com.amazonaws.services.sagemaker.model

Class AlgorithmSpecification

java.lang.Object

com.amazonaws.services.sagemaker.model.AlgorithmSpecification

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AlgorithmSpecification
extends Object
implements Serializable, Cloneable, StructuredPojo

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 Bring Your Own Algorithms .

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AlgorithmSpecification()

Method Summary

Modifier and Type	Method and Description
AlgorithmSpecification	clone()
boolean	equals(Object obj)
String	getTrainingImage() The registry path of the Docker image that contains the training algorithm.
String	getTrainingInputMode() The input mode that the algorithm supports.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setTrainingImage(String trainingImage) The registry path of the Docker image that contains the training algorithm.
void	setTrainingInputMode(String trainingInputMode) The input mode that the algorithm supports.
String	toString() Returns a string representation of this object; useful for testing and debugging.
AlgorithmSpecification	withTrainingImage(String trainingImage) The registry path of the Docker image that contains the training algorithm.
AlgorithmSpecification	withTrainingInputMode(String trainingInputMode) The input mode that the algorithm supports.
AlgorithmSpecification	withTrainingInputMode(TrainingInputMode trainingInputMode) The input mode that the algorithm supports.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AlgorithmSpecification

public AlgorithmSpecification()

Method Detail

setTrainingImage

public void setTrainingImage(String trainingImage)

The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

Parameters:

trainingImage - The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

getTrainingImage

public String getTrainingImage()

The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

Returns:

The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

withTrainingImage

public AlgorithmSpecification withTrainingImage(String trainingImage)

The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

Parameters:

trainingImage - The registry path of the Docker image that contains the training algorithm. For information about using your own algorithms, see Docker Registry Paths for Algorithms Provided by Amazon SageMaker .

Returns:

Returns a reference to this object so that method calls can be chained together.

setTrainingInputMode

public void setTrainingInputMode(String 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 accomodate 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.

Parameters:

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 accomodate 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

getTrainingInputMode

public String getTrainingInputMode()

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 accomodate 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.

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 accomodate 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

withTrainingInputMode

public AlgorithmSpecification withTrainingInputMode(String 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 accomodate 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.

Parameters:

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 accomodate 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

TrainingInputMode

withTrainingInputMode

public AlgorithmSpecification withTrainingInputMode(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 accomodate 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.

Parameters:

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 accomodate 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

TrainingInputMode

toString

public String toString()

Returns a string representation of this object; useful for testing and debugging.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public AlgorithmSpecification clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.