com.amazonaws.services.sagemaker.AmazonSageMakerClient Maven / Gradle / Ivy
/*
* Copyright 2015-2020 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;
import org.w3c.dom.*;
import java.net.*;
import java.util.*;
import javax.annotation.Generated;
import org.apache.commons.logging.*;
import com.amazonaws.*;
import com.amazonaws.annotation.SdkInternalApi;
import com.amazonaws.auth.*;
import com.amazonaws.handlers.*;
import com.amazonaws.http.*;
import com.amazonaws.internal.*;
import com.amazonaws.internal.auth.*;
import com.amazonaws.metrics.*;
import com.amazonaws.regions.*;
import com.amazonaws.transform.*;
import com.amazonaws.util.*;
import com.amazonaws.protocol.json.*;
import com.amazonaws.util.AWSRequestMetrics.Field;
import com.amazonaws.annotation.ThreadSafe;
import com.amazonaws.client.AwsSyncClientParams;
import com.amazonaws.client.builder.AdvancedConfig;
import com.amazonaws.services.sagemaker.AmazonSageMakerClientBuilder;
import com.amazonaws.services.sagemaker.waiters.AmazonSageMakerWaiters;
import com.amazonaws.AmazonServiceException;
import com.amazonaws.services.sagemaker.model.*;
import com.amazonaws.services.sagemaker.model.transform.*;
/**
* Client for accessing SageMaker. All service calls made using this client are blocking, and will not return until the
* service call completes.
*
*
* Provides APIs for creating and managing Amazon SageMaker resources.
*
*
* Other Resources:
*
*
* -
*
*
* -
*
*
*
*/
@ThreadSafe
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AmazonSageMakerClient extends AmazonWebServiceClient implements AmazonSageMaker {
/** Provider for AWS credentials. */
private final AWSCredentialsProvider awsCredentialsProvider;
private static final Log log = LogFactory.getLog(AmazonSageMaker.class);
/** Default signing name for the service. */
private static final String DEFAULT_SIGNING_NAME = "sagemaker";
private volatile AmazonSageMakerWaiters waiters;
/** Client configuration factory providing ClientConfigurations tailored to this client */
protected static final ClientConfigurationFactory configFactory = new ClientConfigurationFactory();
private final AdvancedConfig advancedConfig;
private static final com.amazonaws.protocol.json.SdkJsonProtocolFactory protocolFactory = new com.amazonaws.protocol.json.SdkJsonProtocolFactory(
new JsonClientMetadata()
.withProtocolVersion("1.1")
.withSupportsCbor(false)
.withSupportsIon(false)
.addErrorMetadata(
new JsonErrorShapeMetadata().withErrorCode("ResourceInUse").withExceptionUnmarshaller(
com.amazonaws.services.sagemaker.model.transform.ResourceInUseExceptionUnmarshaller.getInstance()))
.addErrorMetadata(
new JsonErrorShapeMetadata().withErrorCode("ConflictException").withExceptionUnmarshaller(
com.amazonaws.services.sagemaker.model.transform.ConflictExceptionUnmarshaller.getInstance()))
.addErrorMetadata(
new JsonErrorShapeMetadata().withErrorCode("ResourceNotFound").withExceptionUnmarshaller(
com.amazonaws.services.sagemaker.model.transform.ResourceNotFoundExceptionUnmarshaller.getInstance()))
.addErrorMetadata(
new JsonErrorShapeMetadata().withErrorCode("ResourceLimitExceeded").withExceptionUnmarshaller(
com.amazonaws.services.sagemaker.model.transform.ResourceLimitExceededExceptionUnmarshaller.getInstance()))
.withBaseServiceExceptionClass(com.amazonaws.services.sagemaker.model.AmazonSageMakerException.class));
public static AmazonSageMakerClientBuilder builder() {
return AmazonSageMakerClientBuilder.standard();
}
/**
* Constructs a new client to invoke service methods on SageMaker using the specified parameters.
*
*
* All service calls made using this new client object are blocking, and will not return until the service call
* completes.
*
* @param clientParams
* Object providing client parameters.
*/
AmazonSageMakerClient(AwsSyncClientParams clientParams) {
this(clientParams, false);
}
/**
* Constructs a new client to invoke service methods on SageMaker using the specified parameters.
*
*
* All service calls made using this new client object are blocking, and will not return until the service call
* completes.
*
* @param clientParams
* Object providing client parameters.
*/
AmazonSageMakerClient(AwsSyncClientParams clientParams, boolean endpointDiscoveryEnabled) {
super(clientParams);
this.awsCredentialsProvider = clientParams.getCredentialsProvider();
this.advancedConfig = clientParams.getAdvancedConfig();
init();
}
private void init() {
setServiceNameIntern(DEFAULT_SIGNING_NAME);
setEndpointPrefix(ENDPOINT_PREFIX);
// calling this.setEndPoint(...) will also modify the signer accordingly
setEndpoint("sagemaker.us-east-1.amazonaws.com");
HandlerChainFactory chainFactory = new HandlerChainFactory();
requestHandler2s.addAll(chainFactory.newRequestHandlerChain("/com/amazonaws/services/sagemaker/request.handlers"));
requestHandler2s.addAll(chainFactory.newRequestHandler2Chain("/com/amazonaws/services/sagemaker/request.handler2s"));
requestHandler2s.addAll(chainFactory.getGlobalHandlers());
}
/**
*
* Creates an association between the source and the destination. A source can be associated with multiple
* destinations, and a destination can be associated with multiple sources. An association is a lineage tracking
* entity. For more information, see Amazon SageMaker ML Lineage
* Tracking.
*
*
* @param addAssociationRequest
* @return Result of the AddAssociation operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.AddAssociation
* @see AWS API
* Documentation
*/
@Override
public AddAssociationResult addAssociation(AddAssociationRequest request) {
request = beforeClientExecution(request);
return executeAddAssociation(request);
}
@SdkInternalApi
final AddAssociationResult executeAddAssociation(AddAssociationRequest addAssociationRequest) {
ExecutionContext executionContext = createExecutionContext(addAssociationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new AddAssociationRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(addAssociationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "AddAssociation");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new AddAssociationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook
* instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams,
* endpoint configurations, and endpoints.
*
*
* Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information
* about tags, see For more information, see AWS Tagging Strategies.
*
*
*
* Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
* hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
* tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
* tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
* first create the tuning job by specifying them in the Tags parameter of
* CreateHyperParameterTuningJob
*
*
*
* @param addTagsRequest
* @return Result of the AddTags operation returned by the service.
* @sample AmazonSageMaker.AddTags
* @see AWS API
* Documentation
*/
@Override
public AddTagsResult addTags(AddTagsRequest request) {
request = beforeClientExecution(request);
return executeAddTags(request);
}
@SdkInternalApi
final AddTagsResult executeAddTags(AddTagsRequest addTagsRequest) {
ExecutionContext executionContext = createExecutionContext(addTagsRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new AddTagsRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(addTagsRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "AddTags");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true).withHasStreamingSuccessResponse(false), new AddTagsResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Associates a trial component with a trial. A trial component can be associated with multiple trials. To
* disassociate a trial component from a trial, call the DisassociateTrialComponent API.
*
*
* @param associateTrialComponentRequest
* @return Result of the AssociateTrialComponent operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.AssociateTrialComponent
* @see AWS API Documentation
*/
@Override
public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request) {
request = beforeClientExecution(request);
return executeAssociateTrialComponent(request);
}
@SdkInternalApi
final AssociateTrialComponentResult executeAssociateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest) {
ExecutionContext executionContext = createExecutionContext(associateTrialComponentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new AssociateTrialComponentRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(associateTrialComponentRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "AssociateTrialComponent");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new AssociateTrialComponentResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an action. An action is a lineage tracking entity that represents an action or activity. For
* example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact.
* For more information, see Amazon
* SageMaker ML Lineage Tracking.
*
*
* @param createActionRequest
* @return Result of the CreateAction operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateAction
* @see AWS API
* Documentation
*/
@Override
public CreateActionResult createAction(CreateActionRequest request) {
request = beforeClientExecution(request);
return executeCreateAction(request);
}
@SdkInternalApi
final CreateActionResult executeCreateAction(CreateActionRequest createActionRequest) {
ExecutionContext executionContext = createExecutionContext(createActionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateActionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createActionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateAction");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateActionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
*
*
* @param createAlgorithmRequest
* @return Result of the CreateAlgorithm operation returned by the service.
