@ThreadSafe @Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AmazonSageMakerClient extends AmazonWebServiceClient implements AmazonSageMaker
Provides APIs for creating and managing SageMaker resources.
Other Resources:
LOGGING_AWS_REQUEST_METRIC
ENDPOINT_PREFIX
addRequestHandler, addRequestHandler, configureRegion, getClientConfiguration, getEndpointPrefix, getMonitoringListeners, getRequestMetricsCollector, getServiceName, getSignerByURI, getSignerOverride, getSignerRegionOverride, getTimeOffset, makeImmutable, removeRequestHandler, removeRequestHandler, setEndpoint, setEndpoint, setRegion, setServiceNameIntern, setSignerRegionOverride, setTimeOffset, withEndpoint, withRegion, withRegion, withTimeOffset
public static AmazonSageMakerClientBuilder builder()
public AddAssociationResult addAssociation(AddAssociationRequest request)
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.
addAssociation
in interface AmazonSageMaker
addAssociationRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public AddTagsResult addTags(AddTagsRequest request)
Adds or overwrites one or more tags for the specified 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 Amazon Web Services 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
Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the
Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched
before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to
all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile
by specifying them in the Tags
parameter of CreateDomain or CreateUserProfile.
addTags
in interface AmazonSageMaker
addTagsRequest
- public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request)
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.
associateTrialComponent
in interface AmazonSageMaker
associateTrialComponentRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public BatchDescribeModelPackageResult batchDescribeModelPackage(BatchDescribeModelPackageRequest request)
This action batch describes a list of versioned model packages
batchDescribeModelPackage
in interface AmazonSageMaker
batchDescribeModelPackageRequest
- public CreateActionResult createAction(CreateActionRequest request)
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.
createAction
in interface AmazonSageMaker
createActionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request)
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
createAlgorithm
in interface AmazonSageMaker
createAlgorithmRequest
- public CreateAppResult createApp(CreateAppRequest request)
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
createApp
in interface AmazonSageMaker
createAppRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request)
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
createAppImageConfig
in interface AmazonSageMaker
createAppImageConfigRequest
- ResourceInUseException
- Resource being accessed is in use.public CreateArtifactResult createArtifact(CreateArtifactRequest request)
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.
createArtifact
in interface AmazonSageMaker
createArtifactRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request)
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.
CreateAutoMLJobV2
can manage tabular problem types identical to those of its previous version
CreateAutoMLJob
, as well as time-series forecasting, non-tabular problem types such as image or text
classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
createAutoMLJob
in interface AmazonSageMaker
createAutoMLJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateAutoMLJobV2Result createAutoMLJobV2(CreateAutoMLJobV2Request request)
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2
can manage tabular problem types identical to those of its previous version
CreateAutoMLJob
, as well as time-series forecasting, non-tabular problem types such as image or text
classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
For the list of available problem types supported by CreateAutoMLJobV2
, see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
createAutoMLJobV2
in interface AmazonSageMaker
createAutoMLJobV2Request
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateClusterResult createCluster(CreateClusterRequest request)
Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
createCluster
in interface AmazonSageMaker
createClusterRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request)
Creates a Git repository as a resource in your 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 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 Amazon Web Services CodeCommit or in any other Git repository.
createCodeRepository
in interface AmazonSageMaker
createCodeRepositoryRequest
- public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request)
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 Amazon Web Services 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.
createCompilationJob
in interface AmazonSageMaker
createCompilationJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateContextResult createContext(CreateContextRequest request)
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.
createContext
in interface AmazonSageMaker
createContextRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request)
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createDataQualityJobDefinition
in interface AmazonSageMaker
createDataQualityJobDefinitionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request)
Creates a device fleet.
createDeviceFleet
in interface AmazonSageMaker
createDeviceFleetRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateDomainResult createDomain(CreateDomainRequest request)
Creates a Domain
. A domain consists of an associated Amazon Elastic File System volume, a list of
authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC)
configurations. 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 Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other
traffic, you can specify the AppNetworkAccessType
parameter. AppNetworkAccessType
corresponds to the network access type that you choose when you onboard to the domain. 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 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 Amazon SageMaker 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.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
createDomain
in interface AmazonSageMaker
createDomainRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateEdgeDeploymentPlanResult createEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest request)
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
createEdgeDeploymentPlan
in interface AmazonSageMaker
createEdgeDeploymentPlanRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateEdgeDeploymentStageResult createEdgeDeploymentStage(CreateEdgeDeploymentStageRequest request)
Creates a new stage in an existing edge deployment plan.
createEdgeDeploymentStage
in interface AmazonSageMaker
createEdgeDeploymentStageRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request)
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.
createEdgePackagingJob
in interface AmazonSageMaker
createEdgePackagingJobRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
Creates an endpoint using the endpoint configuration specified in the request. 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 SageMaker hosting services.
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 Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, 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 SageMaker receives the request, it sets the endpoint status to Creating
. After it creates the
endpoint, it sets the status to InService
. 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, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services 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 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 SageMaker API Permissions: Actions, Permissions, and Resources Reference.
createEndpoint
in interface AmazonSageMaker
createEndpointRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
Creates an endpoint configuration that 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 SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use 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 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. SageMaker distributes two-thirds of the traffic to Model A, and
one-third to model B.
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.
createEndpointConfig
in interface AmazonSageMaker
createEndpointConfigRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateExperimentResult createExperiment(CreateExperimentRequest request)
Creates a 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.
In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.
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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.
createExperiment
in interface AmazonSageMaker
createExperimentRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest request)
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 Amazon Web Services service
quotas to see the FeatureGroup
s quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an OnlineStore
FeatureGroup
with the InMemory
StorageType
.
