software.amazon.awssdk.services.sagemaker.model

Class AlgorithmSpecification

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.AlgorithmSpecification

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<AlgorithmSpecification.Builder,AlgorithmSpecification>

@Generated(value="software.amazon.awssdk:codegen")
public final class AlgorithmSpecification
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<AlgorithmSpecification.Builder,AlgorithmSpecification>

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	AlgorithmSpecification.Builder

Method Summary

Modifier and Type	Method and Description
String	algorithmName() The name of the algorithm resource to use for the training job.
static AlgorithmSpecification.Builder	builder()
boolean	equals(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
List<MetricDefinition>	metricDefinitions() A list of metric definition objects.
List<SdkField<?>>	sdkFields()
static Class<? extends AlgorithmSpecification.Builder>	serializableBuilderClass()
AlgorithmSpecification.Builder	toBuilder() Take this object and create a builder that contains all of the current property values of this object.
String	toString()
String	trainingImage() The registry path of the Docker image that contains the training algorithm.
TrainingInputMode	trainingInputMode() The input mode that the algorithm supports.
String	trainingInputModeAsString() The input mode that the algorithm supports.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

trainingImage

public String trainingImage()

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters.

Returns:

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters.

algorithmName

public String 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 AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.

Returns:

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 AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.

trainingInputMode

public TrainingInputMode trainingInputMode()

The input mode that the algorithm supports. 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.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

If the service returns an enum value that is not available in the current SDK version, trainingInputMode will return TrainingInputMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingInputModeAsString().

Returns:

The input mode that the algorithm supports. 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.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

See Also:

TrainingInputMode

trainingInputModeAsString

public String trainingInputModeAsString()

The input mode that the algorithm supports. 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.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

If the service returns an enum value that is not available in the current SDK version, trainingInputMode will return TrainingInputMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingInputModeAsString().

Returns:

The input mode that the algorithm supports. 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.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

See Also:

TrainingInputMode

metricDefinitions

public List<MetricDefinition> metricDefinitions()

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

Returns:

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

toBuilder

public AlgorithmSpecification.Builder toBuilder()

Description copied from interface: ToCopyableBuilder

Take this object and create a builder that contains all of the current property values of this object.

Specified by:

toBuilder in interface ToCopyableBuilder<AlgorithmSpecification.Builder,AlgorithmSpecification>

Returns:

a builder for type T

builder

public static AlgorithmSpecification.Builder builder()

serializableBuilderClass

public static Class<? extends AlgorithmSpecification.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

toString

public String toString()

Overrides:

toString in class Object

getValueForField

public <T> Optional<T> getValueForField(String fieldName,
                                        Class<T> clazz)

sdkFields

public List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo

Returns:

List of SdkField in this POJO. May be empty list but should never be null.