 153 of file CreateTrainingJobRequest.h.

◆ WithHyperParameters() [2/2]

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 164 of file CreateTrainingJobRequest.h.

◆ WithInputDataConfig() [1/2]

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 515 of file CreateTrainingJobRequest.h.

◆ WithInputDataConfig() [2/2]

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 532 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 597 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 603 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 659 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 670 of file CreateTrainingJobRequest.h.

◆ WithRoleArn() [1/3]

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 399 of file CreateTrainingJobRequest.h.

◆ WithRoleArn() [2/3]

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 414 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 429 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. 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 776 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. 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 786 of file CreateTrainingJobRequest.h.

◆ WithTags() [1/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 827 of file CreateTrainingJobRequest.h.

◆ WithTags() [2/2]

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

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Definition at line 835 of file CreateTrainingJobRequest.h.

◆ WithTensorBoardOutputConfig() [1/2]

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

Definition at line 1107 of file CreateTrainingJobRequest.h.

◆ WithTensorBoardOutputConfig() [2/2]

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

Definition at line 1110 of file CreateTrainingJobRequest.h.

◆ WithTrainingJobName() [1/3]

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 85 of file CreateTrainingJobRequest.h.

◆ WithTrainingJobName() [2/3]

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 91 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 97 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 716 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 725 of file CreateTrainingJobRequest.h.


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