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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.AmazonWebServiceRequest;
/**
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class CreateAutoMLJobV2Request extends com.amazonaws.AmazonWebServiceRequest implements Serializable, Cloneable {
/**
*
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*
*/
private String autoMLJobName;
/**
*
* An array of channel objects describing the input data and their location. Each channel is a named input source.
* Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend
* on the problem type:
*
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*
*/
private java.util.List autoMLJobInputDataConfig;
/**
*
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
*
*/
private AutoMLOutputDataConfig outputDataConfig;
/**
*
* Defines the configuration settings of one of the supported problem types.
*
*/
private AutoMLProblemTypeConfig autoMLProblemTypeConfig;
/**
*
* The ARN of the role that is used to access the data.
*
*/
private String roleArn;
/**
*
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways,
* such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources.
* Tag keys must be unique per resource.
*
*/
private java.util.List tags;
/**
*
* The security configuration for traffic encryption or Amazon VPC settings.
*
*/
private AutoMLSecurityConfig securityConfig;
/**
*
* 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. For the list of default values per problem type, see AutoMLJobObjective.
*
*
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type
* of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType
* ), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require
* setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple
* candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target
* model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can
* evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*
*/
private AutoMLJobObjective autoMLJobObjective;
/**
*
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*
*/
private ModelDeployConfig modelDeployConfig;
/**
*
* This structure specifies how to split the data into train and validation datasets.
*
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits
* the input dataset into training and validation sets.
*
*
*/
private AutoMLDataSplitConfig dataSplitConfig;
/**
*
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*
*
* @param autoMLJobName
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*/
public void setAutoMLJobName(String autoMLJobName) {
this.autoMLJobName = autoMLJobName;
}
/**
*
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*
*
* @return Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*/
public String getAutoMLJobName() {
return this.autoMLJobName;
}
/**
*
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
*
*
* @param autoMLJobName
* Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withAutoMLJobName(String autoMLJobName) {
setAutoMLJobName(autoMLJobName);
return this;
}
/**
*
* An array of channel objects describing the input data and their location. Each channel is a named input source.
* Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend
* on the problem type:
*
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*
*
* @return An array of channel objects describing the input data and their location. Each channel is a named input
* source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported
* formats depend on the problem type:
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile,
* AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*/
public java.util.List getAutoMLJobInputDataConfig() {
return autoMLJobInputDataConfig;
}
/**
*
* An array of channel objects describing the input data and their location. Each channel is a named input source.
* Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend
* on the problem type:
*
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*
*
* @param autoMLJobInputDataConfig
* An array of channel objects describing the input data and their location. Each channel is a named input
* source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats
* depend on the problem type:
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile,
* AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*/
public void setAutoMLJobInputDataConfig(java.util.Collection autoMLJobInputDataConfig) {
if (autoMLJobInputDataConfig == null) {
this.autoMLJobInputDataConfig = null;
return;
}
this.autoMLJobInputDataConfig = new java.util.ArrayList(autoMLJobInputDataConfig);
}
/**
*
* An array of channel objects describing the input data and their location. Each channel is a named input source.
* Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend
* on the problem type:
*
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setAutoMLJobInputDataConfig(java.util.Collection)} or
* {@link #withAutoMLJobInputDataConfig(java.util.Collection)} if you want to override the existing values.
*
*
* @param autoMLJobInputDataConfig
* An array of channel objects describing the input data and their location. Each channel is a named input
* source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats
* depend on the problem type:
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile,
* AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withAutoMLJobInputDataConfig(AutoMLJobChannel... autoMLJobInputDataConfig) {
if (this.autoMLJobInputDataConfig == null) {
setAutoMLJobInputDataConfig(new java.util.ArrayList(autoMLJobInputDataConfig.length));
}
for (AutoMLJobChannel ele : autoMLJobInputDataConfig) {
this.autoMLJobInputDataConfig.add(ele);
}
return this;
}
/**
*
* An array of channel objects describing the input data and their location. Each channel is a named input source.
* Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend
* on the problem type:
*
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
*
*
* @param autoMLJobInputDataConfig
* An array of channel objects describing the input data and their location. Each channel is a named input
* source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats
* depend on the problem type:
*
* -
*
* For tabular problem types: S3Prefix, ManifestFile.
*
*
* -
*
* For image classification: S3Prefix, ManifestFile,
* AugmentedManifestFile.
*
*
* -
*
* For text classification: S3Prefix.
*
*
* -
*
* For time-series forecasting: S3Prefix.