You must include at least one of OnlineStoreConfig
and OfflineStoreConfig
to create a
FeatureGroup
.
createFeatureGroup
in interface AmazonSageMaker
createFeatureGroupRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request)
Creates a flow definition.
createFlowDefinition
in interface AmazonSageMaker
createFlowDefinitionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateHubResult createHub(CreateHubRequest request)
Create a hub.
createHub
in interface AmazonSageMaker
createHubRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateHubContentReferenceResult createHubContentReference(CreateHubContentReferenceRequest request)
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
createHubContentReference
in interface AmazonSageMaker
createHubContentReferenceRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request)
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.
createHumanTaskUi
in interface AmazonSageMaker
createHumanTaskUiRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
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.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
createHyperParameterTuningJob
in interface AmazonSageMaker
createHyperParameterTuningJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateImageResult createImage(CreateImageRequest request)
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker image.
createImage
in interface AmazonSageMaker
createImageRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateImageVersionResult createImageVersion(CreateImageVersionRequest request)
Creates a version of the SageMaker image specified by ImageName
. The version represents the Amazon
ECR container image specified by BaseImage
.
createImageVersion
in interface AmazonSageMaker
createImageVersionRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public CreateInferenceComponentResult createInferenceComponent(CreateInferenceComponentRequest request)
Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
createInferenceComponent
in interface AmazonSageMaker
createInferenceComponentRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateInferenceExperimentResult createInferenceExperiment(CreateInferenceExperimentRequest request)
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
createInferenceExperiment
in interface AmazonSageMaker
createInferenceExperimentRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateInferenceRecommendationsJobResult createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest request)
Starts a recommendation job. You can create either an instance recommendation or load test job.
createInferenceRecommendationsJob
in interface AmazonSageMaker
createInferenceRecommendationsJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request)
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 Amazon Web Services 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.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job
stops if all data objects in the input manifest file identified in ManifestS3Uri
have been labeled.
A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send
new data objects to an active (InProgress
) streaming labeling job in real time. To learn how to
create a static labeling job, see Create a Labeling Job
(API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling
Job.
createLabelingJob
in interface AmazonSageMaker
createLabelingJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateMlflowTrackingServerResult createMlflowTrackingServer(CreateMlflowTrackingServerRequest request)
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
createMlflowTrackingServer
in interface AmazonSageMaker
createMlflowTrackingServerRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateModelResult createModel(CreateModelRequest request)
Creates a model in 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 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. SageMaker then deploys all of the containers that
you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the CreateTransformJob
API.
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that 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 Amazon Web Services resources, you grant necessary permissions via this role.
createModel
in interface AmazonSageMaker
createModelRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request)
Creates the definition for a model bias job.
createModelBiasJobDefinition
in interface AmazonSageMaker
createModelBiasJobDefinitionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateModelCardResult createModelCard(CreateModelCardRequest request)
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
createModelCard
in interface AmazonSageMaker
createModelCardRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public CreateModelCardExportJobResult createModelCardExportJob(CreateModelCardExportJobRequest request)
Creates an Amazon SageMaker Model Card export job.
createModelCardExportJob
in interface AmazonSageMaker
createModelCardExportJobRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request)
Creates the definition for a model explainability job.
createModelExplainabilityJobDefinition
in interface AmazonSageMaker
createModelExplainabilityJobDefinitionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request)
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in 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 Amazon Web Services 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.
createModelPackage
in interface AmazonSageMaker
createModelPackageRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request)
Creates a model group. A model group contains a group of model versions.
createModelPackageGroup
in interface AmazonSageMaker
createModelPackageGroupRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request)
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
createModelQualityJobDefinition
in interface AmazonSageMaker
createModelQualityJobDefinitionRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request)
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
createMonitoringSchedule
in interface AmazonSageMaker
createMonitoringScheduleRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
Creates an 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.
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.
SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker does the following:
Creates a network interface in the SageMaker VPC.
(Option) If you specified SubnetId
, 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, 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 SageMaker VPC. If you specified
SubnetId
of your VPC, 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, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After 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 SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstance
in interface AmazonSageMaker
createNotebookInstanceRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
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 Amazon 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.
createNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
createNotebookInstanceLifecycleConfigRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateOptimizationJobResult createOptimizationJob(CreateOptimizationJobRequest request)
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
createOptimizationJob
in interface AmazonSageMaker
createOptimizationJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreatePipelineResult createPipeline(CreatePipelineRequest request)
Creates a pipeline using a JSON pipeline definition.
createPipeline
in interface AmazonSageMaker
createPipelineRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user passed to this API defines the permissions to access the app. 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 app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .
The URL that you get from a call to CreatePresignedDomainUrl
has a default timeout of 5 minutes. You
can configure this value using ExpiresInSeconds
. If you try to use the URL after the timeout limit
expires, you are directed to the Amazon Web Services console sign-in page.
createPresignedDomainUrl
in interface AmazonSageMaker
createPresignedDomainUrlRequest
- ResourceNotFoundException
- Resource being access is not found.public CreatePresignedMlflowTrackingServerUrlResult createPresignedMlflowTrackingServerUrl(CreatePresignedMlflowTrackingServerUrlRequest request)
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
createPresignedMlflowTrackingServerUrl
in interface AmazonSageMaker
createPresignedMlflowTrackingServerUrlRequest
- ResourceNotFoundException
- Resource being access is not found.public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker
console, when you choose Open
next to a notebook instance, 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 Amazon Web Services console sign-in page.
createPresignedNotebookInstanceUrl
in interface AmazonSageMaker
createPresignedNotebookInstanceUrlRequest
- public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request)
Creates a processing job.
createProcessingJob
in interface AmazonSageMaker
createProcessingJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public CreateProjectResult createProject(CreateProjectRequest request)
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.
createProject
in interface AmazonSageMaker
createProjectRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateSpaceResult createSpace(CreateSpaceRequest request)
Creates a private space or a space used for real time collaboration in a domain.
createSpace
in interface AmazonSageMaker
createSpaceRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateStudioLifecycleConfigResult createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest request)
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
createStudioLifecycleConfig
in interface AmazonSageMaker
createStudioLifecycleConfigRequest
- ResourceInUseException
- Resource being accessed is in use.public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using 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 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 SageMaker, see Algorithms.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
InputDataConfig
- Describes the input required by the training job and the Amazon S3, EFS, or FSx
location where it is stored.
OutputDataConfig
- Identifies the Amazon S3 bucket where you want 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 Name (ARN) that SageMaker assumes to perform tasks on your behalf
during model training. You must grant this role the necessary permissions so that 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 a managed spot training job has to
complete.
Environment
- The environment variables to set in the Docker container.
RetryStrategy
- The number of times to retry the job when the job fails due to an
InternalServerError
.
