Interface CreateAutoMlJobRequest.Builder
- All Superinterfaces:
AwsRequest.Builder
,Buildable
,CopyableBuilder<CreateAutoMlJobRequest.Builder,
,CreateAutoMlJobRequest> SageMakerRequest.Builder
,SdkBuilder<CreateAutoMlJobRequest.Builder,
,CreateAutoMlJobRequest> SdkPojo
,SdkRequest.Builder
- Enclosing class:
CreateAutoMlJobRequest
-
Method Summary
Modifier and TypeMethodDescriptiondefault CreateAutoMlJobRequest.Builder
autoMLJobConfig
(Consumer<AutoMLJobConfig.Builder> autoMLJobConfig) A collection of settings used to configure an AutoML job.autoMLJobConfig
(AutoMLJobConfig autoMLJobConfig) A collection of settings used to configure an AutoML job.autoMLJobName
(String autoMLJobName) Identifies an Autopilot job.default CreateAutoMlJobRequest.Builder
autoMLJobObjective
(Consumer<AutoMLJobObjective.Builder> autoMLJobObjective) Specifies a metric to minimize or maximize as the objective of a job.autoMLJobObjective
(AutoMLJobObjective autoMLJobObjective) Specifies a metric to minimize or maximize as the objective of a job.generateCandidateDefinitionsOnly
(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.inputDataConfig
(Collection<AutoMLChannel> inputDataConfig) An array of channel objects that describes the input data and its location.inputDataConfig
(Consumer<AutoMLChannel.Builder>... inputDataConfig) An array of channel objects that describes the input data and its location.inputDataConfig
(AutoMLChannel... inputDataConfig) An array of channel objects that describes the input data and its location.default CreateAutoMlJobRequest.Builder
modelDeployConfig
(Consumer<ModelDeployConfig.Builder> modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.modelDeployConfig
(ModelDeployConfig modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.default CreateAutoMlJobRequest.Builder
outputDataConfig
(Consumer<AutoMLOutputDataConfig.Builder> outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.outputDataConfig
(AutoMLOutputDataConfig outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.overrideConfiguration
(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Add an optional request override configuration.overrideConfiguration
(AwsRequestOverrideConfiguration overrideConfiguration) Add an optional request override configuration.problemType
(String problemType) Defines the type of supervised learning problem available for the candidates.problemType
(ProblemType problemType) Defines the type of supervised learning problem available for the candidates.The ARN of the role that is used to access the data.tags
(Collection<Tag> tags) An array of key-value pairs.tags
(Consumer<Tag.Builder>... tags) An array of key-value pairs.An array of key-value pairs.Methods inherited from interface software.amazon.awssdk.awscore.AwsRequest.Builder
overrideConfiguration
Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.services.sagemaker.model.SageMakerRequest.Builder
build
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
-
Method Details
-
autoMLJobName
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
- Parameters:
autoMLJobName
- Identifies an Autopilot job. The name must be unique to your account and is case insensitive.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
InputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.- Parameters:
inputDataConfig
- An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar toInputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
InputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.- Parameters:
inputDataConfig
- An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar toInputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
inputDataConfig
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to
This is a convenience method that creates an instance of theInputDataConfig
supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.AutoMLChannel.Builder
avoiding the need to create one manually viaAutoMLChannel.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toinputDataConfig(List<AutoMLChannel>)
.- Parameters:
inputDataConfig
- a consumer that will call methods onAutoMLChannel.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
outputDataConfig
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
- Parameters:
outputDataConfig
- Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
outputDataConfig
default CreateAutoMlJobRequest.Builder outputDataConfig(Consumer<AutoMLOutputDataConfig.Builder> outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
This is a convenience method that creates an instance of theAutoMLOutputDataConfig.Builder
avoiding the need to create one manually viaAutoMLOutputDataConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tooutputDataConfig(AutoMLOutputDataConfig)
.- Parameters:
outputDataConfig
- a consumer that will call methods onAutoMLOutputDataConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
problemType
Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.
- Parameters:
problemType
- Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
problemType
Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.
- Parameters:
problemType
- Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
autoMLJobObjective
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
- Parameters:
autoMLJobObjective
- Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
autoMLJobObjective
default CreateAutoMlJobRequest.Builder autoMLJobObjective(Consumer<AutoMLJobObjective.Builder> autoMLJobObjective) Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
This is a convenience method that creates an instance of theAutoMLJobObjective.Builder
avoiding the need to create one manually viaAutoMLJobObjective.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toautoMLJobObjective(AutoMLJobObjective)
.- Parameters:
autoMLJobObjective
- a consumer that will call methods onAutoMLJobObjective.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
autoMLJobConfig
A collection of settings used to configure an AutoML job.
- Parameters:
autoMLJobConfig
- A collection of settings used to configure an AutoML job.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
autoMLJobConfig
default CreateAutoMlJobRequest.Builder autoMLJobConfig(Consumer<AutoMLJobConfig.Builder> autoMLJobConfig) A collection of settings used to configure an AutoML job.
This is a convenience method that creates an instance of theAutoMLJobConfig.Builder
avoiding the need to create one manually viaAutoMLJobConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toautoMLJobConfig(AutoMLJobConfig)
.- Parameters:
autoMLJobConfig
- a consumer that will call methods onAutoMLJobConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
roleArn
The ARN of the role that is used to access the data.
- Parameters:
roleArn
- The ARN of the role that is used to access the data.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
generateCandidateDefinitionsOnly
CreateAutoMlJobRequest.Builder generateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
- Parameters:
generateCandidateDefinitionsOnly
- Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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 ServicesResources. Tag keys must be unique per resource.
- Parameters:
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 ServicesResources. Tag keys must be unique per resource.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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 ServicesResources. Tag keys must be unique per resource.
- Parameters:
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 ServicesResources. Tag keys must be unique per resource.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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 ServicesResources. Tag keys must be unique per resource.
This is a convenience method that creates an instance of theTag.Builder
avoiding the need to create one manually viaTag.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed totags(List<Tag>)
.- Parameters:
tags
- a consumer that will call methods onTag.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
modelDeployConfig
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
- Parameters:
modelDeployConfig
- Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
modelDeployConfig
default CreateAutoMlJobRequest.Builder modelDeployConfig(Consumer<ModelDeployConfig.Builder> modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
This is a convenience method that creates an instance of theModelDeployConfig.Builder
avoiding the need to create one manually viaModelDeployConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tomodelDeployConfig(ModelDeployConfig)
.- Parameters:
modelDeployConfig
- a consumer that will call methods onModelDeployConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
overrideConfiguration
CreateAutoMlJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration) Description copied from interface:AwsRequest.Builder
Add an optional request override configuration.- Specified by:
overrideConfiguration
in interfaceAwsRequest.Builder
- Parameters:
overrideConfiguration
- The override configuration.- Returns:
- This object for method chaining.
-
overrideConfiguration
CreateAutoMlJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer) Description copied from interface:AwsRequest.Builder
Add an optional request override configuration.- Specified by:
overrideConfiguration
in interfaceAwsRequest.Builder
- Parameters:
builderConsumer
- AConsumer
to which an emptyAwsRequestOverrideConfiguration.Builder
will be given.- Returns:
- This object for method chaining.
-