* @sample AmazonSageMaker.CreateAlgorithm
* @see AWS API
* Documentation
*/
@Override
public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request) {
request = beforeClientExecution(request);
return executeCreateAlgorithm(request);
}
@SdkInternalApi
final CreateAlgorithmResult executeCreateAlgorithm(CreateAlgorithmRequest createAlgorithmRequest) {
ExecutionContext executionContext = createExecutionContext(createAlgorithmRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateAlgorithmRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createAlgorithmRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateAlgorithm");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateAlgorithmResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This
* operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new
* kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
*
*
* @param createAppRequest
* @return Result of the CreateApp operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateApp
* @see AWS API
* Documentation
*/
@Override
public CreateAppResult createApp(CreateAppRequest request) {
request = beforeClientExecution(request);
return executeCreateApp(request);
}
@SdkInternalApi
final CreateAppResult executeCreateApp(CreateAppRequest createAppRequest) {
ExecutionContext executionContext = createExecutionContext(createAppRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateAppRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createAppRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateApp");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateAppResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the
* Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
*
*
* @param createAppImageConfigRequest
* @return Result of the CreateAppImageConfig operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateAppImageConfig
* @see AWS
* API Documentation
*/
@Override
public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request) {
request = beforeClientExecution(request);
return executeCreateAppImageConfig(request);
}
@SdkInternalApi
final CreateAppImageConfigResult executeCreateAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest) {
ExecutionContext executionContext = createExecutionContext(createAppImageConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateAppImageConfigRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createAppImageConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateAppImageConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateAppImageConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or
* data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see
* Amazon SageMaker ML Lineage
* Tracking.
*
*
* @param createArtifactRequest
* @return Result of the CreateArtifact operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateArtifact
* @see AWS API
* Documentation
*/
@Override
public CreateArtifactResult createArtifact(CreateArtifactRequest request) {
request = beforeClientExecution(request);
return executeCreateArtifact(request);
}
@SdkInternalApi
final CreateArtifactResult executeCreateArtifact(CreateArtifactRequest createArtifactRequest) {
ExecutionContext executionContext = createExecutionContext(createArtifactRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateArtifactRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createArtifactRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateArtifact");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateArtifactResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an Autopilot job.
*
*
* Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the
* steps described in Step 6.1:
* Deploy the Model to Amazon SageMaker Hosting Services.
*
*
* For information about how to use Autopilot, see Automate Model
* Development with Amazon SageMaker Autopilot.
*
*
* @param createAutoMLJobRequest
* @return Result of the CreateAutoMLJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateAutoMLJob
* @see AWS API
* Documentation
*/
@Override
public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request) {
request = beforeClientExecution(request);
return executeCreateAutoMLJob(request);
}
@SdkInternalApi
final CreateAutoMLJobResult executeCreateAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest) {
ExecutionContext executionContext = createExecutionContext(createAutoMLJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateAutoMLJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createAutoMLJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateAutoMLJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateAutoMLJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with
* notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a
* resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it
* persists independently from the lifecycle of any notebook instances it is associated with.
*
*
* The repository can be hosted either in AWS CodeCommit or in any other
* Git repository.
*
*
* @param createCodeRepositoryRequest
* @return Result of the CreateCodeRepository operation returned by the service.
* @sample AmazonSageMaker.CreateCodeRepository
* @see AWS
* API Documentation
*/
@Override
public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request) {
request = beforeClientExecution(request);
return executeCreateCodeRepository(request);
}
@SdkInternalApi
final CreateCodeRepositoryResult executeCreateCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest) {
ExecutionContext executionContext = createExecutionContext(createCodeRepositoryRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateCodeRepositoryRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createCodeRepositoryRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateCodeRepository");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateCodeRepositoryResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model
* artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
*
*
* If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model
* artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them
* as an ML resource.
*
*
* In the request body, you provide the following:
*
*
* -
*
* A name for the compilation job
*
*
* -
*
* Information about the input model artifacts
*
*
* -
*
* The output location for the compiled model and the device (target) that the model runs on
*
*
* -
*
* The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation
* job.
*
*
*
*
* You can also provide a Tag to track the model compilation job's resource use and costs. The response
* body contains the CompilationJobArn for the compiled job.
*
*
* To stop a model compilation job, use StopCompilationJob. To get information about a particular model
* compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use
* ListCompilationJobs.
*
*
* @param createCompilationJobRequest
* @return Result of the CreateCompilationJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateCompilationJob
* @see AWS
* API Documentation
*/
@Override
public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request) {
request = beforeClientExecution(request);
return executeCreateCompilationJob(request);
}
@SdkInternalApi
final CreateCompilationJobResult executeCreateCompilationJob(CreateCompilationJobRequest createCompilationJobRequest) {
ExecutionContext executionContext = createExecutionContext(createCompilationJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateCompilationJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createCompilationJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateCompilationJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateCompilationJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a context. A context is a lineage tracking entity that represents a logical grouping of other
* tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage
* Tracking.
*
*
* @param createContextRequest
* @return Result of the CreateContext operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateContext
* @see AWS API
* Documentation
*/
@Override
public CreateContextResult createContext(CreateContextRequest request) {
request = beforeClientExecution(request);
return executeCreateContext(request);
}
@SdkInternalApi
final CreateContextResult executeCreateContext(CreateContextRequest createContextRequest) {
ExecutionContext executionContext = createExecutionContext(createContextRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateContextRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createContextRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateContext");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateContextResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
*
*
* @param createDataQualityJobDefinitionRequest
* @return Result of the CreateDataQualityJobDefinition operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateDataQualityJobDefinition
* @see AWS API Documentation
*/
@Override
public CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeCreateDataQualityJobDefinition(request);
}
@SdkInternalApi
final CreateDataQualityJobDefinitionResult executeCreateDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(createDataQualityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDataQualityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createDataQualityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateDataQualityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateDataQualityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a device fleet.
*
*
* @param createDeviceFleetRequest
* @return Result of the CreateDeviceFleet operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateDeviceFleet
* @see AWS
* API Documentation
*/
@Override
public CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request) {
request = beforeClientExecution(request);
return executeCreateDeviceFleet(request);
}
@SdkInternalApi
final CreateDeviceFleetResult executeCreateDeviceFleet(CreateDeviceFleetRequest createDeviceFleetRequest) {
ExecutionContext executionContext = createExecutionContext(createDeviceFleetRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDeviceFleetRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createDeviceFleetRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateDeviceFleet");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateDeviceFleetResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic
* File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon
* Virtual Private Cloud (VPC) configurations. An AWS account is limited to one domain per region. Users within a
* domain can share notebook files and other artifacts with each other.
*
*
* EFS storage
*
*
* When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user
* receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
*
*
* SageMaker uses the AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an
* AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For
* more information, see Protect
* Data at Rest Using Encryption.
*
*
* VPC configuration
*
*
* All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For
* other Studio traffic, you can specify the AppNetworkAccessType parameter.
* AppNetworkAccessType corresponds to the network access type that you choose when you onboard to
* Studio. The following options are available:
*
*
* -
*
* PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows
* internet access. This is the default value.
*
*
* -
*
* VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled
* by default. To allow internet access, you must specify a NAT gateway.
*
*
* When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless
* your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups
* allow outbound connections.
*
*
*
*
* For more information, see Connect
* SageMaker Studio Notebooks to Resources in a VPC.
*
*
* @param createDomainRequest
* @return Result of the CreateDomain operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateDomain
* @see AWS API
* Documentation
*/
@Override
public CreateDomainResult createDomain(CreateDomainRequest request) {
request = beforeClientExecution(request);
return executeCreateDomain(request);
}
@SdkInternalApi
final CreateDomainResult executeCreateDomain(CreateDomainRequest createDomainRequest) {
ExecutionContext executionContext = createExecutionContext(createDomainRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDomainRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createDomainRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateDomain");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateDomainResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon
* Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the
* resulting artifacts to an S3 bucket that you specify.
*
*
* @param createEdgePackagingJobRequest
* @return Result of the CreateEdgePackagingJob operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateEdgePackagingJob
* @see AWS API Documentation
*/
@Override
public CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request) {
request = beforeClientExecution(request);
return executeCreateEdgePackagingJob(request);
}
@SdkInternalApi
final CreateEdgePackagingJobResult executeCreateEdgePackagingJob(CreateEdgePackagingJobRequest createEdgePackagingJobRequest) {
ExecutionContext executionContext = createExecutionContext(createEdgePackagingJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateEdgePackagingJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createEdgePackagingJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateEdgePackagingJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateEdgePackagingJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint
* to provision resources and deploy models. You create the endpoint configuration with the
* CreateEndpointConfig API.
