@Generated(value="software.amazon.awssdk:codegen") public final class AlgorithmSpecification extends Object implements StructuredPojo, 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 Bring Your Own Algorithms .
Modifier and Type | Class and Description |
---|---|
static interface |
AlgorithmSpecification.Builder |
Modifier and Type | Method and Description |
---|---|
static AlgorithmSpecification.Builder |
builder() |
boolean |
equals(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
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.
|
copy
public 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 .
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 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.
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()
.
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.
TrainingInputMode
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 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.
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()
.
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.
TrainingInputMode
public AlgorithmSpecification.Builder toBuilder()
ToCopyableBuilder
toBuilder
in interface ToCopyableBuilder<AlgorithmSpecification.Builder,AlgorithmSpecification>
public static AlgorithmSpecification.Builder builder()
public static Class<? extends AlgorithmSpecification.Builder> serializableBuilderClass()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.