Interface CreateTrainingJobRequest.Builder
- All Superinterfaces:
AwsRequest.Builder
,Buildable
,CopyableBuilder<CreateTrainingJobRequest.Builder,
,CreateTrainingJobRequest> SageMakerRequest.Builder
,SdkBuilder<CreateTrainingJobRequest.Builder,
,CreateTrainingJobRequest> SdkPojo
,SdkRequest.Builder
- Enclosing class:
CreateTrainingJobRequest
-
Method Summary
Modifier and TypeMethodDescriptiondefault CreateTrainingJobRequest.Builder
algorithmSpecification
(Consumer<AlgorithmSpecification.Builder> algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.algorithmSpecification
(AlgorithmSpecification algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode.default CreateTrainingJobRequest.Builder
checkpointConfig
(Consumer<CheckpointConfig.Builder> checkpointConfig) Contains information about the output location for managed spot training checkpoint data.checkpointConfig
(CheckpointConfig checkpointConfig) Contains information about the output location for managed spot training checkpoint data.default CreateTrainingJobRequest.Builder
debugHookConfig
(Consumer<DebugHookConfig.Builder> debugHookConfig) Sets the value of the DebugHookConfig property for this object.debugHookConfig
(DebugHookConfig debugHookConfig) Sets the value of the DebugHookConfig property for this object.debugRuleConfigurations
(Collection<DebugRuleConfiguration> debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.debugRuleConfigurations
(Consumer<DebugRuleConfiguration.Builder>... debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.debugRuleConfigurations
(DebugRuleConfiguration... debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.enableInterContainerTrafficEncryption
(Boolean enableInterContainerTrafficEncryption) To encrypt all communications between ML compute instances in distributed training, chooseTrue
.enableManagedSpotTraining
(Boolean enableManagedSpotTraining) To train models using managed spot training, chooseTrue
.enableNetworkIsolation
(Boolean enableNetworkIsolation) Isolates the training container.environment
(Map<String, String> environment) The environment variables to set in the Docker container.default CreateTrainingJobRequest.Builder
experimentConfig
(Consumer<ExperimentConfig.Builder> experimentConfig) Sets the value of the ExperimentConfig property for this object.experimentConfig
(ExperimentConfig experimentConfig) Sets the value of the ExperimentConfig property for this object.hyperParameters
(Map<String, String> hyperParameters) Algorithm-specific parameters that influence the quality of the model.default CreateTrainingJobRequest.Builder
infraCheckConfig
(Consumer<InfraCheckConfig.Builder> infraCheckConfig) Contains information about the infrastructure health check configuration for the training job.infraCheckConfig
(InfraCheckConfig infraCheckConfig) Contains information about the infrastructure health check configuration for the training job.inputDataConfig
(Collection<Channel> inputDataConfig) An array ofChannel
objects.inputDataConfig
(Consumer<Channel.Builder>... inputDataConfig) An array ofChannel
objects.inputDataConfig
(Channel... inputDataConfig) An array ofChannel
objects.default CreateTrainingJobRequest.Builder
outputDataConfig
(Consumer<OutputDataConfig.Builder> outputDataConfig) Specifies the path to the S3 location where you want to store model artifacts.outputDataConfig
(OutputDataConfig outputDataConfig) Specifies the path to the S3 location where you want to store model artifacts.overrideConfiguration
(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Add an optional request override configuration.overrideConfiguration
(AwsRequestOverrideConfiguration overrideConfiguration) Add an optional request override configuration.default CreateTrainingJobRequest.Builder
profilerConfig
(Consumer<ProfilerConfig.Builder> profilerConfig) Sets the value of the ProfilerConfig property for this object.profilerConfig
(ProfilerConfig profilerConfig) Sets the value of the ProfilerConfig property for this object.profilerRuleConfigurations
(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.profilerRuleConfigurations
(Consumer<ProfilerRuleConfiguration.Builder>... profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.profilerRuleConfigurations
(ProfilerRuleConfiguration... profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.default CreateTrainingJobRequest.Builder
remoteDebugConfig
(Consumer<RemoteDebugConfig.Builder> remoteDebugConfig) Configuration for remote debugging.remoteDebugConfig
(RemoteDebugConfig remoteDebugConfig) Configuration for remote debugging.default CreateTrainingJobRequest.Builder
resourceConfig
(Consumer<ResourceConfig.Builder> resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.resourceConfig
(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.default CreateTrainingJobRequest.Builder
retryStrategy
(Consumer<RetryStrategy.Builder> retryStrategy) The number of times to retry the job when the job fails due to anInternalServerError
.retryStrategy
(RetryStrategy retryStrategy) The number of times to retry the job when the job fails due to anInternalServerError
.The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.default CreateTrainingJobRequest.Builder
stoppingCondition
(Consumer<StoppingCondition.Builder> stoppingCondition) Specifies a limit to how long a model training job can run.stoppingCondition
(StoppingCondition stoppingCondition) Specifies a limit to how long a model training job can run.tags
(Collection<Tag> tags) An array of key-value pairs.tags
(Consumer<Tag.Builder>... tags) An array of key-value pairs.An array of key-value pairs.default CreateTrainingJobRequest.Builder
tensorBoardOutputConfig
(Consumer<TensorBoardOutputConfig.Builder> tensorBoardOutputConfig) Sets the value of the TensorBoardOutputConfig property for this object.tensorBoardOutputConfig
(TensorBoardOutputConfig tensorBoardOutputConfig) Sets the value of the TensorBoardOutputConfig property for this object.trainingJobName
(String trainingJobName) The name of the training job.default CreateTrainingJobRequest.Builder
vpcConfig
(Consumer<VpcConfig.Builder> vpcConfig) A VpcConfig object that specifies the VPC that you want your training job to connect to.A VpcConfig object that specifies the VPC that you want your training job to connect to.Methods inherited from interface software.amazon.awssdk.awscore.AwsRequest.Builder
overrideConfiguration
Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.services.sagemaker.model.SageMakerRequest.Builder
build
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
-
Method Details
-
trainingJobName
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
- Parameters:
trainingJobName
- The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
hyperParameters
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the
Length Constraint
.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
- Parameters:
hyperParameters
- Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the
Length Constraint
.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
algorithmSpecification