*
*
* Use this API to deploy models using Amazon SageMaker hosting services.
*
*
* For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the
* Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
*
*
*
* You must not delete an EndpointConfig that is in use by an endpoint that is live or while the
* UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To
* update an endpoint, you must create a new EndpointConfig.
*
*
*
* The endpoint name must be unique within an AWS Region in your AWS account.
*
*
* When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute
* instances), and deploys the model(s) on them.
*
*
*
* When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
* exists. When you read data from a DynamoDB table supporting
* Eventually Consistent Reads , the response might not reflect the results of a recently completed
* write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
* this causes a validation error. If you repeat your read request after a short time, the response should return
* the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
* call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
* DynamoDB eventually consistent read.
*
*
*
* When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it
* creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming
* requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
*
*
* If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS
* Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your
* IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS
* for that region. For more information, see Activating and
* Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search
* the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API
* operations, add the following policies to the role.
*
*
* -
*
* Option 1: For a full Amazon SageMaker access, search and attach the AmazonSageMakerFullAccess
* policy.
*
*
* -
*
* Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the
* JSON file of the IAM role:
*
*
* "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
*
*
* "Resource": [
*
*
* "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
*
*
* "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
*
*
* ]
*
*
* For more information, see Amazon SageMaker API
* Permissions: Actions, Permissions, and Resources Reference.
*
*
*
*
*
* @param createEndpointRequest
* @return Result of the CreateEndpoint operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateEndpoint
* @see AWS API
* Documentation
*/
@Override
public CreateEndpointResult createEndpoint(CreateEndpointRequest request) {
request = beforeClientExecution(request);
return executeCreateEndpoint(request);
}
@SdkInternalApi
final CreateEndpointResult executeCreateEndpoint(CreateEndpointRequest createEndpointRequest) {
ExecutionContext executionContext = createExecutionContext(createEndpointRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateEndpointRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createEndpointRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateEndpoint");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateEndpointResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
* configuration, you identify one or more models, created using the CreateModel API, to deploy and the
* resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
*
*
*
* Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
*
*
*
* In the request, you define a ProductionVariant, for each model that you want to deploy. Each
* ProductionVariant parameter also describes the resources that you want Amazon SageMaker to
* provision. This includes the number and type of ML compute instances to deploy.
*
*
* If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
* want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
* traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
* A, and one-third to model B.
*
*
* For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the
* Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
*
*
*
* When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
* exists. When you read data from a DynamoDB table supporting
* Eventually Consistent Reads , the response might not reflect the results of a recently completed
* write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
* this causes a validation error. If you repeat your read request after a short time, the response should return
* the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
* call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
* DynamoDB eventually consistent read.
*
*
*
* @param createEndpointConfigRequest
* @return Result of the CreateEndpointConfig operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateEndpointConfig
* @see AWS
* API Documentation
*/
@Override
public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request) {
request = beforeClientExecution(request);
return executeCreateEndpointConfig(request);
}
@SdkInternalApi
final CreateEndpointConfigResult executeCreateEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest) {
ExecutionContext executionContext = createExecutionContext(createEndpointConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateEndpointConfigRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createEndpointConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateEndpointConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateEndpointConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an SageMaker experiment. An experiment is a collection of trials that are observed,
* compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a
* machine learning model.
*
*
* The goal of an experiment is to determine the components that produce the best model. Multiple trials are
* performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the
* remaining inputs constant.
*
*
* When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial
* components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must
* use the logging APIs provided by the SDK.
*
*
* You can add tags to experiments, trials, trial components and then use the Search API to search for the
* tags.
*
*
* To add a description to an experiment, specify the optional Description parameter. To add a
* description later, or to change the description, call the UpdateExperiment API.
*
*
* To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties,
* call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the
* ListTrials API. To create a trial call the CreateTrial API.
*
*
* @param createExperimentRequest
* @return Result of the CreateExperiment operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateExperiment
* @see AWS API
* Documentation
*/
@Override
public CreateExperimentResult createExperiment(CreateExperimentRequest request) {
request = beforeClientExecution(request);
return executeCreateExperiment(request);
}
@SdkInternalApi
final CreateExperimentResult executeCreateExperiment(CreateExperimentRequest createExperimentRequest) {
ExecutionContext executionContext = createExecutionContext(createExperimentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateExperimentRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createExperimentRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateExperiment");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateExperimentResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Create a new FeatureGroup. A FeatureGroup is a group of Features defined
* in the FeatureStore to describe a Record.
*
*
* The FeatureGroup defines the schema and features contained in the FeatureGroup. A
* FeatureGroup definition is composed of a list of Features, a
* RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its
* OnlineStore and OfflineStore. Check AWS service quotas to see the
* FeatureGroups quota for your AWS account.
*
*
*
* You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a
* FeatureGroup.
*
*
*
* @param createFeatureGroupRequest
* @return Result of the CreateFeatureGroup operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateFeatureGroup
* @see AWS
* API Documentation
*/
@Override
public CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest request) {
request = beforeClientExecution(request);
return executeCreateFeatureGroup(request);
}
@SdkInternalApi
final CreateFeatureGroupResult executeCreateFeatureGroup(CreateFeatureGroupRequest createFeatureGroupRequest) {
ExecutionContext executionContext = createExecutionContext(createFeatureGroupRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateFeatureGroupRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createFeatureGroupRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateFeatureGroup");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateFeatureGroupResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a flow definition.
*
*
* @param createFlowDefinitionRequest
* @return Result of the CreateFlowDefinition operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateFlowDefinition
* @see AWS
* API Documentation
*/
@Override
public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request) {
request = beforeClientExecution(request);
return executeCreateFlowDefinition(request);
}
@SdkInternalApi
final CreateFlowDefinitionResult executeCreateFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(createFlowDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateFlowDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createFlowDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateFlowDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateFlowDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel
* interface with an instruction area, the item to review, and an input area.
*
*
* @param createHumanTaskUiRequest
* @return Result of the CreateHumanTaskUi operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateHumanTaskUi
* @see AWS
* API Documentation
*/
@Override
public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request) {
request = beforeClientExecution(request);
return executeCreateHumanTaskUi(request);
}
@SdkInternalApi
final CreateHumanTaskUiResult executeCreateHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest) {
ExecutionContext executionContext = createExecutionContext(createHumanTaskUiRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateHumanTaskUiRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createHumanTaskUiRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateHumanTaskUi");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateHumanTaskUiResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many
* training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that
* you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured
* by an objective metric that you choose.
*
*
* @param createHyperParameterTuningJobRequest
* @return Result of the CreateHyperParameterTuningJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateHyperParameterTuningJob
* @see AWS API Documentation
*/
@Override
public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request) {
request = beforeClientExecution(request);
return executeCreateHyperParameterTuningJob(request);
}
@SdkInternalApi
final CreateHyperParameterTuningJobResult executeCreateHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest) {
ExecutionContext executionContext = createExecutionContext(createHyperParameterTuningJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateHyperParameterTuningJobRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createHyperParameterTuningJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateHyperParameterTuningJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateHyperParameterTuningJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a
* container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
*
*
* @param createImageRequest
* @return Result of the CreateImage operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateImage
* @see AWS API
* Documentation
*/
@Override
public CreateImageResult createImage(CreateImageRequest request) {
request = beforeClientExecution(request);
return executeCreateImage(request);
}
@SdkInternalApi
final CreateImageResult executeCreateImage(CreateImageRequest createImageRequest) {
ExecutionContext executionContext = createExecutionContext(createImageRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateImageRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createImageRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateImage");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateImageResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon
* Container Registry (ECR) container image specified by BaseImage.
*
*
* @param createImageVersionRequest
* @return Result of the CreateImageVersion operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreateImageVersion
* @see AWS
* API Documentation
*/
@Override
public CreateImageVersionResult createImageVersion(CreateImageVersionRequest request) {
request = beforeClientExecution(request);
return executeCreateImageVersion(request);
}
@SdkInternalApi
final CreateImageVersionResult executeCreateImageVersion(CreateImageVersionRequest createImageVersionRequest) {
ExecutionContext executionContext = createExecutionContext(createImageVersionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateImageVersionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createImageVersionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateImageVersion");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateImageVersionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to
* train machine learning models.
