@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 SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
Constructor and Description |
---|
AlgorithmSpecification() |
Modifier and Type | Method and Description |
---|---|
AlgorithmSpecification |
clone() |
boolean |
equals(Object obj) |
String |
getAlgorithmName()
The name of the algorithm resource to use for the training job.
|
List<String> |
getContainerArguments()
The arguments for a container used to run a training job.
|
List<String> |
getContainerEntrypoint()
The entrypoint script for a Docker container used
to run a training job.
|
Boolean |
getEnableSageMakerMetricsTimeSeries()
To generate and save time-series metrics during training, set to
true . |
List<MetricDefinition> |
getMetricDefinitions()
A list of metric definition objects.
|
String |
getTrainingImage()
The registry path of the Docker image that contains the training algorithm.
|
TrainingImageConfig |
getTrainingImageConfig()
The configuration to use an image from a private Docker registry for a training job.
|
String |
getTrainingInputMode() |
int |
hashCode() |
Boolean |
isEnableSageMakerMetricsTimeSeries()
To generate and save time-series metrics during training, set to
true . |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setAlgorithmName(String algorithmName)
The name of the algorithm resource to use for the training job.
|
void |
setContainerArguments(Collection<String> containerArguments)
The arguments for a container used to run a training job.
|
void |
setContainerEntrypoint(Collection<String> containerEntrypoint)
The entrypoint script for a Docker container used
to run a training job.
|
void |
setEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries)
To generate and save time-series metrics during training, set to
true . |
void |
setMetricDefinitions(Collection<MetricDefinition> metricDefinitions)
A list of metric definition objects.
|
void |
setTrainingImage(String trainingImage)
The registry path of the Docker image that contains the training algorithm.
|
void |
setTrainingImageConfig(TrainingImageConfig trainingImageConfig)
The configuration to use an image from a private Docker registry for a training job.
|
void |
setTrainingInputMode(String trainingInputMode) |
String |
toString()
Returns a string representation of this object.
|
AlgorithmSpecification |
withAlgorithmName(String algorithmName)
The name of the algorithm resource to use for the training job.
|
AlgorithmSpecification |
withContainerArguments(Collection<String> containerArguments)
The arguments for a container used to run a training job.
|
AlgorithmSpecification |
withContainerArguments(String... containerArguments)
The arguments for a container used to run a training job.
|
AlgorithmSpecification |
withContainerEntrypoint(Collection<String> containerEntrypoint)
The entrypoint script for a Docker container used
to run a training job.
|
AlgorithmSpecification |
withContainerEntrypoint(String... containerEntrypoint)
The entrypoint script for a Docker container used
to run a training job.
|
AlgorithmSpecification |
withEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries)
To generate and save time-series metrics during training, set to
true . |
AlgorithmSpecification |
withMetricDefinitions(Collection<MetricDefinition> metricDefinitions)
A list of metric definition objects.
|
AlgorithmSpecification |
withMetricDefinitions(MetricDefinition... metricDefinitions)
A list of metric definition objects.
|
AlgorithmSpecification |
withTrainingImage(String trainingImage)
The registry path of the Docker image that contains the training algorithm.
|
AlgorithmSpecification |
withTrainingImageConfig(TrainingImageConfig trainingImageConfig)
The configuration to use an image from a private Docker registry for a training job.
|
AlgorithmSpecification |
withTrainingInputMode(String trainingInputMode) |
AlgorithmSpecification |
withTrainingInputMode(TrainingInputMode trainingInputMode) |
public void setTrainingImage(String 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]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
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]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
public String getTrainingImage()
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]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
registry/repository[:tag]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
public AlgorithmSpecification withTrainingImage(String 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]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
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]
and registry/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 the TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter description.
public void setAlgorithmName(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 Amazon Web Services Marketplace.
You must specify either the algorithm name to the AlgorithmName
parameter or the image URI of the
algorithm container to the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter, you can't specify a value for
TrainingImage
, 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.
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 the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the
TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter,
you can't specify a value for TrainingImage
, 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.
public String getAlgorithmName()
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 the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter, you can't specify a value for
TrainingImage
, 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.