CreateTrainingJobRequest.Builder algorithmSpecification(AlgorithmSpecification algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
- Parameters:
algorithmSpecification
- The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
algorithmSpecification
default CreateTrainingJobRequest.Builder algorithmSpecification(Consumer<AlgorithmSpecification.Builder> algorithmSpecification) The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
This is a convenience method that creates an instance of theAlgorithmSpecification.Builder
avoiding the need to create one manually viaAlgorithmSpecification.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toalgorithmSpecification(AlgorithmSpecification)
.- Parameters:
algorithmSpecification
- a consumer that will call methods onAlgorithmSpecification.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
roleArn
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the
iam:PassRole
permission.- Parameters:
roleArn
- The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the
iam:PassRole
permission.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of
Channel
objects. Each channel is a named input source.InputDataConfig
describes the input data and its location.Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data,
training_data
andvalidation_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, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
- Parameters:
inputDataConfig
- An array ofChannel
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
andvalidation_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, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of
Channel
objects. Each channel is a named input source.InputDataConfig
describes the input data and its location.Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data,
training_data
andvalidation_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, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
- Parameters:
inputDataConfig
- An array ofChannel
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
andvalidation_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, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of
Channel
objects. Each channel is a named input source.InputDataConfig
describes the input data and its location.Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data,
training_data
andvalidation_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, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
This is a convenience method that creates an instance of theChannel.Builder
avoiding the need to create one manually viaChannel.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toinputDataConfig(List<Channel>)
.- Parameters:
inputDataConfig
- a consumer that will call methods onChannel.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
outputDataConfig
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
- Parameters:
outputDataConfig
- Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
outputDataConfig
default CreateTrainingJobRequest.Builder outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig) Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
This is a convenience method that creates an instance of theOutputDataConfig.Builder
avoiding the need to create one manually viaOutputDataConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tooutputDataConfig(OutputDataConfig)
.- Parameters:
outputDataConfig
- a consumer that will call methods onOutputDataConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
resourceConfig
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.- Parameters:
resourceConfig
- The resources, including the ML compute instances and ML storage volumes, to use for model training.ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
File
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
resourceConfig
default CreateTrainingJobRequest.Builder resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose
This is a convenience method that creates an instance of theFile
as theTrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.ResourceConfig.Builder
avoiding the need to create one manually viaResourceConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toresourceConfig(ResourceConfig)
.- Parameters:
resourceConfig
- a consumer that will call methods onResourceConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
vpcConfig
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
- Parameters:
vpcConfig
- A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
vpcConfig
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
This is a convenience method that creates an instance of theVpcConfig.Builder
avoiding the need to create one manually viaVpcConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tovpcConfig(VpcConfig)
.- Parameters:
vpcConfig
- a consumer that will call methods onVpcConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
stoppingCondition
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
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.- Parameters:
stoppingCondition
- Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.To stop a job, SageMaker sends the algorithm the
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
stoppingCondition
default CreateTrainingJobRequest.Builder stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition) Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the
This is a convenience method that creates an instance of theSIGTERM
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.StoppingCondition.Builder
avoiding the need to create one manually viaStoppingCondition.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tostoppingCondition(StoppingCondition)
.- Parameters:
stoppingCondition
- a consumer that will call methods onStoppingCondition.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
- Parameters:
tags
- An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
- Parameters:
tags
- An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
tags
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
This is a convenience method that creates an instance of theTag.Builder
avoiding the need to create one manually viaTag.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed totags(List<Tag>)
.- Parameters:
tags
- a consumer that will call methods onTag.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
enableNetworkIsolation
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
- Parameters:
enableNetworkIsolation
- Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
enableInterContainerTrafficEncryption
CreateTrainingJobRequest.Builder enableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption) 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.- Parameters:
enableInterContainerTrafficEncryption
- To encrypt all communications between ML compute instances in distributed training, chooseTrue
. 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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
enableManagedSpotTraining
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.