*
*
* You can select your workforce from one of three providers:
*
*
* -
*
* A private workforce that you create. It can include employees, contractors, and outside experts. Use a private
* workforce when want the data to stay within your organization or when a specific set of skills is required.
*
*
* -
*
* One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
*
*
* -
*
* The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data
* or data that has been stripped of any personally identifiable information.
*
*
*
*
* You can also use automated data labeling to reduce the number of data objects that need to be labeled by a
* human. Automated data labeling uses active learning to determine if a data object can be labeled by
* machine or if it needs to be sent to a human worker. For more information, see Using Automated Data
* Labeling.
*
*
* The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that
* describes the location of each object. For more information, see Using Input and Output Data.
*
*
* The output can be used as the manifest file for another labeling job or as training data for your machine
* learning models.
*
*
* @param createLabelingJobRequest
* @return Result of the CreateLabelingJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateLabelingJob
* @see AWS
* API Documentation
*/
@Override
public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request) {
request = beforeClientExecution(request);
return executeCreateLabelingJob(request);
}
@SdkInternalApi
final CreateLabelingJobResult executeCreateLabelingJob(CreateLabelingJobRequest createLabelingJobRequest) {
ExecutionContext executionContext = createExecutionContext(createLabelingJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateLabelingJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createLabelingJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateLabelingJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateLabelingJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the
* primary container, you specify the Docker image that contains inference code, artifacts (from prior training),
* and a custom environment map that the inference code uses when you deploy the model for predictions.
*
*
* Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
*
*
* To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
* create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers
* that you defined for the model in the hosting environment.
*
*
* For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the
* Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
*
*
* To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon
* SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
*
*
* In the CreateModel request, you must define a container with the PrimaryContainer
* parameter.
*
*
* In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and
* docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also
* use the IAM role to manage permissions the inference code needs. For example, if the inference code access any
* other AWS resources, you grant necessary permissions via this role.
*
*
* @param createModelRequest
* @return Result of the CreateModel operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateModel
* @see AWS API
* Documentation
*/
@Override
public CreateModelResult createModel(CreateModelRequest request) {
request = beforeClientExecution(request);
return executeCreateModel(request);
}
@SdkInternalApi
final CreateModelResult executeCreateModel(CreateModelRequest createModelRequest) {
ExecutionContext executionContext = createExecutionContext(createModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModel");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates the definition for a model bias job.
*
*
* @param createModelBiasJobDefinitionRequest
* @return Result of the CreateModelBiasJobDefinition operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateModelBiasJobDefinition
* @see AWS API Documentation
*/
@Override
public CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeCreateModelBiasJobDefinition(request);
}
@SdkInternalApi
final CreateModelBiasJobDefinitionResult executeCreateModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(createModelBiasJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelBiasJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createModelBiasJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModelBiasJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateModelBiasJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates the definition for a model explainability job.
*
*
* @param createModelExplainabilityJobDefinitionRequest
* @return Result of the CreateModelExplainabilityJobDefinition operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
@Override
public CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeCreateModelExplainabilityJobDefinition(request);
}
@SdkInternalApi
final CreateModelExplainabilityJobDefinitionResult executeCreateModelExplainabilityJobDefinition(
CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(createModelExplainabilityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelExplainabilityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createModelExplainabilityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModelExplainabilityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateModelExplainabilityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a
* versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace
* to create models in Amazon SageMaker.
*
*
* To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
* location of your model artifacts, provide values for InferenceSpecification. To create a model from
* an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for
* SourceAlgorithmSpecification.
*
*
*
* There are two types of model packages:
*
*
* -
*
* Versioned - a model that is part of a model group in the model registry.
*
*
* -
*
* Unversioned - a model package that is not part of a model group.
*
*
*
*
*
* @param createModelPackageRequest
* @return Result of the CreateModelPackage operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify an experiment, trial, or trial component.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateModelPackage
* @see AWS
* API Documentation
*/
@Override
public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request) {
request = beforeClientExecution(request);
return executeCreateModelPackage(request);
}
@SdkInternalApi
final CreateModelPackageResult executeCreateModelPackage(CreateModelPackageRequest createModelPackageRequest) {
ExecutionContext executionContext = createExecutionContext(createModelPackageRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelPackageRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createModelPackageRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModelPackage");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateModelPackageResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a model group. A model group contains a group of model versions.
*
*
* @param createModelPackageGroupRequest
* @return Result of the CreateModelPackageGroup operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateModelPackageGroup
* @see AWS API Documentation
*/
@Override
public CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request) {
request = beforeClientExecution(request);
return executeCreateModelPackageGroup(request);
}
@SdkInternalApi
final CreateModelPackageGroupResult executeCreateModelPackageGroup(CreateModelPackageGroupRequest createModelPackageGroupRequest) {
ExecutionContext executionContext = createExecutionContext(createModelPackageGroupRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelPackageGroupRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createModelPackageGroupRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModelPackageGroup");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateModelPackageGroupResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
*
*
* @param createModelQualityJobDefinitionRequest
* @return Result of the CreateModelQualityJobDefinition operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateModelQualityJobDefinition
* @see AWS API Documentation
*/
@Override
public CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeCreateModelQualityJobDefinition(request);
}
@SdkInternalApi
final CreateModelQualityJobDefinitionResult executeCreateModelQualityJobDefinition(
CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(createModelQualityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateModelQualityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createModelQualityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateModelQualityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateModelQualityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an
* Amazon SageMaker Endoint.
*
*
* @param createMonitoringScheduleRequest
* @return Result of the CreateMonitoringSchedule operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateMonitoringSchedule
* @see AWS API Documentation
*/
@Override
public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request) {
request = beforeClientExecution(request);
return executeCreateMonitoringSchedule(request);
}
@SdkInternalApi
final CreateMonitoringScheduleResult executeCreateMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest) {
ExecutionContext executionContext = createExecutionContext(createMonitoringScheduleRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateMonitoringScheduleRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createMonitoringScheduleRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateMonitoringSchedule");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateMonitoringScheduleResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance
* running on a Jupyter notebook.
*
*
* In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run.
* Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model
* training, and attaches an ML storage volume to the notebook instance.
*
*
* Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker
* with a specific algorithm or with a machine learning framework.
*
*
* After receiving the request, Amazon SageMaker does the following:
*
*
* -
*
* Creates a network interface in the Amazon SageMaker VPC.
*
*
* -
*
* (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC,
* which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon
* SageMaker attaches the security group that you specified in the request to the network interface that it creates
* in your VPC.
*
*
* -
*
* Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
* SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this
* instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
* groups allow it.
*
*
*
*
* After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change
* the name of a notebook instance after you create it.
*
*
* After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter
* notebooks. For example, you can write code to explore a dataset that you can use for model training, train a
* model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
*
*
* For more information, see How It
* Works.
*
*
* @param createNotebookInstanceRequest
* @return Result of the CreateNotebookInstance operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateNotebookInstance
* @see AWS API Documentation
*/
@Override
public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request) {
request = beforeClientExecution(request);
return executeCreateNotebookInstance(request);
}
@SdkInternalApi
final CreateNotebookInstanceResult executeCreateNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest) {
ExecutionContext executionContext = createExecutionContext(createNotebookInstanceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateNotebookInstanceRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createNotebookInstanceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateNotebookInstance");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateNotebookInstanceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle
* configuration is a collection of shell scripts that run when you create or start a notebook instance.
*
*
* Each lifecycle configuration script has a limit of 16384 characters.
*
*
* The value of the $PATH environment variable that is available to both scripts is
* /sbin:bin:/usr/sbin:/usr/bin.
*
*
* View CloudWatch Logs for notebook instance lifecycle configurations in log group
* /aws/sagemaker/NotebookInstances in log stream
* [notebook-instance-name]/[LifecycleConfigHook].
*
*
* Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes,
* it fails and the notebook instance is not created or started.
*
*
* For information about notebook instance lifestyle configurations, see Step 2.1: (Optional)
* Customize a Notebook Instance.