For more information about SageMaker, see How It Works.
createTrainingJob
in interface AmazonSageMaker
createTrainingJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
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 Amazon Web
Services Region in an Amazon Web Services account.
ModelName
- Identifies the model to use. ModelName
must be the name of an existing
Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services 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.
createTransformJob
in interface AmazonSageMaker
createTransformJobRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public CreateTrialResult createTrial(CreateTrialRequest request)
Creates an 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 SageMaker experiment.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.
createTrial
in interface AmazonSageMaker
createTrialRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request)
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 SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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
in interface AmazonSageMaker
createTrialComponentRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request)
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 a domain. If an administrator invites a person by email or imports them from IAM Identity Center, 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 home directory.
createUserProfile
in interface AmazonSageMaker
createUserProfileRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.public CreateWorkforceResult createWorkforce(CreateWorkforceRequest request)
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the
DeleteWorkforce
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).
createWorkforce
in interface AmazonSageMaker
createWorkforceRequest
- public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request)
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.
createWorkteam
in interface AmazonSageMaker
createWorkteamRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public DeleteActionResult deleteAction(DeleteActionRequest request)
Deletes an action.
deleteAction
in interface AmazonSageMaker
deleteActionRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request)
Removes the specified algorithm from your account.
deleteAlgorithm
in interface AmazonSageMaker
deleteAlgorithmRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteAppResult deleteApp(DeleteAppRequest request)
Used to stop and delete an app.
deleteApp
in interface AmazonSageMaker
deleteAppRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request)
Deletes an AppImageConfig.
deleteAppImageConfig
in interface AmazonSageMaker
deleteAppImageConfigRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteArtifactResult deleteArtifact(DeleteArtifactRequest request)
Deletes an artifact. Either ArtifactArn
or Source
must be specified.
deleteArtifact
in interface AmazonSageMaker
deleteArtifactRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request)
Deletes an association.
deleteAssociation
in interface AmazonSageMaker
deleteAssociationRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteClusterResult deleteCluster(DeleteClusterRequest request)
Delete a SageMaker HyperPod cluster.
deleteCluster
in interface AmazonSageMaker
deleteClusterRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request)
Deletes the specified Git repository from your account.
deleteCodeRepository
in interface AmazonSageMaker
deleteCodeRepositoryRequest
- public DeleteCompilationJobResult deleteCompilationJob(DeleteCompilationJobRequest request)
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.
You can delete a compilation job only if its current status is COMPLETED
, FAILED
, or
STOPPED
. If the job status is STARTING
or INPROGRESS
, stop the job, and
then delete it after its status becomes STOPPED
.
deleteCompilationJob
in interface AmazonSageMaker
deleteCompilationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteContextResult deleteContext(DeleteContextRequest request)
Deletes an context.
deleteContext
in interface AmazonSageMaker
deleteContextRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request)
Deletes a data quality monitoring job definition.
deleteDataQualityJobDefinition
in interface AmazonSageMaker
deleteDataQualityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request)
Deletes a fleet.
deleteDeviceFleet
in interface AmazonSageMaker
deleteDeviceFleetRequest
- ResourceInUseException
- Resource being accessed is in use.public DeleteDomainResult deleteDomain(DeleteDomainRequest request)
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomain
in interface AmazonSageMaker
deleteDomainRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteEdgeDeploymentPlanResult deleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest request)
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
deleteEdgeDeploymentPlan
in interface AmazonSageMaker
deleteEdgeDeploymentPlanRequest
- ResourceInUseException
- Resource being accessed is in use.public DeleteEdgeDeploymentStageResult deleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest request)
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
deleteEdgeDeploymentStage
in interface AmazonSageMaker
deleteEdgeDeploymentStageRequest
- ResourceInUseException
- Resource being accessed is in use.public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key
grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do
not delete or revoke the permissions for your
ExecutionRoleArn
, otherwise SageMaker cannot delete these resources.
deleteEndpoint
in interface AmazonSageMaker
deleteEndpointRequest
- public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
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.
deleteEndpointConfig
in interface AmazonSageMaker
deleteEndpointConfigRequest
- public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request)
Deletes an 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.
deleteExperiment
in interface AmazonSageMaker
deleteExperimentRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest request)
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 Amazon Web Services Glue database and
tables that are automatically created for your OfflineStore
are not deleted.
Note that it can take approximately 10-15 minutes to delete an OnlineStore
FeatureGroup
with the InMemory
StorageType
.
deleteFeatureGroup
in interface AmazonSageMaker
deleteFeatureGroupRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request)
Deletes the specified flow definition.
deleteFlowDefinition
in interface AmazonSageMaker
deleteFlowDefinitionRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteHubResult deleteHub(DeleteHubRequest request)
Delete a hub.
deleteHub
in interface AmazonSageMaker
deleteHubRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteHubContentResult deleteHubContent(DeleteHubContentRequest request)
Delete the contents of a hub.
deleteHubContent
in interface AmazonSageMaker
deleteHubContentRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteHubContentReferenceResult deleteHubContentReference(DeleteHubContentReferenceRequest request)
Delete a hub content reference in order to remove a model from a private hub.
deleteHubContentReference
in interface AmazonSageMaker
deleteHubContentReferenceRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request)
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 ListHumanTaskUis.
When you delete a worker task template, it no longer appears when you call ListHumanTaskUis
.
deleteHumanTaskUi
in interface AmazonSageMaker
deleteHumanTaskUiRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteHyperParameterTuningJobResult deleteHyperParameterTuningJob(DeleteHyperParameterTuningJobRequest request)
Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob
API deletes only the tuning
job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob
API. It
does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
deleteHyperParameterTuningJob
in interface AmazonSageMaker
deleteHyperParameterTuningJobRequest
- public DeleteImageResult deleteImage(DeleteImageRequest request)
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImage
in interface AmazonSageMaker
deleteImageRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request)
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteImageVersion
in interface AmazonSageMaker
deleteImageVersionRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteInferenceComponentResult deleteInferenceComponent(DeleteInferenceComponentRequest request)
Deletes an inference component.
deleteInferenceComponent
in interface AmazonSageMaker
deleteInferenceComponentRequest
- public DeleteInferenceExperimentResult deleteInferenceExperiment(DeleteInferenceExperimentRequest request)
Deletes an inference experiment.