*
*
* -
*
* For text generation (LLMs fine-tuning): S3Prefix.
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withAutoMLJobInputDataConfig(java.util.Collection autoMLJobInputDataConfig) {
setAutoMLJobInputDataConfig(autoMLJobInputDataConfig);
return this;
}
/**
*
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
*
*
* @param outputDataConfig
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an
* AutoML job.
*/
public void setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) {
this.outputDataConfig = outputDataConfig;
}
/**
*
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
*
*
* @return Provides information about encryption and the Amazon S3 output path needed to store artifacts from an
* AutoML job.
*/
public AutoMLOutputDataConfig getOutputDataConfig() {
return this.outputDataConfig;
}
/**
*
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
*
*
* @param outputDataConfig
* Provides information about encryption and the Amazon S3 output path needed to store artifacts from an
* AutoML job.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) {
setOutputDataConfig(outputDataConfig);
return this;
}
/**
*
* Defines the configuration settings of one of the supported problem types.
*
*
* @param autoMLProblemTypeConfig
* Defines the configuration settings of one of the supported problem types.
*/
public void setAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig) {
this.autoMLProblemTypeConfig = autoMLProblemTypeConfig;
}
/**
*
* Defines the configuration settings of one of the supported problem types.
*
*
* @return Defines the configuration settings of one of the supported problem types.
*/
public AutoMLProblemTypeConfig getAutoMLProblemTypeConfig() {
return this.autoMLProblemTypeConfig;
}
/**
*
* Defines the configuration settings of one of the supported problem types.
*
*
* @param autoMLProblemTypeConfig
* Defines the configuration settings of one of the supported problem types.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig) {
setAutoMLProblemTypeConfig(autoMLProblemTypeConfig);
return this;
}
/**
*
* The ARN of the role that is used to access the data.
*
*
* @param roleArn
* The ARN of the role that is used to access the data.
*/
public void setRoleArn(String roleArn) {
this.roleArn = roleArn;
}
/**
*
* The ARN of the role that is used to access the data.
*
*
* @return The ARN of the role that is used to access the data.
*/
public String getRoleArn() {
return this.roleArn;
}
/**
*
* The ARN of the role that is used to access the data.
*
*
* @param roleArn
* The ARN of the role that is used to access the data.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withRoleArn(String roleArn) {
setRoleArn(roleArn);
return this;
}
/**
*
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways,
* such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources.
* Tag keys must be unique per resource.
*
*
* @return An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
* different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web
* ServicesResources. Tag keys must be unique per resource.
*/
public java.util.List getTags() {
return tags;
}
/**
*
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways,
* such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources.
* Tag keys must be unique per resource.
*
*
* @param tags
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
* different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web
* ServicesResources. Tag keys must be unique per resource.
*/
public void setTags(java.util.Collection tags) {
if (tags == null) {
this.tags = null;
return;
}
this.tags = new java.util.ArrayList(tags);
}
/**
*
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways,
* such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources.
* Tag keys must be unique per resource.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setTags(java.util.Collection)} or {@link #withTags(java.util.Collection)} if you want to override the
* existing values.
*
*
* @param tags
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
* different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web
* ServicesResources. Tag keys must be unique per resource.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withTags(Tag... tags) {
if (this.tags == null) {
setTags(new java.util.ArrayList(tags.length));
}
for (Tag ele : tags) {
this.tags.add(ele);
}
return this;
}
/**
*
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways,
* such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources.
* Tag keys must be unique per resource.
*
*
* @param tags
* An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in
* different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web
* ServicesResources. Tag keys must be unique per resource.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withTags(java.util.Collection tags) {
setTags(tags);
return this;
}
/**
*
* The security configuration for traffic encryption or Amazon VPC settings.
*
*
* @param securityConfig
* The security configuration for traffic encryption or Amazon VPC settings.
*/
public void setSecurityConfig(AutoMLSecurityConfig securityConfig) {
this.securityConfig = securityConfig;
}
/**
*
* The security configuration for traffic encryption or Amazon VPC settings.
*
*
* @return The security configuration for traffic encryption or Amazon VPC settings.
*/
public AutoMLSecurityConfig getSecurityConfig() {
return this.securityConfig;
}
/**
*
* The security configuration for traffic encryption or Amazon VPC settings.
*
*
* @param securityConfig
* The security configuration for traffic encryption or Amazon VPC settings.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withSecurityConfig(AutoMLSecurityConfig securityConfig) {
setSecurityConfig(securityConfig);
return this;
}
/**
*
* 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. For the list of default values per problem type, see AutoMLJobObjective.