- Parameters:
enableManagedSpotTraining
- To train models using managed spot training, chooseTrue
. 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.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
checkpointConfig
Contains information about the output location for managed spot training checkpoint data.
- Parameters:
checkpointConfig
- Contains information about the output location for managed spot training checkpoint data.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
checkpointConfig
default CreateTrainingJobRequest.Builder checkpointConfig(Consumer<CheckpointConfig.Builder> checkpointConfig) Contains information about the output location for managed spot training checkpoint data.
This is a convenience method that creates an instance of theCheckpointConfig.Builder
avoiding the need to create one manually viaCheckpointConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tocheckpointConfig(CheckpointConfig)
.- Parameters:
checkpointConfig
- a consumer that will call methods onCheckpointConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
debugHookConfig
Sets the value of the DebugHookConfig property for this object.- Parameters:
debugHookConfig
- The new value for the DebugHookConfig property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
debugHookConfig
default CreateTrainingJobRequest.Builder debugHookConfig(Consumer<DebugHookConfig.Builder> debugHookConfig) Sets the value of the DebugHookConfig property for this object. This is a convenience method that creates an instance of theDebugHookConfig.Builder
avoiding the need to create one manually viaDebugHookConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed todebugHookConfig(DebugHookConfig)
.- Parameters:
debugHookConfig
- a consumer that will call methods onDebugHookConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
debugRuleConfigurations
CreateTrainingJobRequest.Builder debugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
- Parameters:
debugRuleConfigurations
- Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
debugRuleConfigurations
CreateTrainingJobRequest.Builder debugRuleConfigurations(DebugRuleConfiguration... debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
- Parameters:
debugRuleConfigurations
- Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
debugRuleConfigurations
CreateTrainingJobRequest.Builder debugRuleConfigurations(Consumer<DebugRuleConfiguration.Builder>... debugRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
This is a convenience method that creates an instance of theDebugRuleConfiguration.Builder
avoiding the need to create one manually viaDebugRuleConfiguration.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed todebugRuleConfigurations(List<DebugRuleConfiguration>)
.- Parameters:
debugRuleConfigurations
- a consumer that will call methods onDebugRuleConfiguration.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
tensorBoardOutputConfig
CreateTrainingJobRequest.Builder tensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig) Sets the value of the TensorBoardOutputConfig property for this object.- Parameters:
tensorBoardOutputConfig
- The new value for the TensorBoardOutputConfig property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
tensorBoardOutputConfig
default CreateTrainingJobRequest.Builder tensorBoardOutputConfig(Consumer<TensorBoardOutputConfig.Builder> tensorBoardOutputConfig) Sets the value of the TensorBoardOutputConfig property for this object. This is a convenience method that creates an instance of theTensorBoardOutputConfig.Builder
avoiding the need to create one manually viaTensorBoardOutputConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed totensorBoardOutputConfig(TensorBoardOutputConfig)
.- Parameters:
tensorBoardOutputConfig
- a consumer that will call methods onTensorBoardOutputConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
experimentConfig
Sets the value of the ExperimentConfig property for this object.- Parameters:
experimentConfig
- The new value for the ExperimentConfig property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
experimentConfig
default CreateTrainingJobRequest.Builder experimentConfig(Consumer<ExperimentConfig.Builder> experimentConfig) Sets the value of the ExperimentConfig property for this object. This is a convenience method that creates an instance of theExperimentConfig.Builder
avoiding the need to create one manually viaExperimentConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toexperimentConfig(ExperimentConfig)
.- Parameters:
experimentConfig
- a consumer that will call methods onExperimentConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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profilerConfig
Sets the value of the ProfilerConfig property for this object.- Parameters:
profilerConfig
- The new value for the ProfilerConfig property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
profilerConfig
default CreateTrainingJobRequest.Builder profilerConfig(Consumer<ProfilerConfig.Builder> profilerConfig) Sets the value of the ProfilerConfig property for this object. This is a convenience method that creates an instance of theProfilerConfig.Builder