This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.
deleteInferenceExperiment
in interface AmazonSageMaker
deleteInferenceExperimentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public DeleteMlflowTrackingServerResult deleteMlflowTrackingServer(DeleteMlflowTrackingServerRequest request)
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
deleteMlflowTrackingServer
in interface AmazonSageMaker
deleteMlflowTrackingServerRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteModelResult deleteModel(DeleteModelRequest request)
Deletes a model. The DeleteModel
API deletes only the model entry that was created in 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.
deleteModel
in interface AmazonSageMaker
deleteModelRequest
- public DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request)
Deletes an Amazon SageMaker model bias job definition.
deleteModelBiasJobDefinition
in interface AmazonSageMaker
deleteModelBiasJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteModelCardResult deleteModelCard(DeleteModelCardRequest request)
Deletes an Amazon SageMaker Model Card.
deleteModelCard
in interface AmazonSageMaker
deleteModelCardRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request)
Deletes an Amazon SageMaker model explainability job definition.
deleteModelExplainabilityJobDefinition
in interface AmazonSageMaker
deleteModelExplainabilityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request)
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
deleteModelPackage
in interface AmazonSageMaker
deleteModelPackageRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest request)
Deletes the specified model group.
deleteModelPackageGroup
in interface AmazonSageMaker
deleteModelPackageGroupRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request)
Deletes a model group resource policy.
deleteModelPackageGroupPolicy
in interface AmazonSageMaker
deleteModelPackageGroupPolicyRequest
- public DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request)
Deletes the secified model quality monitoring job definition.
deleteModelQualityJobDefinition
in interface AmazonSageMaker
deleteModelQualityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)
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.
deleteMonitoringSchedule
in interface AmazonSageMaker
deleteMonitoringScheduleRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
Deletes an 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. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstance
in interface AmazonSageMaker
deleteNotebookInstanceRequest
- public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
deleteNotebookInstanceLifecycleConfigRequest
- public DeleteOptimizationJobResult deleteOptimizationJob(DeleteOptimizationJobRequest request)
Deletes an optimization job.
deleteOptimizationJob
in interface AmazonSageMaker
deleteOptimizationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DeletePipelineResult deletePipeline(DeletePipelineRequest request)
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all
running instances of the pipeline using the StopPipelineExecution
API. When you delete a pipeline,
all instances of the pipeline are deleted.
deletePipeline
in interface AmazonSageMaker
deletePipelineRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteProjectResult deleteProject(DeleteProjectRequest request)
Delete the specified project.
deleteProject
in interface AmazonSageMaker
deleteProjectRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public DeleteSpaceResult deleteSpace(DeleteSpaceRequest request)
Used to delete a space.
deleteSpace
in interface AmazonSageMaker
deleteSpaceRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteStudioLifecycleConfigResult deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest request)
Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
deleteStudioLifecycleConfig
in interface AmazonSageMaker
deleteStudioLifecycleConfigRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceInUseException
- Resource being accessed is in use.public DeleteTagsResult deleteTags(DeleteTagsRequest request)
Deletes the specified tags from an 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.
When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.
deleteTags
in interface AmazonSageMaker
deleteTagsRequest
- public DeleteTrialResult deleteTrial(DeleteTrialRequest request)
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.
deleteTrial
in interface AmazonSageMaker
deleteTrialRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request)
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.
deleteTrialComponent
in interface AmazonSageMaker
deleteTrialComponentRequest
- ResourceNotFoundException
- Resource being access is not found.public DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request)
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
deleteUserProfile
in interface AmazonSageMaker
deleteUserProfileRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request)
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
If a private workforce contains one or more work teams, you must use the DeleteWorkteam
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 receive a ResourceInUse
error.
deleteWorkforce
in interface AmazonSageMaker
deleteWorkforceRequest
- public DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest request)
Deletes an existing work team. This operation can't be undone.
deleteWorkteam
in interface AmazonSageMaker
deleteWorkteamRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest request)
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
deregisterDevices
in interface AmazonSageMaker
deregisterDevicesRequest
- public DescribeActionResult describeAction(DescribeActionRequest request)
Describes an action.
describeAction
in interface AmazonSageMaker
describeActionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest request)
Returns a description of the specified algorithm that is in your account.
describeAlgorithm
in interface AmazonSageMaker
describeAlgorithmRequest
- public DescribeAppResult describeApp(DescribeAppRequest request)
Describes the app.
describeApp
in interface AmazonSageMaker
describeAppRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest request)
Describes an AppImageConfig.
describeAppImageConfig
in interface AmazonSageMaker
describeAppImageConfigRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeArtifactResult describeArtifact(DescribeArtifactRequest request)
Describes an artifact.
describeArtifact
in interface AmazonSageMaker
describeArtifactRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest request)
Returns information about an AutoML job created by calling CreateAutoMLJob.
AutoML jobs created by calling CreateAutoMLJobV2
cannot be described by DescribeAutoMLJob
.
describeAutoMLJob
in interface AmazonSageMaker
describeAutoMLJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeAutoMLJobV2Result describeAutoMLJobV2(DescribeAutoMLJobV2Request request)
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
describeAutoMLJobV2
in interface AmazonSageMaker
describeAutoMLJobV2Request
- ResourceNotFoundException
- Resource being access is not found.public DescribeClusterResult describeCluster(DescribeClusterRequest request)
Retrieves information of a SageMaker HyperPod cluster.
describeCluster
in interface AmazonSageMaker
describeClusterRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeClusterNodeResult describeClusterNode(DescribeClusterNodeRequest request)
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
describeClusterNode
in interface AmazonSageMaker
describeClusterNodeRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest request)
Gets details about the specified Git repository.
describeCodeRepository
in interface AmazonSageMaker
describeCodeRepositoryRequest
- public DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest request)
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
describeCompilationJob
in interface AmazonSageMaker
describeCompilationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeContextResult describeContext(DescribeContextRequest request)
Describes a context.
describeContext
in interface AmazonSageMaker
describeContextRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest request)
Gets the details of a data quality monitoring job definition.
describeDataQualityJobDefinition
in interface AmazonSageMaker
describeDataQualityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeDeviceResult describeDevice(DescribeDeviceRequest request)
Describes the device.
