public static interface TrainingJobDefinition.Builder extends SdkPojo, CopyableBuilder<TrainingJobDefinition.Builder,TrainingJobDefinition>
| Modifier and Type | Method and Description |
|---|---|
TrainingJobDefinition.Builder |
hyperParameters(Map<String,String> hyperParameters)
The hyperparameters used for the training job.
|
TrainingJobDefinition.Builder |
inputDataConfig(Channel... inputDataConfig)
An array of
Channel objects, each of which specifies an input source. |
TrainingJobDefinition.Builder |
inputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects, each of which specifies an input source. |
TrainingJobDefinition.Builder |
inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of
Channel objects, each of which specifies an input source. |
default TrainingJobDefinition.Builder |
outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
the path to the S3 bucket where you want to store model artifacts.
|
TrainingJobDefinition.Builder |
outputDataConfig(OutputDataConfig outputDataConfig)
the path to the S3 bucket where you want to store model artifacts.
|
default TrainingJobDefinition.Builder |
resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
TrainingJobDefinition.Builder |
resourceConfig(ResourceConfig resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
default TrainingJobDefinition.Builder |
stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition)
Specifies a limit to how long a model training job can run.
|
TrainingJobDefinition.Builder |
stoppingCondition(StoppingCondition stoppingCondition)
Specifies a limit to how long a model training job can run.
|
TrainingJobDefinition.Builder |
trainingInputMode(String trainingInputMode)
Sets the value of the TrainingInputMode property for this object.
|
TrainingJobDefinition.Builder |
trainingInputMode(TrainingInputMode trainingInputMode)
Sets the value of the TrainingInputMode property for this object.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildTrainingJobDefinition.Builder trainingInputMode(String trainingInputMode)
trainingInputMode - The new value for the TrainingInputMode property for this object.TrainingInputMode,
TrainingInputModeTrainingJobDefinition.Builder trainingInputMode(TrainingInputMode trainingInputMode)
trainingInputMode - The new value for the TrainingInputMode property for this object.TrainingInputMode,
TrainingInputModeTrainingJobDefinition.Builder hyperParameters(Map<String,String> hyperParameters)
The hyperparameters used for the training job.
hyperParameters - The hyperparameters used for the training job.TrainingJobDefinition.Builder inputDataConfig(Collection<Channel> inputDataConfig)
An array of Channel objects, each of which specifies an input source.
inputDataConfig - An array of Channel objects, each of which specifies an input source.TrainingJobDefinition.Builder inputDataConfig(Channel... inputDataConfig)
An array of Channel objects, each of which specifies an input source.
inputDataConfig - An array of Channel objects, each of which specifies an input source.TrainingJobDefinition.Builder inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of Channel objects, each of which specifies an input source.
Channel.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) TrainingJobDefinition.Builder outputDataConfig(OutputDataConfig outputDataConfig)
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
outputDataConfig - the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for
the artifacts.default TrainingJobDefinition.Builder outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
the path to the S3 bucket 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)TrainingJobDefinition.Builder resourceConfig(ResourceConfig resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
resourceConfig - The resources, including the ML compute instances and ML storage volumes, to use for model training.default TrainingJobDefinition.Builder resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
This is a convenience method that creates an instance of theResourceConfig.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)TrainingJobDefinition.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.
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.
default TrainingJobDefinition.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.
This is a convenience method that creates an instance of theStoppingCondition.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)Copyright © 2023. All rights reserved.