*
*
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type
* of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType
* ), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require
* setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple
* candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target
* model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can
* evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*
*
* @param 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. For the list of default values per problem type, see
* AutoMLJobObjective.
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate
* the type of supervised learning problem in AutoMLProblemTypeConfig (
* TabularJobConfig.ProblemType), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not
* require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring
* multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly
* fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After
* fine-tuning a language model, you can evaluate the quality of its generated text using different metrics.
* For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*/
public void setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) {
this.autoMLJobObjective = 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. For the list of default values per problem type, see AutoMLJobObjective.
*
*
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type
* of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType
* ), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require
* setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple
* candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target
* model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can
* evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*
*
* @return 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. For the list of default values per problem type, see
* AutoMLJobObjective.
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate
* the type of supervised learning problem in AutoMLProblemTypeConfig (
* TabularJobConfig.ProblemType), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not
* require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring
* multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly
* fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After
* fine-tuning a language model, you can evaluate the quality of its generated text using different metrics.
* For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*/
public AutoMLJobObjective getAutoMLJobObjective() {
return this.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. For the list of default values per problem type, see AutoMLJobObjective.
*
*
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type
* of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType
* ), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require
* setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple
* candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target
* model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can
* evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
*
*
* @param 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. For the list of default values per problem type, see
* AutoMLJobObjective.
*
* -
*
* For tabular problem types: You must either provide both the AutoMLJobObjective and indicate
* the type of supervised learning problem in AutoMLProblemTypeConfig (
* TabularJobConfig.ProblemType), or none at all.
*
*
* -
*
* For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not
* require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring
* multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly
* fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After
* fine-tuning a language model, you can evaluate the quality of its generated text using different metrics.
* For a list of the available metrics, see Metrics for
* fine-tuning LLMs in Autopilot.
*
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) {
setAutoMLJobObjective(autoMLJobObjective);
return this;
}
/**
*
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*
*
* @param modelDeployConfig
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*/
public void setModelDeployConfig(ModelDeployConfig modelDeployConfig) {
this.modelDeployConfig = modelDeployConfig;
}
/**
*
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*
*
* @return Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*/
public ModelDeployConfig getModelDeployConfig() {
return this.modelDeployConfig;
}
/**
*
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
*
*
* @param modelDeployConfig
* Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withModelDeployConfig(ModelDeployConfig modelDeployConfig) {
setModelDeployConfig(modelDeployConfig);
return this;
}
/**
*
* This structure specifies how to split the data into train and validation datasets.
*
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits
* the input dataset into training and validation sets.
*
*
*
* @param dataSplitConfig
* This structure specifies how to split the data into train and validation datasets.
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically
* splits the input dataset into training and validation sets.
*
*/
public void setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) {
this.dataSplitConfig = dataSplitConfig;
}
/**
*
* This structure specifies how to split the data into train and validation datasets.
*
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits
* the input dataset into training and validation sets.
*
*
*
* @return This structure specifies how to split the data into train and validation datasets.
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically
* splits the input dataset into training and validation sets.
*
*/
public AutoMLDataSplitConfig getDataSplitConfig() {
return this.dataSplitConfig;
}
/**
*
* This structure specifies how to split the data into train and validation datasets.
*
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits
* the input dataset into training and validation sets.
*
*
*
* @param dataSplitConfig
* This structure specifies how to split the data into train and validation datasets.
*
* The validation and training datasets must contain the same headers. For jobs created by calling
* CreateAutoMLJob, the validation dataset must be less than 2 GB in size.