describeDevice
in interface AmazonSageMaker
describeDeviceRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest request)
A description of the fleet the device belongs to.
describeDeviceFleet
in interface AmazonSageMaker
describeDeviceFleetRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeDomainResult describeDomain(DescribeDomainRequest request)
The description of the domain.
describeDomain
in interface AmazonSageMaker
describeDomainRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeEdgeDeploymentPlanResult describeEdgeDeploymentPlan(DescribeEdgeDeploymentPlanRequest request)
Describes an edge deployment plan with deployment status per stage.
describeEdgeDeploymentPlan
in interface AmazonSageMaker
describeEdgeDeploymentPlanRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest request)
A description of edge packaging jobs.
describeEdgePackagingJob
in interface AmazonSageMaker
describeEdgePackagingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
Returns the description of an endpoint.
describeEndpoint
in interface AmazonSageMaker
describeEndpointRequest
- public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
Returns the description of an endpoint configuration created using the CreateEndpointConfig
API.
describeEndpointConfig
in interface AmazonSageMaker
describeEndpointConfigRequest
- public DescribeExperimentResult describeExperiment(DescribeExperimentRequest request)
Provides a list of an experiment's properties.
describeExperiment
in interface AmazonSageMaker
describeExperimentRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest request)
Use this operation to describe a FeatureGroup
. The response includes information on the creation
time, FeatureGroup
name, the unique identifier for each FeatureGroup
, and more.
describeFeatureGroup
in interface AmazonSageMaker
describeFeatureGroupRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeFeatureMetadataResult describeFeatureMetadata(DescribeFeatureMetadataRequest request)
Shows the metadata for a feature within a feature group.
describeFeatureMetadata
in interface AmazonSageMaker
describeFeatureMetadataRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest request)
Returns information about the specified flow definition.
describeFlowDefinition
in interface AmazonSageMaker
describeFlowDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeHubResult describeHub(DescribeHubRequest request)
Describes a hub.
describeHub
in interface AmazonSageMaker
describeHubRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeHubContentResult describeHubContent(DescribeHubContentRequest request)
Describe the content of a hub.
describeHubContent
in interface AmazonSageMaker
describeHubContentRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest request)
Returns information about the requested human task user interface (worker task template).
describeHumanTaskUi
in interface AmazonSageMaker
describeHumanTaskUiRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
describeHyperParameterTuningJob
in interface AmazonSageMaker
describeHyperParameterTuningJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeImageResult describeImage(DescribeImageRequest request)
Describes a SageMaker image.
describeImage
in interface AmazonSageMaker
describeImageRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest request)
Describes a version of a SageMaker image.
describeImageVersion
in interface AmazonSageMaker
describeImageVersionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeInferenceComponentResult describeInferenceComponent(DescribeInferenceComponentRequest request)
Returns information about an inference component.
describeInferenceComponent
in interface AmazonSageMaker
describeInferenceComponentRequest
- public DescribeInferenceExperimentResult describeInferenceExperiment(DescribeInferenceExperimentRequest request)
Returns details about an inference experiment.
describeInferenceExperiment
in interface AmazonSageMaker
describeInferenceExperimentRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeInferenceRecommendationsJobResult describeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest request)
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
describeInferenceRecommendationsJob
in interface AmazonSageMaker
describeInferenceRecommendationsJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest request)
Gets information about a labeling job.
describeLabelingJob
in interface AmazonSageMaker
describeLabelingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeLineageGroupResult describeLineageGroup(DescribeLineageGroupRequest request)
Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
describeLineageGroup
in interface AmazonSageMaker
describeLineageGroupRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeMlflowTrackingServerResult describeMlflowTrackingServer(DescribeMlflowTrackingServerRequest request)
Returns information about an MLflow Tracking Server.
describeMlflowTrackingServer
in interface AmazonSageMaker
describeMlflowTrackingServerRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeModelResult describeModel(DescribeModelRequest request)
Describes a model that you created using the CreateModel
API.
describeModel
in interface AmazonSageMaker
describeModelRequest
- public DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest request)
Returns a description of a model bias job definition.
describeModelBiasJobDefinition
in interface AmazonSageMaker
describeModelBiasJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeModelCardResult describeModelCard(DescribeModelCardRequest request)
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
describeModelCard
in interface AmazonSageMaker
describeModelCardRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeModelCardExportJobResult describeModelCardExportJob(DescribeModelCardExportJobRequest request)
Describes an Amazon SageMaker Model Card export job.
describeModelCardExportJob
in interface AmazonSageMaker
describeModelCardExportJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest request)
Returns a description of a model explainability job definition.
describeModelExplainabilityJobDefinition
in interface AmazonSageMaker
describeModelExplainabilityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest request)
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
describeModelPackage
in interface AmazonSageMaker
describeModelPackageRequest
- public DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest request)
Gets a description for the specified model group.
describeModelPackageGroup
in interface AmazonSageMaker
describeModelPackageGroupRequest
- public DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest request)
Returns a description of a model quality job definition.
describeModelQualityJobDefinition
in interface AmazonSageMaker
describeModelQualityJobDefinitionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)
Describes the schedule for a monitoring job.
describeMonitoringSchedule
in interface AmazonSageMaker
describeMonitoringScheduleRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
Returns information about a notebook instance.
describeNotebookInstance
in interface AmazonSageMaker
describeNotebookInstanceRequest
- public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
describeNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
describeNotebookInstanceLifecycleConfigRequest
- public DescribeOptimizationJobResult describeOptimizationJob(DescribeOptimizationJobRequest request)
Provides the properties of the specified optimization job.
describeOptimizationJob
in interface AmazonSageMaker
describeOptimizationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribePipelineResult describePipeline(DescribePipelineRequest request)
Describes the details of a pipeline.
describePipeline
in interface AmazonSageMaker
describePipelineRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest request)
Describes the details of an execution's pipeline definition.
describePipelineDefinitionForExecution
in interface AmazonSageMaker
describePipelineDefinitionForExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest request)
Describes the details of a pipeline execution.
describePipelineExecution
in interface AmazonSageMaker
describePipelineExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest request)
Returns a description of a processing job.
describeProcessingJob
in interface AmazonSageMaker
describeProcessingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeProjectResult describeProject(DescribeProjectRequest request)
Describes the details of a project.
