@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 |
---|---|
static CreateHyperParameterTuningJobRequest.Builder |
builder() |
boolean |
equals(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz)
Used to retrieve the value of a field from any class that extends
SdkRequest . |
int |
hashCode() |
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()
Take this object and create a builder that contains all of the current property values of this object.
|
String |
toString() |
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.
|
HyperParameterTuningJobWarmStartConfig |
warmStartConfig()
Specifies configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a
starting point.
|
overrideConfiguration
copy
public 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 AWS account and AWS Region. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
public 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 automatic-model-tuning
public 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 HyperParameterTuningJobWarmStartConfig warmStartConfig()
Specifies 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
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
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 List<Tag> tags()
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies.
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.
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
public CreateHyperParameterTuningJobRequest.Builder toBuilder()
ToCopyableBuilder
toBuilder
in interface ToCopyableBuilder<CreateHyperParameterTuningJobRequest.Builder,CreateHyperParameterTuningJobRequest>
toBuilder
in class SageMakerRequest
public static CreateHyperParameterTuningJobRequest.Builder builder()
public static Class<? extends CreateHyperParameterTuningJobRequest.Builder> serializableBuilderClass()
public <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
SdkRequest
SdkRequest
. The field name
specified should match the member name from the corresponding service-2.json model specified in the
codegen-resources folder for a given service. The class specifies what class to cast the returned value to.
If the returned value is also a modeled class, the SdkRequest.getValueForField(String, Class)
method will
again be available.getValueForField
in class SdkRequest
fieldName
- The name of the member to be retrieved.clazz
- The class to cast the returned object to.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.