*
*
*
* This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically
* splits the input dataset into training and validation sets.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateAutoMLJobV2Request withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) {
setDataSplitConfig(dataSplitConfig);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getAutoMLJobName() != null)
sb.append("AutoMLJobName: ").append(getAutoMLJobName()).append(",");
if (getAutoMLJobInputDataConfig() != null)
sb.append("AutoMLJobInputDataConfig: ").append(getAutoMLJobInputDataConfig()).append(",");
if (getOutputDataConfig() != null)
sb.append("OutputDataConfig: ").append(getOutputDataConfig()).append(",");
if (getAutoMLProblemTypeConfig() != null)
sb.append("AutoMLProblemTypeConfig: ").append(getAutoMLProblemTypeConfig()).append(",");
if (getRoleArn() != null)
sb.append("RoleArn: ").append(getRoleArn()).append(",");
if (getTags() != null)
sb.append("Tags: ").append(getTags()).append(",");
if (getSecurityConfig() != null)
sb.append("SecurityConfig: ").append(getSecurityConfig()).append(",");
if (getAutoMLJobObjective() != null)
sb.append("AutoMLJobObjective: ").append(getAutoMLJobObjective()).append(",");
if (getModelDeployConfig() != null)
sb.append("ModelDeployConfig: ").append(getModelDeployConfig()).append(",");
if (getDataSplitConfig() != null)
sb.append("DataSplitConfig: ").append(getDataSplitConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof CreateAutoMLJobV2Request == false)
return false;
CreateAutoMLJobV2Request other = (CreateAutoMLJobV2Request) obj;
if (other.getAutoMLJobName() == null ^ this.getAutoMLJobName() == null)
return false;
if (other.getAutoMLJobName() != null && other.getAutoMLJobName().equals(this.getAutoMLJobName()) == false)
return false;
if (other.getAutoMLJobInputDataConfig() == null ^ this.getAutoMLJobInputDataConfig() == null)
return false;
if (other.getAutoMLJobInputDataConfig() != null && other.getAutoMLJobInputDataConfig().equals(this.getAutoMLJobInputDataConfig()) == false)
return false;
if (other.getOutputDataConfig() == null ^ this.getOutputDataConfig() == null)
return false;
if (other.getOutputDataConfig() != null && other.getOutputDataConfig().equals(this.getOutputDataConfig()) == false)
return false;
if (other.getAutoMLProblemTypeConfig() == null ^ this.getAutoMLProblemTypeConfig() == null)
return false;
if (other.getAutoMLProblemTypeConfig() != null && other.getAutoMLProblemTypeConfig().equals(this.getAutoMLProblemTypeConfig()) == false)
return false;
if (other.getRoleArn() == null ^ this.getRoleArn() == null)
return false;
if (other.getRoleArn() != null && other.getRoleArn().equals(this.getRoleArn()) == false)
return false;
if (other.getTags() == null ^ this.getTags() == null)
return false;
if (other.getTags() != null && other.getTags().equals(this.getTags()) == false)
return false;
if (other.getSecurityConfig() == null ^ this.getSecurityConfig() == null)
return false;
if (other.getSecurityConfig() != null && other.getSecurityConfig().equals(this.getSecurityConfig()) == false)
return false;
if (other.getAutoMLJobObjective() == null ^ this.getAutoMLJobObjective() == null)
return false;
if (other.getAutoMLJobObjective() != null && other.getAutoMLJobObjective().equals(this.getAutoMLJobObjective()) == false)
return false;
if (other.getModelDeployConfig() == null ^ this.getModelDeployConfig() == null)
return false;
if (other.getModelDeployConfig() != null && other.getModelDeployConfig().equals(this.getModelDeployConfig()) == false)
return false;
if (other.getDataSplitConfig() == null ^ this.getDataSplitConfig() == null)
return false;
if (other.getDataSplitConfig() != null && other.getDataSplitConfig().equals(this.getDataSplitConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getAutoMLJobName() == null) ? 0 : getAutoMLJobName().hashCode());
hashCode = prime * hashCode + ((getAutoMLJobInputDataConfig() == null) ? 0 : getAutoMLJobInputDataConfig().hashCode());
hashCode = prime * hashCode + ((getOutputDataConfig() == null) ? 0 : getOutputDataConfig().hashCode());
hashCode = prime * hashCode + ((getAutoMLProblemTypeConfig() == null) ? 0 : getAutoMLProblemTypeConfig().hashCode());
hashCode = prime * hashCode + ((getRoleArn() == null) ? 0 : getRoleArn().hashCode());
hashCode = prime * hashCode + ((getTags() == null) ? 0 : getTags().hashCode());
hashCode = prime * hashCode + ((getSecurityConfig() == null) ? 0 : getSecurityConfig().hashCode());
hashCode = prime * hashCode + ((getAutoMLJobObjective() == null) ? 0 : getAutoMLJobObjective().hashCode());
hashCode = prime * hashCode + ((getModelDeployConfig() == null) ? 0 : getModelDeployConfig().hashCode());
hashCode = prime * hashCode + ((getDataSplitConfig() == null) ? 0 : getDataSplitConfig().hashCode());
return hashCode;
}
@Override
public CreateAutoMLJobV2Request clone() {
return (CreateAutoMLJobV2Request) super.clone();
}
}