public static interface CreateTrainingJobRequest.Builder extends SageMakerRequest.Builder, SdkPojo, CopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
| Modifier and Type | Method and Description |
|---|---|
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.
|
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.
|
CreateTrainingJobRequest.Builder |
checkpointConfig(CheckpointConfig checkpointConfig)
Contains information about the output location for managed spot training checkpoint data.
|
default CreateTrainingJobRequest.Builder |
checkpointConfig(Consumer<CheckpointConfig.Builder> 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.
|
CreateTrainingJobRequest.Builder |
debugHookConfig(DebugHookConfig debugHookConfig)
Sets the value of the DebugHookConfig property for this object.
|
CreateTrainingJobRequest.Builder |
debugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
|
CreateTrainingJobRequest.Builder |
debugRuleConfigurations(Consumer<DebugRuleConfiguration.Builder>... debugRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
|
CreateTrainingJobRequest.Builder |
debugRuleConfigurations(DebugRuleConfiguration... debugRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
|
CreateTrainingJobRequest.Builder |
enableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
To encrypt all communications between ML compute instances in distributed training, choose
True. |
CreateTrainingJobRequest.Builder |
enableManagedSpotTraining(Boolean enableManagedSpotTraining)
To train models using managed spot training, choose
True. |
CreateTrainingJobRequest.Builder |
enableNetworkIsolation(Boolean enableNetworkIsolation)
Isolates the training container.
|
CreateTrainingJobRequest.Builder |
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.
|
CreateTrainingJobRequest.Builder |
experimentConfig(ExperimentConfig experimentConfig)
Sets the value of the ExperimentConfig property for this object.
|
CreateTrainingJobRequest.Builder |
hyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters that influence the quality of the model.
|
CreateTrainingJobRequest.Builder |
inputDataConfig(Channel... inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest.Builder |
inputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest.Builder |
inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of
Channel objects. |
default CreateTrainingJobRequest.Builder |
outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
Specifies the path to the S3 location where you want to store model artifacts.
|
CreateTrainingJobRequest.Builder |
outputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 location where you want to store model artifacts.
|
CreateTrainingJobRequest.Builder |
overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) |
CreateTrainingJobRequest.Builder |
overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) |
default CreateTrainingJobRequest.Builder |
profilerConfig(Consumer<ProfilerConfig.Builder> profilerConfig)
Sets the value of the ProfilerConfig property for this object.
|
CreateTrainingJobRequest.Builder |
profilerConfig(ProfilerConfig profilerConfig)
Sets the value of the ProfilerConfig property for this object.
|
CreateTrainingJobRequest.Builder |
profilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
|
CreateTrainingJobRequest.Builder |
profilerRuleConfigurations(Consumer<ProfilerRuleConfiguration.Builder>... profilerRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
|
CreateTrainingJobRequest.Builder |
profilerRuleConfigurations(ProfilerRuleConfiguration... profilerRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
|
default CreateTrainingJobRequest.Builder |
resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
CreateTrainingJobRequest.Builder |
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 an
InternalServerError. |
CreateTrainingJobRequest.Builder |
retryStrategy(RetryStrategy retryStrategy)
The number of times to retry the job when the job fails due to an
InternalServerError. |
CreateTrainingJobRequest.Builder |
roleArn(String roleArn)
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.
|
CreateTrainingJobRequest.Builder |
stoppingCondition(StoppingCondition stoppingCondition)
Specifies a limit to how long a model training job can run.
|
CreateTrainingJobRequest.Builder |
tags(Collection<Tag> tags)
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
tags(Consumer<Tag.Builder>... tags)
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
tags(Tag... tags)
An array of key-value pairs.
|
default CreateTrainingJobRequest.Builder |
tensorBoardOutputConfig(Consumer<TensorBoardOutputConfig.Builder> tensorBoardOutputConfig)
Sets the value of the TensorBoardOutputConfig property for this object.
|
CreateTrainingJobRequest.Builder |
tensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)
Sets the value of the TensorBoardOutputConfig property for this object.
|
CreateTrainingJobRequest.Builder |
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.
|
CreateTrainingJobRequest.Builder |
vpcConfig(VpcConfig vpcConfig)
A VpcConfig object that specifies the VPC that you want your training job to connect to.
|
buildoverrideConfigurationequalsBySdkFields, sdkFieldscopyapplyMutation, buildCreateTrainingJobRequest.Builder trainingJobName(String trainingJobName)
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
trainingJobName - The name of the training job. The name must be unique within an Amazon Web Services Region in an
Amazon Web Services account.CreateTrainingJobRequest.Builder hyperParameters(Map<String,String> 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.