*
*
* @param createNotebookInstanceLifecycleConfigRequest
* @return Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
@Override
public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request) {
request = beforeClientExecution(request);
return executeCreateNotebookInstanceLifecycleConfig(request);
}
@SdkInternalApi
final CreateNotebookInstanceLifecycleConfigResult executeCreateNotebookInstanceLifecycleConfig(
CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest) {
ExecutionContext executionContext = createExecutionContext(createNotebookInstanceLifecycleConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateNotebookInstanceLifecycleConfigRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createNotebookInstanceLifecycleConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateNotebookInstanceLifecycleConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreateNotebookInstanceLifecycleConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a pipeline using a JSON pipeline definition.
*
*
* @param createPipelineRequest
* @return Result of the CreatePipeline operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreatePipeline
* @see AWS API
* Documentation
*/
@Override
public CreatePipelineResult createPipeline(CreatePipelineRequest request) {
request = beforeClientExecution(request);
return executeCreatePipeline(request);
}
@SdkInternalApi
final CreatePipelineResult executeCreatePipeline(CreatePipelineRequest createPipelineRequest) {
ExecutionContext executionContext = createExecutionContext(createPipelineRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreatePipelineRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createPipelineRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreatePipeline");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreatePipelineResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be
* automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated
* with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the
* authentication mode equals IAM.
*
*
*
* The URL that you get from a call to CreatePresignedDomainUrl is valid only for 5 minutes. If you try
* to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
*
*
*
* @param createPresignedDomainUrlRequest
* @return Result of the CreatePresignedDomainUrl operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreatePresignedDomainUrl
* @see AWS API Documentation
*/
@Override
public CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request) {
request = beforeClientExecution(request);
return executeCreatePresignedDomainUrl(request);
}
@SdkInternalApi
final CreatePresignedDomainUrlResult executeCreatePresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest) {
ExecutionContext executionContext = createExecutionContext(createPresignedDomainUrlRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreatePresignedDomainUrlRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createPresignedDomainUrlRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreatePresignedDomainUrl");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreatePresignedDomainUrlResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
* console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing
* the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
* page.
*
*
* The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the
* presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for
* this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook
* instance.
*
*
* You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
* Use the NotIpAddress condition operator and the aws:SourceIP condition context key to
* specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
* Limit Access to a Notebook Instance by IP Address.
*
*
*
* The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you
* try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
*
*
*
* @param createPresignedNotebookInstanceUrlRequest
* @return Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
* @sample AmazonSageMaker.CreatePresignedNotebookInstanceUrl
* @see AWS API Documentation
*/
@Override
public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request) {
request = beforeClientExecution(request);
return executeCreatePresignedNotebookInstanceUrl(request);
}
@SdkInternalApi
final CreatePresignedNotebookInstanceUrlResult executeCreatePresignedNotebookInstanceUrl(
CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest) {
ExecutionContext executionContext = createExecutionContext(createPresignedNotebookInstanceUrlRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreatePresignedNotebookInstanceUrlRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(createPresignedNotebookInstanceUrlRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreatePresignedNotebookInstanceUrl");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new CreatePresignedNotebookInstanceUrlResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a processing job.
*
*
* @param createProcessingJobRequest
* @return Result of the CreateProcessingJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreateProcessingJob
* @see AWS
* API Documentation
*/
@Override
public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request) {
request = beforeClientExecution(request);
return executeCreateProcessingJob(request);
}
@SdkInternalApi
final CreateProcessingJobResult executeCreateProcessingJob(CreateProcessingJobRequest createProcessingJobRequest) {
ExecutionContext executionContext = createExecutionContext(createProcessingJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateProcessingJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createProcessingJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateProcessingJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateProcessingJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from
* training to deploying an approved model.
*
*
* @param createProjectRequest
* @return Result of the CreateProject operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateProject
* @see AWS API
* Documentation
*/
@Override
public CreateProjectResult createProject(CreateProjectRequest request) {
request = beforeClientExecution(request);
return executeCreateProject(request);
}
@SdkInternalApi
final CreateProjectResult executeCreateProject(CreateProjectRequest createProjectRequest) {
ExecutionContext executionContext = createExecutionContext(createProjectRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateProjectRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createProjectRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateProject");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateProjectResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an
* Amazon S3 location that you specify.
*
*
* If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model
* artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon
* SageMaker, provided that you know how to use them for inference.
*
*
* In the request body, you provide the following:
*
*
* -
*
* AlgorithmSpecification - Identifies the training algorithm to use.
*
*
* -
*
* HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model
* parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
* hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
*
*
* -
*
* InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is
* stored.
*
*
* -
*
* OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the
* results of model training.
*
*
* -
*
* ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
* for model training. In distributed training, you specify more than one instance.
*
*
* -
*
* EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by
* using Amazon EC2 Spot instances. For more information, see Managed Spot
* Training.
*
*
* -
*
* RoleArn - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your
* behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
* successfully complete model training.
*
*
* -
*
* StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time
* limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a
* managed spot training job to complete.
*
*
*
*
* For more information about Amazon SageMaker, see How It Works.
*
*
* @param createTrainingJobRequest
* @return Result of the CreateTrainingJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreateTrainingJob
* @see AWS
* API Documentation
*/
@Override
public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request) {
request = beforeClientExecution(request);
return executeCreateTrainingJob(request);
}
@SdkInternalApi
final CreateTrainingJobResult executeCreateTrainingJob(CreateTrainingJobRequest createTrainingJobRequest) {
ExecutionContext executionContext = createExecutionContext(createTrainingJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateTrainingJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createTrainingJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateTrainingJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateTrainingJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these
* results to an Amazon S3 location that you specify.
*
*
* To perform batch transformations, you create a transform job and use the data that you have readily available.
*
*
* In the request body, you provide the following:
*
*
* -
*
* TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an
* AWS account.
*
*
* -
*
* ModelName - Identifies the model to use. ModelName must be the name of an existing
* Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
* CreateModel.
*
*
* -
*
* TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is
* stored.
*
*
* -
*
* TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
* results from the transform job.
*
*
* -
*
* TransformResources - Identifies the ML compute instances for the transform job.
*
*
*
*
* For more information about how batch transformation works, see Batch Transform.
*
*
* @param createTransformJobRequest
* @return Result of the CreateTransformJob operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.CreateTransformJob
* @see AWS
* API Documentation
*/
@Override
public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request) {
request = beforeClientExecution(request);
return executeCreateTransformJob(request);
}
@SdkInternalApi
final CreateTransformJobResult executeCreateTransformJob(CreateTransformJobRequest createTransformJobRequest) {
ExecutionContext executionContext = createExecutionContext(createTransformJobRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateTransformJobRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createTransformJobRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateTransformJob");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateTransformJobResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a
* machine learning model. A trial is part of a single Amazon SageMaker experiment.
*
*
* When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial
* components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must
* use the logging APIs provided by the SDK.
*
*
* You can add tags to a trial and then use the Search API to search for the tags.
*
*
* To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the
* DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
*
*
* @param createTrialRequest
* @return Result of the CreateTrial operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateTrial
* @see AWS API
* Documentation
*/
@Override
public CreateTrialResult createTrial(CreateTrialRequest request) {
request = beforeClientExecution(request);
return executeCreateTrial(request);
}
@SdkInternalApi
final CreateTrialResult executeCreateTrial(CreateTrialRequest createTrialRequest) {
ExecutionContext executionContext = createExecutionContext(createTrialRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateTrialRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createTrialRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateTrial");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateTrialResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one
* or more trial components. A trial component can be used in multiple trials.
*
*
* Trial components include pre-processing jobs, training jobs, and batch transform jobs.
*
*
* When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial
* components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must
* use the logging APIs provided by the SDK.
*
*
* You can add tags to a trial component and then use the Search API to search for the tags.
*
*
*
* CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This
* includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
* to CreateTrialComponent from outside one of these environments results in an error.
*
*
*
* @param createTrialComponentRequest
* @return Result of the CreateTrialComponent operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateTrialComponent
* @see AWS
* API Documentation
*/
@Override
public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request) {
request = beforeClientExecution(request);
return executeCreateTrialComponent(request);
}
@SdkInternalApi
final CreateTrialComponentResult executeCreateTrialComponent(CreateTrialComponentRequest createTrialComponentRequest) {
ExecutionContext executionContext = createExecutionContext(createTrialComponentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateTrialComponentRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createTrialComponentRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateTrialComponent");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateTrialComponentResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference
* a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when
* a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from
* SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual
* user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
*
*
* @param createUserProfileRequest
* @return Result of the CreateUserProfile operation returned by the service.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.CreateUserProfile
* @see AWS
* API Documentation
*/
@Override
public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request) {
request = beforeClientExecution(request);
return executeCreateUserProfile(request);
}
@SdkInternalApi
final CreateUserProfileResult executeCreateUserProfile(CreateUserProfileRequest createUserProfileRequest) {
ExecutionContext executionContext = createExecutionContext(createUserProfileRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateUserProfileRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createUserProfileRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateUserProfile");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateUserProfileResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Use this operation to create a workforce. This operation will return an error if a workforce already exists in
* the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
*
*
* If you want to create a new workforce in an AWS Region where a workforce already exists, use the API operation to
* delete the existing workforce and then use CreateWorkforce to create a new workforce.