You must specify either the algorithm name to the AlgorithmName
parameter or the image URI
of the algorithm container to the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the
TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter, you can't specify a value for TrainingImage
, 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.
public AlgorithmSpecification withAlgorithmName(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 Amazon Web Services Marketplace.
You must specify either the algorithm name to the AlgorithmName
parameter or the image URI of the
algorithm container to the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter, you can't specify a value for
TrainingImage
, 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.
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 the TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with the
TrainingImage
parameter. If you specify a value for the AlgorithmName
parameter,
you can't specify a value for TrainingImage
, 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.
public void setTrainingInputMode(String trainingInputMode)
trainingInputMode
- TrainingInputMode
public String getTrainingInputMode()
TrainingInputMode
public AlgorithmSpecification withTrainingInputMode(String trainingInputMode)
trainingInputMode
- TrainingInputMode
public AlgorithmSpecification withTrainingInputMode(TrainingInputMode trainingInputMode)
trainingInputMode
- TrainingInputMode
public List<MetricDefinition> getMetricDefinitions()
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.
public void setMetricDefinitions(Collection<MetricDefinition> 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.
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.public AlgorithmSpecification withMetricDefinitions(MetricDefinition... 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.
NOTE: This method appends the values to the existing list (if any). Use
setMetricDefinitions(java.util.Collection)
or withMetricDefinitions(java.util.Collection)
if
you want to override the existing values.
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.public AlgorithmSpecification withMetricDefinitions(Collection<MetricDefinition> 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.
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.public void setEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries)
To generate and save time-series metrics during training, set to true
. The default is
false
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
enableSageMakerMetricsTimeSeries
- To generate and save time-series metrics during training, set to true
. The default is
false
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
public Boolean getEnableSageMakerMetricsTimeSeries()
To generate and save time-series metrics during training, set to true
. The default is
false
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
true
. The default is
false
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
public AlgorithmSpecification withEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries)
To generate and save time-series metrics during training, set to true
. The default is
false
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
enableSageMakerMetricsTimeSeries
- To generate and save time-series metrics during training, set to true
. The default is
false
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
public Boolean isEnableSageMakerMetricsTimeSeries()
To generate and save time-series metrics during training, set to true
. The default is
false
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
true
. The default is
false
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
public List<String> getContainerEntrypoint()
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.
public void setContainerEntrypoint(Collection<String> 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.
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.public AlgorithmSpecification withContainerEntrypoint(String... 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.
NOTE: This method appends the values to the existing list (if any). Use
setContainerEntrypoint(java.util.Collection)
or withContainerEntrypoint(java.util.Collection)
if you want to override the existing values.
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.public AlgorithmSpecification withContainerEntrypoint(Collection<String> 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.
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.public List<String> getContainerArguments()
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
public void setContainerArguments(Collection<String> containerArguments)
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
containerArguments
- The arguments for a container used to run a training job. See How
Amazon SageMaker Runs Your Training Image for additional information.public AlgorithmSpecification withContainerArguments(String... containerArguments)
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
NOTE: This method appends the values to the existing list (if any). Use
setContainerArguments(java.util.Collection)
or withContainerArguments(java.util.Collection)
if
you want to override the existing values.
containerArguments
- The arguments for a container used to run a training job. See How
Amazon SageMaker Runs Your Training Image for additional information.public AlgorithmSpecification withContainerArguments(Collection<String> containerArguments)
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
containerArguments
- The arguments for a container used to run a training job. See How
Amazon SageMaker Runs Your Training Image for additional information.public void setTrainingImageConfig(TrainingImageConfig trainingImageConfig)
The configuration to use an image from a private Docker registry for a training job.
trainingImageConfig
- The configuration to use an image from a private Docker registry for a training job.public TrainingImageConfig getTrainingImageConfig()
The configuration to use an image from a private Docker registry for a training job.
public AlgorithmSpecification withTrainingImageConfig(TrainingImageConfig trainingImageConfig)
The configuration to use an image from a private Docker registry for a training job.
trainingImageConfig
- The configuration to use an image from a private Docker registry for a training job.public String toString()
toString
in class Object
Object.toString()
public AlgorithmSpecification clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.