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.
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.
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.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 via AlgorithmSpecification.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and
its result is passed to algorithmSpecification(AlgorithmSpecification).
algorithmSpecification - a consumer that will call methods on AlgorithmSpecification.BuilderalgorithmSpecification(AlgorithmSpecification)CreateTrainingJobRequest.Builder roleArn(String 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.
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.
CreateTrainingJobRequest.Builder inputDataConfig(Collection<Channel> inputDataConfig)
An array of Channel objects. Each channel is a named input source. InputDataConfig
describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels
of input data, training_data and validation_data. The configuration for each
channel provides the S3, 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.
inputDataConfig - An array of Channel objects. Each channel is a named input source.
InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two
channels of input data, training_data and validation_data. The configuration
for each channel provides the S3, 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.
CreateTrainingJobRequest.Builder inputDataConfig(Channel... inputDataConfig)
An array of Channel objects. Each channel is a named input source. InputDataConfig
describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels
of input data, training_data and validation_data. The configuration for each
channel provides the S3, 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.
inputDataConfig - An array of Channel objects. Each channel is a named input source.
InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two
channels of input data, training_data and validation_data. The configuration
for each channel provides the S3, 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.
CreateTrainingJobRequest.Builder inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of Channel objects. Each channel is a named input source. InputDataConfig
describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels
of input data, training_data and validation_data. The configuration for each
channel provides the S3, 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.
This is a convenience method that creates an instance of theChannel.Builder avoiding the need to create one
manually via Channel.builder().
When the Consumer completes,
SdkBuilder.build() is called immediately and its
result is passed to #inputDataConfig(List.
inputDataConfig - a consumer that will call methods on
Channel.Builder#inputDataConfig(java.util.Collection) CreateTrainingJobRequest.Builder outputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
outputDataConfig - Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates
subfolders for the artifacts.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 via OutputDataConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to outputDataConfig(OutputDataConfig).
outputDataConfig - a consumer that will call methods on OutputDataConfig.BuilderoutputDataConfig(OutputDataConfig)CreateTrainingJobRequest.Builder resourceConfig(ResourceConfig 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 the TrainingInputMode in the algorithm specification. For
distributed training algorithms, specify an instance count greater than 1.
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 the TrainingInputMode in the algorithm
specification. For distributed training algorithms, specify an instance count greater than 1.
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 File as the TrainingInputMode in the algorithm specification. For
distributed training algorithms, specify an instance count greater than 1.
ResourceConfig.Builder avoiding the need
to create one manually via ResourceConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to resourceConfig(ResourceConfig).
resourceConfig - a consumer that will call methods on ResourceConfig.BuilderresourceConfig(ResourceConfig)CreateTrainingJobRequest.Builder vpcConfig(VpcConfig 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.
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.default CreateTrainingJobRequest.Builder vpcConfig(Consumer<VpcConfig.Builder> 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 via VpcConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its result
is passed to vpcConfig(VpcConfig).
vpcConfig - a consumer that will call methods on VpcConfig.BuildervpcConfig(VpcConfig)CreateTrainingJobRequest.Builder stoppingCondition(StoppingCondition 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.
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.
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 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.
StoppingCondition.Builder avoiding the
need to create one manually via StoppingCondition.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to stoppingCondition(StoppingCondition).
stoppingCondition - a consumer that will call methods on StoppingCondition.BuilderstoppingCondition(StoppingCondition)CreateTrainingJobRequest.Builder tags(Collection<Tag> 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.
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.CreateTrainingJobRequest.Builder tags(Tag... 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.
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.CreateTrainingJobRequest.Builder tags(Consumer<Tag.Builder>... 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
via Tag.builder().
When the Consumer completes,
SdkBuilder.build() is called immediately and its
result is passed to #tags(List.
tags - a consumer that will call methods on
Tag.Builder#tags(java.util.Collection) CreateTrainingJobRequest.Builder enableNetworkIsolation(Boolean 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.
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.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.
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.CreateTrainingJobRequest.Builder enableManagedSpotTraining(Boolean 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.
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.