describeProject
in interface AmazonSageMaker
describeProjectRequest
- public DescribeSpaceResult describeSpace(DescribeSpaceRequest request)
Describes the space.
describeSpace
in interface AmazonSageMaker
describeSpaceRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeStudioLifecycleConfigResult describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest request)
Describes the Amazon SageMaker Studio Lifecycle Configuration.
describeStudioLifecycleConfig
in interface AmazonSageMaker
describeStudioLifecycleConfigRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
describeSubscribedWorkteam
in interface AmazonSageMaker
describeSubscribedWorkteamRequest
- public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails,
TrainingJobStatus
is Failed
and, depending on the FailureReason
,
attributes like TrainingStartTime
, TrainingTimeInSeconds
, TrainingEndTime
,
and BillableTimeInSeconds
may not be present in the response.
describeTrainingJob
in interface AmazonSageMaker
describeTrainingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
Returns information about a transform job.
describeTransformJob
in interface AmazonSageMaker
describeTransformJobRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeTrialResult describeTrial(DescribeTrialRequest request)
Provides a list of a trial's properties.
describeTrial
in interface AmazonSageMaker
describeTrialRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest request)
Provides a list of a trials component's properties.
describeTrialComponent
in interface AmazonSageMaker
describeTrialComponentRequest
- ResourceNotFoundException
- Resource being access is not found.public DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest request)
Describes a user profile. For more information, see CreateUserProfile
.
describeUserProfile
in interface AmazonSageMaker
describeUserProfileRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest request)
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
This operation applies only to private workforces.
describeWorkforce
in interface AmazonSageMaker
describeWorkforceRequest
- public DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest request)
Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
describeWorkteam
in interface AmazonSageMaker
describeWorkteamRequest
- public DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest request)
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
disableSagemakerServicecatalogPortfolio
in interface AmazonSageMaker
disableSagemakerServicecatalogPortfolioRequest
- public DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest request)
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify
ExperimentTrialComponent
for the Resource
parameter. The list appears in the response
under Results.TrialComponent.Parents
.
disassociateTrialComponent
in interface AmazonSageMaker
disassociateTrialComponentRequest
- ResourceNotFoundException
- Resource being access is not found.public EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest request)
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
enableSagemakerServicecatalogPortfolio
in interface AmazonSageMaker
enableSagemakerServicecatalogPortfolioRequest
- public GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest request)
Describes a fleet.
getDeviceFleetReport
in interface AmazonSageMaker
getDeviceFleetReportRequest
- public GetLineageGroupPolicyResult getLineageGroupPolicy(GetLineageGroupPolicyRequest request)
The resource policy for the lineage group.
getLineageGroupPolicy
in interface AmazonSageMaker
getLineageGroupPolicyRequest
- ResourceNotFoundException
- Resource being access is not found.public GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest request)
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
getModelPackageGroupPolicy
in interface AmazonSageMaker
getModelPackageGroupPolicyRequest
- public GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest request)
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
getSagemakerServicecatalogPortfolioStatus
in interface AmazonSageMaker
getSagemakerServicecatalogPortfolioStatusRequest
- public GetScalingConfigurationRecommendationResult getScalingConfigurationRecommendation(GetScalingConfigurationRecommendationRequest request)
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
getScalingConfigurationRecommendation
in interface AmazonSageMaker
getScalingConfigurationRecommendationRequest
- ResourceNotFoundException
- Resource being access is not found.public GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest request)
An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible
matches for the property name to use in Search
queries. Provides suggestions for
HyperParameters
, Tags
, and Metrics
.
getSearchSuggestions
in interface AmazonSageMaker
getSearchSuggestionsRequest
- public ImportHubContentResult importHubContent(ImportHubContentRequest request)
Import hub content.
importHubContent
in interface AmazonSageMaker
importHubContentRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public ListActionsResult listActions(ListActionsRequest request)
Lists the actions in your account and their properties.
listActions
in interface AmazonSageMaker
listActionsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest request)
Lists the machine learning algorithms that have been created.
listAlgorithms
in interface AmazonSageMaker
listAlgorithmsRequest
- public ListAliasesResult listAliases(ListAliasesRequest request)
Lists the aliases of a specified image or image version.
listAliases
in interface AmazonSageMaker
listAliasesRequest
- ResourceNotFoundException
- Resource being access is not found.public ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest request)
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
listAppImageConfigs
in interface AmazonSageMaker
listAppImageConfigsRequest
- public ListAppsResult listApps(ListAppsRequest request)
Lists apps.
listApps
in interface AmazonSageMaker
listAppsRequest
- public ListArtifactsResult listArtifacts(ListArtifactsRequest request)
Lists the artifacts in your account and their properties.
listArtifacts
in interface AmazonSageMaker
listArtifactsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListAssociationsResult listAssociations(ListAssociationsRequest request)
Lists the associations in your account and their properties.
listAssociations
in interface AmazonSageMaker
listAssociationsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest request)
Request a list of jobs.
listAutoMLJobs
in interface AmazonSageMaker
listAutoMLJobsRequest
- public ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)
List the candidates created for the job.
listCandidatesForAutoMLJob
in interface AmazonSageMaker
listCandidatesForAutoMLJobRequest
- ResourceNotFoundException
- Resource being access is not found.public ListClusterNodesResult listClusterNodes(ListClusterNodesRequest request)
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
listClusterNodes
in interface AmazonSageMaker
listClusterNodesRequest
- ResourceNotFoundException
- Resource being access is not found.public ListClustersResult listClusters(ListClustersRequest request)
Retrieves the list of SageMaker HyperPod clusters.
listClusters
in interface AmazonSageMaker
listClustersRequest
- public ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest request)
Gets a list of the Git repositories in your account.
listCodeRepositories
in interface AmazonSageMaker
listCodeRepositoriesRequest
- public ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest request)
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
listCompilationJobs
in interface AmazonSageMaker
listCompilationJobsRequest
- public ListContextsResult listContexts(ListContextsRequest request)
Lists the contexts in your account and their properties.
listContexts
in interface AmazonSageMaker
listContextsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest request)
Lists the data quality job definitions in your account.
listDataQualityJobDefinitions
in interface AmazonSageMaker
listDataQualityJobDefinitionsRequest
- public ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest request)
Returns a list of devices in the fleet.
