@Generated(value="software.amazon.awssdk:codegen") public final class CreateHyperParameterTuningJobRequest extends SageMakerRequest implements ToCopyableBuilder<CreateHyperParameterTuningJobRequest.Builder,CreateHyperParameterTuningJobRequest>
| Modifier and Type | Class and Description |
|---|---|
static interface |
CreateHyperParameterTuningJobRequest.Builder |
| Modifier and Type | Method and Description |
|---|---|
Autotune |
autotune()
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following
fields:
|
static CreateHyperParameterTuningJobRequest.Builder |
builder() |
boolean |
equals(Object obj) |
boolean |
equalsBySdkFields(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
int |
hashCode() |
boolean |
hasTags()
For responses, this returns true if the service returned a value for the Tags property.
|
boolean |
hasTrainingJobDefinitions()
For responses, this returns true if the service returned a value for the TrainingJobDefinitions property.
|
HyperParameterTuningJobConfig |
hyperParameterTuningJobConfig()
The
HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the
objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the
tuning job.
|
String |
hyperParameterTuningJobName()
The name of the tuning job.
|
List<SdkField<?>> |
sdkFields() |
static Class<? extends CreateHyperParameterTuningJobRequest.Builder> |
serializableBuilderClass() |
List<Tag> |
tags()
An array of key-value pairs.
|
CreateHyperParameterTuningJobRequest.Builder |
toBuilder() |
String |
toString()
Returns a string representation of this object.
|
HyperParameterTrainingJobDefinition |
trainingJobDefinition()
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches,
including static hyperparameters, input data configuration, output data configuration, resource configuration,
and stopping condition.
|
List<HyperParameterTrainingJobDefinition> |
trainingJobDefinitions()
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
|
HyperParameterTuningJobWarmStartConfig |
warmStartConfig()
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as
a starting point.
|
overrideConfigurationclone, finalize, getClass, notify, notifyAll, wait, wait, waitcopypublic final String hyperParameterTuningJobName()
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
public final HyperParameterTuningJobConfig hyperParameterTuningJobConfig()
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
public final HyperParameterTrainingJobDefinition trainingJobDefinition()
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
public final boolean hasTrainingJobDefinitions()
isEmpty() method on the
property). This is useful because the SDK will never return a null collection or map, but you may need to
differentiate between the service returning nothing (or null) and the service returning an empty collection or
map. For requests, this returns true if a value for the property was specified in the request builder, and false
if a value was not specified.public final List<HyperParameterTrainingJobDefinition> trainingJobDefinitions()
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasTrainingJobDefinitions() method.
public final HyperParameterTuningJobWarmStartConfig warmStartConfig()
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If
you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
configuration, the training job that performs the best in the new tuning job is compared to the best training
jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the
objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective
metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value
for the warm start configuration, the training job that performs the best in the new tuning job is
compared to the best training jobs from the parent tuning jobs. From these, the training job that
performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
public final boolean hasTags()
isEmpty() method on the property). This is useful
because the SDK will never return a null collection or map, but you may need to differentiate between the service
returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true
if a value for the property was specified in the request builder, and false if a value was not specified.public final List<Tag> tags()
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasTags() method.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
public final Autotune autotune()
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
RetryStrategy: The number of times to retry a training job.
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
ResourceLimits : The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
RetryStrategy: The number of times to retry a training job.
Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
public CreateHyperParameterTuningJobRequest.Builder toBuilder()
toBuilder in interface ToCopyableBuilder<CreateHyperParameterTuningJobRequest.Builder,CreateHyperParameterTuningJobRequest>toBuilder in class SageMakerRequestpublic static CreateHyperParameterTuningJobRequest.Builder builder()
public static Class<? extends CreateHyperParameterTuningJobRequest.Builder> serializableBuilderClass()
public final int hashCode()
hashCode in class AwsRequestpublic final boolean equals(Object obj)
equals in class AwsRequestpublic final boolean equalsBySdkFields(Object obj)
equalsBySdkFields in interface SdkPojopublic final String toString()
public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
getValueForField in class SdkRequestCopyright © 2023. All rights reserved.