CreateTrainingJobRequest.Builder checkpointConfig(CheckpointConfig checkpointConfig)
Contains information about the output location for managed spot training checkpoint data.
checkpointConfig - Contains information about the output location for managed spot training checkpoint data.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 via CheckpointConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to checkpointConfig(CheckpointConfig).
checkpointConfig - a consumer that will call methods on CheckpointConfig.BuildercheckpointConfig(CheckpointConfig)CreateTrainingJobRequest.Builder debugHookConfig(DebugHookConfig debugHookConfig)
debugHookConfig - The new value for the DebugHookConfig property for this object.default CreateTrainingJobRequest.Builder debugHookConfig(Consumer<DebugHookConfig.Builder> debugHookConfig)
DebugHookConfig.Builder avoiding the
need to create one manually via DebugHookConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to debugHookConfig(DebugHookConfig).
debugHookConfig - a consumer that will call methods on DebugHookConfig.BuilderdebugHookConfig(DebugHookConfig)CreateTrainingJobRequest.Builder debugRuleConfigurations(Collection<DebugRuleConfiguration> debugRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
debugRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.CreateTrainingJobRequest.Builder debugRuleConfigurations(DebugRuleConfiguration... debugRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
debugRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.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 via
DebugRuleConfiguration.builder().
When the Consumer completes,
SdkBuilder.build() is called
immediately and its result is passed to #debugRuleConfigurations(List.
debugRuleConfigurations - a consumer that will call methods on
DebugRuleConfiguration.Builder#debugRuleConfigurations(java.util.Collection) CreateTrainingJobRequest.Builder tensorBoardOutputConfig(TensorBoardOutputConfig tensorBoardOutputConfig)
tensorBoardOutputConfig - The new value for the TensorBoardOutputConfig property for this object.default CreateTrainingJobRequest.Builder tensorBoardOutputConfig(Consumer<TensorBoardOutputConfig.Builder> tensorBoardOutputConfig)
TensorBoardOutputConfig.Builder avoiding
the need to create one manually via TensorBoardOutputConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately
and its result is passed to tensorBoardOutputConfig(TensorBoardOutputConfig).
tensorBoardOutputConfig - a consumer that will call methods on TensorBoardOutputConfig.BuildertensorBoardOutputConfig(TensorBoardOutputConfig)CreateTrainingJobRequest.Builder experimentConfig(ExperimentConfig experimentConfig)
experimentConfig - The new value for the ExperimentConfig property for this object.default CreateTrainingJobRequest.Builder experimentConfig(Consumer<ExperimentConfig.Builder> experimentConfig)
ExperimentConfig.Builder avoiding the
need to create one manually via ExperimentConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to experimentConfig(ExperimentConfig).
experimentConfig - a consumer that will call methods on ExperimentConfig.BuilderexperimentConfig(ExperimentConfig)CreateTrainingJobRequest.Builder profilerConfig(ProfilerConfig profilerConfig)
profilerConfig - The new value for the ProfilerConfig property for this object.default CreateTrainingJobRequest.Builder profilerConfig(Consumer<ProfilerConfig.Builder> profilerConfig)
ProfilerConfig.Builder avoiding the need
to create one manually via ProfilerConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to profilerConfig(ProfilerConfig).
profilerConfig - a consumer that will call methods on ProfilerConfig.BuilderprofilerConfig(ProfilerConfig)CreateTrainingJobRequest.Builder profilerRuleConfigurations(Collection<ProfilerRuleConfiguration> profilerRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
profilerRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for profiling system and framework
metrics.CreateTrainingJobRequest.Builder profilerRuleConfigurations(ProfilerRuleConfiguration... profilerRuleConfigurations)
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
profilerRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for profiling system and framework
metrics.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 via
ProfilerRuleConfiguration.builder().
When the Consumer completes,
SdkBuilder.build() is called
immediately and its result is passed to #profilerRuleConfigurations(List.
profilerRuleConfigurations - a consumer that will call methods on
ProfilerRuleConfiguration.Builder#profilerRuleConfigurations(java.util.Collection) CreateTrainingJobRequest.Builder environment(Map<String,String> environment)
The environment variables to set in the Docker container.
environment - The environment variables to set in the Docker container.CreateTrainingJobRequest.Builder retryStrategy(RetryStrategy retryStrategy)
The number of times to retry the job when the job fails due to an InternalServerError.
retryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.default CreateTrainingJobRequest.Builder retryStrategy(Consumer<RetryStrategy.Builder> retryStrategy)
The number of times to retry the job when the job fails due to an InternalServerError.
RetryStrategy.Builder avoiding the need
to create one manually via RetryStrategy.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to retryStrategy(RetryStrategy).
retryStrategy - a consumer that will call methods on RetryStrategy.BuilderretryStrategy(RetryStrategy)CreateTrainingJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
overrideConfiguration in interface AwsRequest.BuilderCreateTrainingJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
overrideConfiguration in interface AwsRequest.BuilderCopyright © 2022. All rights reserved.