listDeviceFleets
in interface AmazonSageMaker
listDeviceFleetsRequest
- public ListDevicesResult listDevices(ListDevicesRequest request)
A list of devices.
listDevices
in interface AmazonSageMaker
listDevicesRequest
- public ListDomainsResult listDomains(ListDomainsRequest request)
Lists the domains.
listDomains
in interface AmazonSageMaker
listDomainsRequest
- public ListEdgeDeploymentPlansResult listEdgeDeploymentPlans(ListEdgeDeploymentPlansRequest request)
Lists all edge deployment plans.
listEdgeDeploymentPlans
in interface AmazonSageMaker
listEdgeDeploymentPlansRequest
- public ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest request)
Returns a list of edge packaging jobs.
listEdgePackagingJobs
in interface AmazonSageMaker
listEdgePackagingJobsRequest
- public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
Lists endpoint configurations.
listEndpointConfigs
in interface AmazonSageMaker
listEndpointConfigsRequest
- public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
Lists endpoints.
listEndpoints
in interface AmazonSageMaker
listEndpointsRequest
- public ListExperimentsResult listExperiments(ListExperimentsRequest request)
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
listExperiments
in interface AmazonSageMaker
listExperimentsRequest
- public ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest request)
List FeatureGroup
s based on given filter and order.
listFeatureGroups
in interface AmazonSageMaker
listFeatureGroupsRequest
- public ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest request)
Returns information about the flow definitions in your account.
listFlowDefinitions
in interface AmazonSageMaker
listFlowDefinitionsRequest
- public ListHubContentVersionsResult listHubContentVersions(ListHubContentVersionsRequest request)
List hub content versions.
listHubContentVersions
in interface AmazonSageMaker
listHubContentVersionsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListHubContentsResult listHubContents(ListHubContentsRequest request)
List the contents of a hub.
listHubContents
in interface AmazonSageMaker
listHubContentsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListHubsResult listHubs(ListHubsRequest request)
List all existing hubs.
listHubs
in interface AmazonSageMaker
listHubsRequest
- public ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest request)
Returns information about the human task user interfaces in your account.
listHumanTaskUis
in interface AmazonSageMaker
listHumanTaskUisRequest
- public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobs
in interface AmazonSageMaker
listHyperParameterTuningJobsRequest
- public ListImageVersionsResult listImageVersions(ListImageVersionsRequest request)
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
listImageVersions
in interface AmazonSageMaker
listImageVersionsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListImagesResult listImages(ListImagesRequest request)
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
listImages
in interface AmazonSageMaker
listImagesRequest
- public ListInferenceComponentsResult listInferenceComponents(ListInferenceComponentsRequest request)
Lists the inference components in your account and their properties.
listInferenceComponents
in interface AmazonSageMaker
listInferenceComponentsRequest
- public ListInferenceExperimentsResult listInferenceExperiments(ListInferenceExperimentsRequest request)
Returns the list of all inference experiments.
listInferenceExperiments
in interface AmazonSageMaker
listInferenceExperimentsRequest
- public ListInferenceRecommendationsJobStepsResult listInferenceRecommendationsJobSteps(ListInferenceRecommendationsJobStepsRequest request)
Returns a list of the subtasks for an Inference Recommender job.
The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
listInferenceRecommendationsJobSteps
in interface AmazonSageMaker
listInferenceRecommendationsJobStepsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListInferenceRecommendationsJobsResult listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest request)
Lists recommendation jobs that satisfy various filters.
listInferenceRecommendationsJobs
in interface AmazonSageMaker
listInferenceRecommendationsJobsRequest
- public ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest request)
Gets a list of labeling jobs.
listLabelingJobs
in interface AmazonSageMaker
listLabelingJobsRequest
- public ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)
Gets a list of labeling jobs assigned to a specified work team.
listLabelingJobsForWorkteam
in interface AmazonSageMaker
listLabelingJobsForWorkteamRequest
- ResourceNotFoundException
- Resource being access is not found.public ListLineageGroupsResult listLineageGroups(ListLineageGroupsRequest request)
A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
listLineageGroups
in interface AmazonSageMaker
listLineageGroupsRequest
- public ListMlflowTrackingServersResult listMlflowTrackingServers(ListMlflowTrackingServersRequest request)
Lists all MLflow Tracking Servers.
listMlflowTrackingServers
in interface AmazonSageMaker
listMlflowTrackingServersRequest
- public ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest request)
Lists model bias jobs definitions that satisfy various filters.
listModelBiasJobDefinitions
in interface AmazonSageMaker
listModelBiasJobDefinitionsRequest
- public ListModelCardExportJobsResult listModelCardExportJobs(ListModelCardExportJobsRequest request)
List the export jobs for the Amazon SageMaker Model Card.
listModelCardExportJobs
in interface AmazonSageMaker
listModelCardExportJobsRequest
- public ListModelCardVersionsResult listModelCardVersions(ListModelCardVersionsRequest request)
List existing versions of an Amazon SageMaker Model Card.
listModelCardVersions
in interface AmazonSageMaker
listModelCardVersionsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListModelCardsResult listModelCards(ListModelCardsRequest request)
List existing model cards.
listModelCards
in interface AmazonSageMaker
listModelCardsRequest
- public ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest request)
Lists model explainability job definitions that satisfy various filters.
listModelExplainabilityJobDefinitions
in interface AmazonSageMaker
listModelExplainabilityJobDefinitionsRequest
- public ListModelMetadataResult listModelMetadata(ListModelMetadataRequest request)
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
listModelMetadata
in interface AmazonSageMaker
listModelMetadataRequest
- public ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest request)
Gets a list of the model groups in your Amazon Web Services account.
listModelPackageGroups
in interface AmazonSageMaker
listModelPackageGroupsRequest
- public ListModelPackagesResult listModelPackages(ListModelPackagesRequest request)
Lists the model packages that have been created.
listModelPackages
in interface AmazonSageMaker
listModelPackagesRequest
- public ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest request)
Gets a list of model quality monitoring job definitions in your account.
listModelQualityJobDefinitions
in interface AmazonSageMaker
listModelQualityJobDefinitionsRequest
- public ListModelsResult listModels(ListModelsRequest request)
Lists models created with the CreateModel
API.
