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)
Sets a duration for training.
|
TrainingJobDefinition.Builder |
stoppingCondition(StoppingCondition stoppingCondition)
Sets a duration for training.
|
TrainingJobDefinition.Builder |
trainingInputMode(String trainingInputMode)
The input mode used by the algorithm for the training job.
|
TrainingJobDefinition.Builder |
trainingInputMode(TrainingInputMode trainingInputMode)
The input mode used by the algorithm for the training job.
|
copyapplyMutation, buildTrainingJobDefinition.Builder trainingInputMode(String trainingInputMode)
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from
S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If
an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
container.
trainingInputMode - The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
algorithms support, see Algorithms.
If an algorithm supports the File input mode, Amazon SageMaker downloads the training
data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for
training container. If an algorithm supports the Pipe input mode, Amazon SageMaker
streams data directly from S3 to the container.
TrainingInputMode,
TrainingInputModeTrainingJobDefinition.Builder trainingInputMode(TrainingInputMode trainingInputMode)
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.
If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from
S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If
an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
container.
trainingInputMode - The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
algorithms support, see Algorithms.
If an algorithm supports the File input mode, Amazon SageMaker downloads the training
data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for
training container. If an algorithm supports the Pipe input mode, Amazon SageMaker
streams data directly from S3 to the container.
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.
List.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #inputDataConfig(List) .inputDataConfig - a consumer that will call methods on List.Builder #inputDataConfig(List) TrainingJobDefinition.Builder outputDataConfig(OutputDataConfig outputDataConfig)
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
outputDataConfig - the path to the S3 bucket where you want to store model artifacts. Amazon 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. Amazon SageMaker creates subfolders for the artifacts.
This is a convenience 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 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)
Sets a duration for training. Use this parameter 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 might use this 120-second window to save the model artifacts.
stoppingCondition - Sets a duration for training. Use this parameter 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 might use this 120-second window to save the model artifacts.
default TrainingJobDefinition.Builder stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition)
Sets a duration for training. Use this parameter 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 might use this 120-second window to save the model artifacts.
This is a convenience 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 © 2017 Amazon Web Services, Inc. All Rights Reserved.