Interface AlgorithmSpecification.Builder
- All Superinterfaces:
Buildable
,CopyableBuilder<AlgorithmSpecification.Builder,
,AlgorithmSpecification> SdkBuilder<AlgorithmSpecification.Builder,
,AlgorithmSpecification> SdkPojo
- Enclosing class:
AlgorithmSpecification
-
Method Summary
Modifier and TypeMethodDescriptionalgorithmName
(String algorithmName) The name of the algorithm resource to use for the training job.containerArguments
(String... containerArguments) The arguments for a container used to run a training job.containerArguments
(Collection<String> containerArguments) The arguments for a container used to run a training job.containerEntrypoint
(String... containerEntrypoint) The entrypoint script for a Docker container used to run a training job.containerEntrypoint
(Collection<String> containerEntrypoint) The entrypoint script for a Docker container used to run a training job.enableSageMakerMetricsTimeSeries
(Boolean enableSageMakerMetricsTimeSeries) To generate and save time-series metrics during training, set totrue
.metricDefinitions
(Collection<MetricDefinition> metricDefinitions) A list of metric definition objects.metricDefinitions
(Consumer<MetricDefinition.Builder>... metricDefinitions) A list of metric definition objects.metricDefinitions
(MetricDefinition... metricDefinitions) A list of metric definition objects.trainingImage
(String trainingImage) The registry path of the Docker image that contains the training algorithm.default AlgorithmSpecification.Builder
trainingImageConfig
(Consumer<TrainingImageConfig.Builder> trainingImageConfig) The configuration to use an image from a private Docker registry for a training job.trainingImageConfig
(TrainingImageConfig trainingImageConfig) The configuration to use an image from a private Docker registry for a training job.trainingInputMode
(String trainingInputMode) Sets the value of the TrainingInputMode property for this object.trainingInputMode
(TrainingInputMode trainingInputMode) Sets the value of the TrainingInputMode property for this object.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
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Method Details
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trainingImage
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.You must specify either the algorithm name to the
AlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter.For more information, see the note in the
AlgorithmName
parameter description.- Parameters:
trainingImage
- The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports bothregistry/repository[:tag]
andregistry/repository[@digest]
image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.You must specify either the algorithm name to the
AlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter.For more information, see the note in the
AlgorithmName
parameter description.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
algorithmName
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
You must specify either the algorithm name to the
AlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter.Note that the
AlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa.If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a
null
error.- Parameters:
algorithmName
- The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.You must specify either the algorithm name to the
AlgorithmName
parameter or the image URI of the algorithm container to theTrainingImage
parameter.Note that the
AlgorithmName
parameter is mutually exclusive with theTrainingImage
parameter. If you specify a value for theAlgorithmName
parameter, you can't specify a value forTrainingImage
, and vice versa.If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a
null
error.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
trainingInputMode
Sets the value of the TrainingInputMode property for this object.- Parameters:
trainingInputMode
- The new value for the TrainingInputMode property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
trainingInputMode
Sets the value of the TrainingInputMode property for this object.- Parameters:
trainingInputMode
- The new value for the TrainingInputMode property for this object.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
metricDefinitions
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
- Parameters:
metricDefinitions
- A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
metricDefinitions
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
- Parameters:
metricDefinitions
- A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
metricDefinitions
AlgorithmSpecification.Builder metricDefinitions(Consumer<MetricDefinition.Builder>... metricDefinitions) A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
This is a convenience method that creates an instance of theMetricDefinition.Builder
avoiding the need to create one manually viaMetricDefinition.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tometricDefinitions(List<MetricDefinition>)
.- Parameters:
metricDefinitions
- a consumer that will call methods onMetricDefinition.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
enableSageMakerMetricsTimeSeries
AlgorithmSpecification.Builder enableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries) To generate and save time-series metrics during training, set to
true
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
- Parameters:
enableSageMakerMetricsTimeSeries
- To generate and save time-series metrics during training, set totrue
. The default isfalse
and time-series metrics aren't generated except in the following cases:-
You use one of the SageMaker built-in algorithms
-
You use one of the following Prebuilt SageMaker Docker Images:
-
Tensorflow (version >= 1.15)
-
MXNet (version >= 1.6)
-
PyTorch (version >= 1.3)
-
-
You specify at least one MetricDefinition
-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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containerEntrypoint
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
- Parameters:
containerEntrypoint
- The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
containerEntrypoint
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
- Parameters:
containerEntrypoint
- The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
containerArguments
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
- Parameters:
containerArguments
- The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
containerArguments
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
- Parameters:
containerArguments
- The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
trainingImageConfig
The configuration to use an image from a private Docker registry for a training job.
- Parameters:
trainingImageConfig
- The configuration to use an image from a private Docker registry for a training job.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
trainingImageConfig
default AlgorithmSpecification.Builder trainingImageConfig(Consumer<TrainingImageConfig.Builder> trainingImageConfig) The configuration to use an image from a private Docker registry for a training job.
This is a convenience method that creates an instance of theTrainingImageConfig.Builder
avoiding the need to create one manually viaTrainingImageConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed totrainingImageConfig(TrainingImageConfig)
.- Parameters:
trainingImageConfig
- a consumer that will call methods onTrainingImageConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-