avoiding the need to create one manually viaProfilerConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toprofilerConfig(ProfilerConfig)
.- Parameters:
profilerConfig
- a consumer that will call methods onProfilerConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
profilerRuleConfigurations
CreateTrainingJobRequest.Builder profilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
- Parameters:
profilerRuleConfigurations
- Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
profilerRuleConfigurations
CreateTrainingJobRequest.Builder profilerRuleConfigurations(ProfilerRuleConfiguration... profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
- Parameters:
profilerRuleConfigurations
- Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
profilerRuleConfigurations
CreateTrainingJobRequest.Builder profilerRuleConfigurations(Consumer<ProfilerRuleConfiguration.Builder>... profilerRuleConfigurations) Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
This is a convenience method that creates an instance of theProfilerRuleConfiguration.Builder
avoiding the need to create one manually viaProfilerRuleConfiguration.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toprofilerRuleConfigurations(List<ProfilerRuleConfiguration>)
.- Parameters:
profilerRuleConfigurations
- a consumer that will call methods onProfilerRuleConfiguration.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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environment
The environment variables to set in the Docker container.
- Parameters:
environment
- The environment variables to set in the Docker container.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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retryStrategy
The number of times to retry the job when the job fails due to an
InternalServerError
.- Parameters:
retryStrategy
- The number of times to retry the job when the job fails due to anInternalServerError
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
retryStrategy
default CreateTrainingJobRequest.Builder retryStrategy(Consumer<RetryStrategy.Builder> retryStrategy) The number of times to retry the job when the job fails due to an
This is a convenience method that creates an instance of theInternalServerError
.RetryStrategy.Builder
avoiding the need to create one manually viaRetryStrategy.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toretryStrategy(RetryStrategy)
.- Parameters:
retryStrategy
- a consumer that will call methods onRetryStrategy.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
remoteDebugConfig
Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
- Parameters:
remoteDebugConfig
- Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
remoteDebugConfig
default CreateTrainingJobRequest.Builder remoteDebugConfig(Consumer<RemoteDebugConfig.Builder> remoteDebugConfig) Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
This is a convenience method that creates an instance of theRemoteDebugConfig.Builder
avoiding the need to create one manually viaRemoteDebugConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toremoteDebugConfig(RemoteDebugConfig)
.- Parameters:
remoteDebugConfig
- a consumer that will call methods onRemoteDebugConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
infraCheckConfig
Contains information about the infrastructure health check configuration for the training job.
- Parameters:
infraCheckConfig
- Contains information about the infrastructure health check configuration for the training job.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
infraCheckConfig
default CreateTrainingJobRequest.Builder infraCheckConfig(Consumer<InfraCheckConfig.Builder> infraCheckConfig) Contains information about the infrastructure health check configuration for the training job.
This is a convenience method that creates an instance of theInfraCheckConfig.Builder
avoiding the need to create one manually viaInfraCheckConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toinfraCheckConfig(InfraCheckConfig)
.- Parameters:
infraCheckConfig
- a consumer that will call methods onInfraCheckConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
overrideConfiguration
CreateTrainingJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) Description copied from interface:AwsRequest.Builder
Add an optional request override configuration.- Specified by:
overrideConfiguration
in interfaceAwsRequest.Builder
- Parameters:
overrideConfiguration
- The override configuration.- Returns:
- This object for method chaining.
-
overrideConfiguration
CreateTrainingJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Description copied from interface:AwsRequest.Builder
Add an optional request override configuration.- Specified by:
overrideConfiguration
in interfaceAwsRequest.Builder
- Parameters:
builderConsumer
- AConsumer
to which an emptyAwsRequestOverrideConfiguration.Builder
will be given.- Returns:
- This object for method chaining.
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