*
*
* To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
* CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
* For more information, see Create a Private
* Workforce (Amazon Cognito).
*
*
* To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
* OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and
* Amazon A2I to create work teams. For more information, see Create a Private
* Workforce (OIDC IdP).
*
*
* @param createWorkforceRequest
* @return Result of the CreateWorkforce operation returned by the service.
* @sample AmazonSageMaker.CreateWorkforce
* @see AWS API
* Documentation
*/
@Override
public CreateWorkforceResult createWorkforce(CreateWorkforceRequest request) {
request = beforeClientExecution(request);
return executeCreateWorkforce(request);
}
@SdkInternalApi
final CreateWorkforceResult executeCreateWorkforce(CreateWorkforceRequest createWorkforceRequest) {
ExecutionContext executionContext = createExecutionContext(createWorkforceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateWorkforceRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createWorkforceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateWorkforce");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateWorkforceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools.
* You must first create the user pools before you can create a work team.
*
*
* You cannot create more than 25 work teams in an account and region.
*
*
* @param createWorkteamRequest
* @return Result of the CreateWorkteam operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceLimitExceededException
* You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
* created.
* @sample AmazonSageMaker.CreateWorkteam
* @see AWS API
* Documentation
*/
@Override
public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request) {
request = beforeClientExecution(request);
return executeCreateWorkteam(request);
}
@SdkInternalApi
final CreateWorkteamResult executeCreateWorkteam(CreateWorkteamRequest createWorkteamRequest) {
ExecutionContext executionContext = createExecutionContext(createWorkteamRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateWorkteamRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(createWorkteamRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "CreateWorkteam");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new CreateWorkteamResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an action.
*
*
* @param deleteActionRequest
* @return Result of the DeleteAction operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteAction
* @see AWS API
* Documentation
*/
@Override
public DeleteActionResult deleteAction(DeleteActionRequest request) {
request = beforeClientExecution(request);
return executeDeleteAction(request);
}
@SdkInternalApi
final DeleteActionResult executeDeleteAction(DeleteActionRequest deleteActionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteActionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteActionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteActionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteAction");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteActionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Removes the specified algorithm from your account.
*
*
* @param deleteAlgorithmRequest
* @return Result of the DeleteAlgorithm operation returned by the service.
* @sample AmazonSageMaker.DeleteAlgorithm
* @see AWS API
* Documentation
*/
@Override
public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request) {
request = beforeClientExecution(request);
return executeDeleteAlgorithm(request);
}
@SdkInternalApi
final DeleteAlgorithmResult executeDeleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest) {
ExecutionContext executionContext = createExecutionContext(deleteAlgorithmRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteAlgorithmRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteAlgorithmRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteAlgorithm");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteAlgorithmResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Used to stop and delete an app.
*
*
* @param deleteAppRequest
* @return Result of the DeleteApp operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteApp
* @see AWS API
* Documentation
*/
@Override
public DeleteAppResult deleteApp(DeleteAppRequest request) {
request = beforeClientExecution(request);
return executeDeleteApp(request);
}
@SdkInternalApi
final DeleteAppResult executeDeleteApp(DeleteAppRequest deleteAppRequest) {
ExecutionContext executionContext = createExecutionContext(deleteAppRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteAppRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteAppRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteApp");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteAppResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an AppImageConfig.
*
*
* @param deleteAppImageConfigRequest
* @return Result of the DeleteAppImageConfig operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteAppImageConfig
* @see AWS
* API Documentation
*/
@Override
public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request) {
request = beforeClientExecution(request);
return executeDeleteAppImageConfig(request);
}
@SdkInternalApi
final DeleteAppImageConfigResult executeDeleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest) {
ExecutionContext executionContext = createExecutionContext(deleteAppImageConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteAppImageConfigRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteAppImageConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteAppImageConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteAppImageConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an artifact. Either ArtifactArn or Source must be specified.
*
*
* @param deleteArtifactRequest
* @return Result of the DeleteArtifact operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteArtifact
* @see AWS API
* Documentation
*/
@Override
public DeleteArtifactResult deleteArtifact(DeleteArtifactRequest request) {
request = beforeClientExecution(request);
return executeDeleteArtifact(request);
}
@SdkInternalApi
final DeleteArtifactResult executeDeleteArtifact(DeleteArtifactRequest deleteArtifactRequest) {
ExecutionContext executionContext = createExecutionContext(deleteArtifactRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteArtifactRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteArtifactRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteArtifact");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteArtifactResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an association.
*
*
* @param deleteAssociationRequest
* @return Result of the DeleteAssociation operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteAssociation
* @see AWS
* API Documentation
*/
@Override
public DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request) {
request = beforeClientExecution(request);
return executeDeleteAssociation(request);
}
@SdkInternalApi
final DeleteAssociationResult executeDeleteAssociation(DeleteAssociationRequest deleteAssociationRequest) {
ExecutionContext executionContext = createExecutionContext(deleteAssociationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteAssociationRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteAssociationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteAssociation");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteAssociationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified Git repository from your account.
*
*
* @param deleteCodeRepositoryRequest
* @return Result of the DeleteCodeRepository operation returned by the service.
* @sample AmazonSageMaker.DeleteCodeRepository
* @see AWS
* API Documentation
*/
@Override
public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request) {
request = beforeClientExecution(request);
return executeDeleteCodeRepository(request);
}
@SdkInternalApi
final DeleteCodeRepositoryResult executeDeleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest) {
ExecutionContext executionContext = createExecutionContext(deleteCodeRepositoryRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteCodeRepositoryRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteCodeRepositoryRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteCodeRepository");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteCodeRepositoryResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an context.
*
*
* @param deleteContextRequest
* @return Result of the DeleteContext operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteContext
* @see AWS API
* Documentation
*/
@Override
public DeleteContextResult deleteContext(DeleteContextRequest request) {
request = beforeClientExecution(request);
return executeDeleteContext(request);
}
@SdkInternalApi
final DeleteContextResult executeDeleteContext(DeleteContextRequest deleteContextRequest) {
ExecutionContext executionContext = createExecutionContext(deleteContextRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteContextRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteContextRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteContext");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteContextResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a data quality monitoring job definition.
*
*
* @param deleteDataQualityJobDefinitionRequest
* @return Result of the DeleteDataQualityJobDefinition operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteDataQualityJobDefinition
* @see AWS API Documentation
*/
@Override
public DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeDeleteDataQualityJobDefinition(request);
}
@SdkInternalApi
final DeleteDataQualityJobDefinitionResult executeDeleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteDataQualityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteDataQualityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteDataQualityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteDataQualityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteDataQualityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a fleet.
*
*
* @param deleteDeviceFleetRequest
* @return Result of the DeleteDeviceFleet operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @sample AmazonSageMaker.DeleteDeviceFleet
* @see AWS
* API Documentation
*/
@Override
public DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request) {
request = beforeClientExecution(request);
return executeDeleteDeviceFleet(request);
}
@SdkInternalApi
final DeleteDeviceFleetResult executeDeleteDeviceFleet(DeleteDeviceFleetRequest deleteDeviceFleetRequest) {
ExecutionContext executionContext = createExecutionContext(deleteDeviceFleetRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteDeviceFleetRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteDeviceFleetRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteDeviceFleet");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteDeviceFleetResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again
* using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including
* data, notebooks, and other artifacts.
*
*
* @param deleteDomainRequest
* @return Result of the DeleteDomain operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteDomain
* @see AWS API
* Documentation
*/
@Override
public DeleteDomainResult deleteDomain(DeleteDomainRequest request) {
request = beforeClientExecution(request);
return executeDeleteDomain(request);
}
@SdkInternalApi
final DeleteDomainResult executeDeleteDomain(DeleteDomainRequest deleteDomainRequest) {
ExecutionContext executionContext = createExecutionContext(deleteDomainRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteDomainRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteDomainRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteDomain");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteDomainResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was
* created.
*
*
* Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use
* the RevokeGrant API call.
*
*
* @param deleteEndpointRequest
* @return Result of the DeleteEndpoint operation returned by the service.
* @sample AmazonSageMaker.DeleteEndpoint
* @see AWS API
* Documentation
*/
@Override
public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request) {
request = beforeClientExecution(request);
return executeDeleteEndpoint(request);
}
@SdkInternalApi
final DeleteEndpointResult executeDeleteEndpoint(DeleteEndpointRequest deleteEndpointRequest) {
ExecutionContext executionContext = createExecutionContext(deleteEndpointRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteEndpointRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteEndpointRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteEndpoint");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteEndpointResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified
* configuration. It does not delete endpoints created using the configuration.
*
*
* You must not delete an EndpointConfig in use by an endpoint that is live or while the
* UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you
* delete the EndpointConfig of an endpoint that is active or being created or updated you may lose
* visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring
* charges.
*
*
* @param deleteEndpointConfigRequest
* @return Result of the DeleteEndpointConfig operation returned by the service.
* @sample AmazonSageMaker.DeleteEndpointConfig
* @see AWS
* API Documentation
*/
@Override
public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request) {
request = beforeClientExecution(request);
return executeDeleteEndpointConfig(request);
}
@SdkInternalApi
final DeleteEndpointConfigResult executeDeleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest) {
ExecutionContext executionContext = createExecutionContext(deleteEndpointConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteEndpointConfigRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteEndpointConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteEndpointConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteEndpointConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the
* ListTrials API to get a list of the trials associated with the experiment.
*
*
* @param deleteExperimentRequest
* @return Result of the DeleteExperiment operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteExperiment
* @see AWS API
* Documentation
*/
@Override
public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request) {
request = beforeClientExecution(request);
return executeDeleteExperiment(request);
}
@SdkInternalApi
final DeleteExperimentResult executeDeleteExperiment(DeleteExperimentRequest deleteExperimentRequest) {
ExecutionContext executionContext = createExecutionContext(deleteExperimentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteExperimentRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteExperimentRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteExperiment");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteExperimentResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Delete the FeatureGroup and any data that was written to the OnlineStore of the
* FeatureGroup. Data cannot be accessed from the OnlineStore immediately after
* DeleteFeatureGroup is called.
*
*
* Data written into the OfflineStore will not be deleted. The AWS Glue database and tables that are
* automatically created for your OfflineStore are not deleted.
*
*
* @param deleteFeatureGroupRequest
* @return Result of the DeleteFeatureGroup operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteFeatureGroup
* @see AWS
* API Documentation
*/
@Override
public DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest request) {
request = beforeClientExecution(request);
return executeDeleteFeatureGroup(request);
}
@SdkInternalApi
final DeleteFeatureGroupResult executeDeleteFeatureGroup(DeleteFeatureGroupRequest deleteFeatureGroupRequest) {
ExecutionContext executionContext = createExecutionContext(deleteFeatureGroupRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteFeatureGroupRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteFeatureGroupRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteFeatureGroup");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteFeatureGroupResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified flow definition.
*
*
* @param deleteFlowDefinitionRequest
* @return Result of the DeleteFlowDefinition operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteFlowDefinition
* @see AWS
* API Documentation
*/
@Override
public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request) {
request = beforeClientExecution(request);
return executeDeleteFlowDefinition(request);
}
@SdkInternalApi
final DeleteFlowDefinitionResult executeDeleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteFlowDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteFlowDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteFlowDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteFlowDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteFlowDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Use this operation to delete a human task user interface (worker task template).
*
*
* To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
* task template, it no longer appears when you call ListHumanTaskUis.
*
*
* @param deleteHumanTaskUiRequest
* @return Result of the DeleteHumanTaskUi operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteHumanTaskUi
* @see AWS
* API Documentation
*/
@Override
public DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request) {
request = beforeClientExecution(request);
return executeDeleteHumanTaskUi(request);
}
@SdkInternalApi
final DeleteHumanTaskUiResult executeDeleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest) {
ExecutionContext executionContext = createExecutionContext(deleteHumanTaskUiRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteHumanTaskUiRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteHumanTaskUiRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteHumanTaskUi");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteHumanTaskUiResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
*
*
* @param deleteImageRequest
* @return Result of the DeleteImage operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteImage
* @see AWS API
* Documentation
*/
@Override
public DeleteImageResult deleteImage(DeleteImageRequest request) {
request = beforeClientExecution(request);
return executeDeleteImage(request);
}
@SdkInternalApi
final DeleteImageResult executeDeleteImage(DeleteImageRequest deleteImageRequest) {
ExecutionContext executionContext = createExecutionContext(deleteImageRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteImageRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteImageRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteImage");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteImageResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
*
*
* @param deleteImageVersionRequest
* @return Result of the DeleteImageVersion operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteImageVersion
* @see AWS
* API Documentation
*/
@Override
public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request) {
request = beforeClientExecution(request);
return executeDeleteImageVersion(request);
}
@SdkInternalApi
final DeleteImageVersionResult executeDeleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteImageVersionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteImageVersionRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteImageVersionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteImageVersion");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteImageVersionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon
* SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the
* IAM role that you specified when creating the model.
*
*
* @param deleteModelRequest
* @return Result of the DeleteModel operation returned by the service.
* @sample AmazonSageMaker.DeleteModel
* @see AWS API
* Documentation
*/
@Override
public DeleteModelResult deleteModel(DeleteModelRequest request) {
request = beforeClientExecution(request);
return executeDeleteModel(request);
}
@SdkInternalApi
final DeleteModelResult executeDeleteModel(DeleteModelRequest deleteModelRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModel");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an Amazon SageMaker model bias job definition.
*
*
* @param deleteModelBiasJobDefinitionRequest
* @return Result of the DeleteModelBiasJobDefinition operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteModelBiasJobDefinition
* @see AWS API Documentation
*/
@Override
public DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelBiasJobDefinition(request);
}
@SdkInternalApi
final DeleteModelBiasJobDefinitionResult executeDeleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelBiasJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelBiasJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteModelBiasJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelBiasJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteModelBiasJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an Amazon SageMaker model explainability job definition.
*
*
* @param deleteModelExplainabilityJobDefinitionRequest
* @return Result of the DeleteModelExplainabilityJobDefinition operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteModelExplainabilityJobDefinition
* @see AWS API Documentation
*/
@Override
public DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelExplainabilityJobDefinition(request);
}
@SdkInternalApi
final DeleteModelExplainabilityJobDefinitionResult executeDeleteModelExplainabilityJobDefinition(
DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelExplainabilityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelExplainabilityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteModelExplainabilityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelExplainabilityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteModelExplainabilityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a model package.
*
*
* A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to
* model packages listed on AWS Marketplace to create models in Amazon SageMaker.
*
*
* @param deleteModelPackageRequest
* @return Result of the DeleteModelPackage operation returned by the service.
* @throws ConflictException
* There was a conflict when you attempted to modify an experiment, trial, or trial component.
* @sample AmazonSageMaker.DeleteModelPackage
* @see AWS
* API Documentation
*/
@Override
public DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelPackage(request);
}
@SdkInternalApi
final DeleteModelPackageResult executeDeleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelPackageRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelPackageRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteModelPackageRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelPackage");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteModelPackageResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified model group.
*
*
* @param deleteModelPackageGroupRequest
* @return Result of the DeleteModelPackageGroup operation returned by the service.
* @sample AmazonSageMaker.DeleteModelPackageGroup
* @see AWS API Documentation
*/
@Override
public DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelPackageGroup(request);
}
@SdkInternalApi
final DeleteModelPackageGroupResult executeDeleteModelPackageGroup(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelPackageGroupRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelPackageGroupRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteModelPackageGroupRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelPackageGroup");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteModelPackageGroupResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a model group resource policy.
*
*
* @param deleteModelPackageGroupPolicyRequest
* @return Result of the DeleteModelPackageGroupPolicy operation returned by the service.
* @sample AmazonSageMaker.DeleteModelPackageGroupPolicy
* @see AWS API Documentation
*/
@Override
public DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelPackageGroupPolicy(request);
}
@SdkInternalApi
final DeleteModelPackageGroupPolicyResult executeDeleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelPackageGroupPolicyRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelPackageGroupPolicyRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteModelPackageGroupPolicyRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelPackageGroupPolicy");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteModelPackageGroupPolicyResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the secified model quality monitoring job definition.
*
*
* @param deleteModelQualityJobDefinitionRequest
* @return Result of the DeleteModelQualityJobDefinition operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteModelQualityJobDefinition
* @see AWS API Documentation
*/
@Override
public DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request) {
request = beforeClientExecution(request);
return executeDeleteModelQualityJobDefinition(request);
}
@SdkInternalApi
final DeleteModelQualityJobDefinitionResult executeDeleteModelQualityJobDefinition(
DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteModelQualityJobDefinitionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteModelQualityJobDefinitionRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteModelQualityJobDefinitionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteModelQualityJobDefinition");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteModelQualityJobDefinitionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job
* execution history of the monitoring schedule.
*
*
* @param deleteMonitoringScheduleRequest
* @return Result of the DeleteMonitoringSchedule operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteMonitoringSchedule
* @see AWS API Documentation
*/
@Override
public DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request) {
request = beforeClientExecution(request);
return executeDeleteMonitoringSchedule(request);
}
@SdkInternalApi
final DeleteMonitoringScheduleResult executeDeleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest) {
ExecutionContext executionContext = createExecutionContext(deleteMonitoringScheduleRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteMonitoringScheduleRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteMonitoringScheduleRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteMonitoringSchedule");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteMonitoringScheduleResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
* StopNotebookInstance API.
*
*
*
* When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance,
* and deletes the ML storage volume and the network interface associated with the notebook instance.
*
*
*
* @param deleteNotebookInstanceRequest
* @return Result of the DeleteNotebookInstance operation returned by the service.
* @sample AmazonSageMaker.DeleteNotebookInstance
* @see AWS API Documentation
*/
@Override
public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request) {
request = beforeClientExecution(request);
return executeDeleteNotebookInstance(request);
}
@SdkInternalApi
final DeleteNotebookInstanceResult executeDeleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest) {
ExecutionContext executionContext = createExecutionContext(deleteNotebookInstanceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteNotebookInstanceRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteNotebookInstanceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteNotebookInstance");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteNotebookInstanceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a notebook instance lifecycle configuration.
*
*
* @param deleteNotebookInstanceLifecycleConfigRequest
* @return Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
* @sample AmazonSageMaker.DeleteNotebookInstanceLifecycleConfig
* @see AWS API Documentation
*/
@Override
public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request) {
request = beforeClientExecution(request);
return executeDeleteNotebookInstanceLifecycleConfig(request);
}
@SdkInternalApi
final DeleteNotebookInstanceLifecycleConfigResult executeDeleteNotebookInstanceLifecycleConfig(
DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest) {
ExecutionContext executionContext = createExecutionContext(deleteNotebookInstanceLifecycleConfigRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteNotebookInstanceLifecycleConfigRequestProtocolMarshaller(protocolFactory).marshall(super
.beforeMarshalling(deleteNotebookInstanceLifecycleConfigRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteNotebookInstanceLifecycleConfig");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false),
new DeleteNotebookInstanceLifecycleConfigResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a pipeline if there are no in-progress executions.
*
*
* @param deletePipelineRequest
* @return Result of the DeletePipeline operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeletePipeline
* @see AWS API
* Documentation
*/
@Override
public DeletePipelineResult deletePipeline(DeletePipelineRequest request) {
request = beforeClientExecution(request);
return executeDeletePipeline(request);
}
@SdkInternalApi
final DeletePipelineResult executeDeletePipeline(DeletePipelineRequest deletePipelineRequest) {
ExecutionContext executionContext = createExecutionContext(deletePipelineRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeletePipelineRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deletePipelineRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeletePipeline");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeletePipelineResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Delete the specified project.
*
*
* @param deleteProjectRequest
* @return Result of the DeleteProject operation returned by the service.
* @sample AmazonSageMaker.DeleteProject
* @see AWS API
* Documentation
*/
@Override
public DeleteProjectResult deleteProject(DeleteProjectRequest request) {
request = beforeClientExecution(request);
return executeDeleteProject(request);
}
@SdkInternalApi
final DeleteProjectResult executeDeleteProject(DeleteProjectRequest deleteProjectRequest) {
ExecutionContext executionContext = createExecutionContext(deleteProjectRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteProjectRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteProjectRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteProject");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteProjectResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified tags from an Amazon SageMaker resource.
*
*
* To list a resource's tags, use the ListTags API.
*
*
*
* When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from
* training jobs that the hyperparameter tuning job launched before you called this API.
*
*
*
* @param deleteTagsRequest
* @return Result of the DeleteTags operation returned by the service.
* @sample AmazonSageMaker.DeleteTags
* @see AWS API
* Documentation
*/
@Override
public DeleteTagsResult deleteTags(DeleteTagsRequest request) {
request = beforeClientExecution(request);
return executeDeleteTags(request);
}
@SdkInternalApi
final DeleteTagsResult executeDeleteTags(DeleteTagsRequest deleteTagsRequest) {
ExecutionContext executionContext = createExecutionContext(deleteTagsRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteTagsRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteTagsRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteTags");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteTagsResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the
* DescribeTrialComponent API to get the list of trial components.
*
*
* @param deleteTrialRequest
* @return Result of the DeleteTrial operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteTrial
* @see AWS API
* Documentation
*/
@Override
public DeleteTrialResult deleteTrial(DeleteTrialRequest request) {
request = beforeClientExecution(request);
return executeDeleteTrial(request);
}
@SdkInternalApi
final DeleteTrialResult executeDeleteTrial(DeleteTrialRequest deleteTrialRequest) {
ExecutionContext executionContext = createExecutionContext(deleteTrialRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteTrialRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteTrialRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteTrial");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteTrialResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes the specified trial component. A trial component must be disassociated from all trials before the trial
* component can be deleted. To disassociate a trial component from a trial, call the
* DisassociateTrialComponent API.
*
*
* @param deleteTrialComponentRequest
* @return Result of the DeleteTrialComponent operation returned by the service.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteTrialComponent
* @see AWS
* API Documentation
*/
@Override
public DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request) {
request = beforeClientExecution(request);
return executeDeleteTrialComponent(request);
}
@SdkInternalApi
final DeleteTrialComponentResult executeDeleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest) {
ExecutionContext executionContext = createExecutionContext(deleteTrialComponentRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteTrialComponentRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteTrialComponentRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteTrialComponent");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteTrialComponentResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including
* data, notebooks, and other artifacts.
*
*
* @param deleteUserProfileRequest
* @return Result of the DeleteUserProfile operation returned by the service.
* @throws ResourceInUseException
* Resource being accessed is in use.
* @throws ResourceNotFoundException
* Resource being access is not found.
* @sample AmazonSageMaker.DeleteUserProfile
* @see AWS
* API Documentation
*/
@Override
public DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request) {
request = beforeClientExecution(request);
return executeDeleteUserProfile(request);
}
@SdkInternalApi
final DeleteUserProfileResult executeDeleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest) {
ExecutionContext executionContext = createExecutionContext(deleteUserProfileRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteUserProfileRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteUserProfileRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteUserProfile");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteUserProfileResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Use this operation to delete a workforce.
*
*
* If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to
* delete the existing workforce and then use to create a new workforce.
*
*
*
* If a private workforce contains one or more work teams, you must use the operation to delete all work teams
* before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will
* recieve a ResourceInUse error.
*
*
*
* @param deleteWorkforceRequest
* @return Result of the DeleteWorkforce operation returned by the service.
* @sample AmazonSageMaker.DeleteWorkforce
* @see AWS API
* Documentation
*/
@Override
public DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request) {
request = beforeClientExecution(request);
return executeDeleteWorkforce(request);
}
@SdkInternalApi
final DeleteWorkforceResult executeDeleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest) {
ExecutionContext executionContext = createExecutionContext(deleteWorkforceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteWorkforceRequestProtocolMarshaller(protocolFactory).marshall(super.beforeMarshalling(deleteWorkforceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
request.addHandlerContext(HandlerContextKey.ENDPOINT_OVERRIDDEN, isEndpointOverridden());
request.addHandlerContext(HandlerContextKey.SIGNING_REGION, getSigningRegion());
request.addHandlerContext(HandlerContextKey.SERVICE_ID, "SageMaker");
request.addHandlerContext(HandlerContextKey.OPERATION_NAME, "DeleteWorkforce");
request.addHandlerContext(HandlerContextKey.ADVANCED_CONFIG, advancedConfig);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true).withHasStreamingSuccessResponse(false), new DeleteWorkforceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*