listModels
in interface AmazonSageMaker
listModelsRequest
- public ListMonitoringAlertHistoryResult listMonitoringAlertHistory(ListMonitoringAlertHistoryRequest request)
Gets a list of past alerts in a model monitoring schedule.
listMonitoringAlertHistory
in interface AmazonSageMaker
listMonitoringAlertHistoryRequest
- ResourceNotFoundException
- Resource being access is not found.public ListMonitoringAlertsResult listMonitoringAlerts(ListMonitoringAlertsRequest request)
Gets the alerts for a single monitoring schedule.
listMonitoringAlerts
in interface AmazonSageMaker
listMonitoringAlertsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest request)
Returns list of all monitoring job executions.
listMonitoringExecutions
in interface AmazonSageMaker
listMonitoringExecutionsRequest
- public ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest request)
Returns list of all monitoring schedules.
listMonitoringSchedules
in interface AmazonSageMaker
listMonitoringSchedulesRequest
- public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigs
in interface AmazonSageMaker
listNotebookInstanceLifecycleConfigsRequest
- public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
listNotebookInstances
in interface AmazonSageMaker
listNotebookInstancesRequest
- public ListOptimizationJobsResult listOptimizationJobs(ListOptimizationJobsRequest request)
Lists the optimization jobs in your account and their properties.
listOptimizationJobs
in interface AmazonSageMaker
listOptimizationJobsRequest
- public ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest request)
Gets a list of PipeLineExecutionStep
objects.
listPipelineExecutionSteps
in interface AmazonSageMaker
listPipelineExecutionStepsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest request)
Gets a list of the pipeline executions.
listPipelineExecutions
in interface AmazonSageMaker
listPipelineExecutionsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListPipelineParametersForExecutionResult listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest request)
Gets a list of parameters for a pipeline execution.
listPipelineParametersForExecution
in interface AmazonSageMaker
listPipelineParametersForExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.public ListPipelinesResult listPipelines(ListPipelinesRequest request)
Gets a list of pipelines.
listPipelines
in interface AmazonSageMaker
listPipelinesRequest
- public ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest request)
Lists processing jobs that satisfy various filters.
listProcessingJobs
in interface AmazonSageMaker
listProcessingJobsRequest
- public ListProjectsResult listProjects(ListProjectsRequest request)
Gets a list of the projects in an Amazon Web Services account.
listProjects
in interface AmazonSageMaker
listProjectsRequest
- public ListResourceCatalogsResult listResourceCatalogs(ListResourceCatalogsRequest request)
Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of
ResourceCatalog
s viewable is 1000.
listResourceCatalogs
in interface AmazonSageMaker
listResourceCatalogsRequest
- public ListSpacesResult listSpaces(ListSpacesRequest request)
Lists spaces.
listSpaces
in interface AmazonSageMaker
listSpacesRequest
- public ListStageDevicesResult listStageDevices(ListStageDevicesRequest request)
Lists devices allocated to the stage, containing detailed device information and deployment status.
listStageDevices
in interface AmazonSageMaker
listStageDevicesRequest
- public ListStudioLifecycleConfigsResult listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest request)
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
listStudioLifecycleConfigs
in interface AmazonSageMaker
listStudioLifecycleConfigsRequest
- ResourceInUseException
- Resource being accessed is in use.public ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be
empty if no work team satisfies the filter specified in the NameContains
parameter.
listSubscribedWorkteams
in interface AmazonSageMaker
listSubscribedWorkteamsRequest
- public ListTagsResult listTags(ListTagsRequest request)
Returns the tags for the specified SageMaker resource.
listTags
in interface AmazonSageMaker
listTagsRequest
- public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
Lists training jobs.
When StatusEquals
and MaxResults
are set at the same time, the MaxResults
number of training jobs are first retrieved ignoring the StatusEquals
parameter and then they are
filtered by the StatusEquals
parameter, which is returned as a response.
For example, if ListTrainingJobs
is invoked with the following parameters:
{ ... MaxResults: 100, StatusEquals: InProgress ... }
First, 100 trainings jobs with any status, including those other than InProgress
, are selected
(sorted according to the creation time, from the most current to the oldest). Next, those with a status of
InProgress
are returned.
You can quickly test the API using the following Amazon Web Services CLI code.
aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
listTrainingJobs
in interface AmazonSageMaker
listTrainingJobsRequest
- public ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJob
in interface AmazonSageMaker
listTrainingJobsForHyperParameterTuningJobRequest
- ResourceNotFoundException
- Resource being access is not found.public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
Lists transform jobs.
listTransformJobs
in interface AmazonSageMaker
listTransformJobsRequest
- public ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest request)
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
ExperimentName
SourceArn
TrialName
listTrialComponents
in interface AmazonSageMaker
listTrialComponentsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListTrialsResult listTrials(ListTrialsRequest request)
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
listTrials
in interface AmazonSageMaker
listTrialsRequest
- ResourceNotFoundException
- Resource being access is not found.public ListUserProfilesResult listUserProfiles(ListUserProfilesRequest request)
Lists user profiles.
listUserProfiles
in interface AmazonSageMaker
listUserProfilesRequest
- public ListWorkforcesResult listWorkforces(ListWorkforcesRequest request)
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
listWorkforces
in interface AmazonSageMaker
listWorkforcesRequest
- public ListWorkteamsResult listWorkteams(ListWorkteamsRequest request)
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team
satisfies the filter specified in the NameContains
parameter.
listWorkteams
in interface AmazonSageMaker
listWorkteamsRequest
- public PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest request)
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
putModelPackageGroupPolicy
in interface AmazonSageMaker
putModelPackageGroupPolicyRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public QueryLineageResult queryLineage(QueryLineageRequest request)
Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
queryLineage
in interface AmazonSageMaker
queryLineageRequest
- ResourceNotFoundException
- Resource being access is not found.public RegisterDevicesResult registerDevices(RegisterDevicesRequest request)
Register devices.
registerDevices
in interface AmazonSageMaker
registerDevicesRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest request)
Renders the UI template so that you can preview the worker's experience.
renderUiTemplate
in interface AmazonSageMaker
renderUiTemplateRequest
- ResourceNotFoundException
- Resource being access is not found.public RetryPipelineExecutionResult retryPipelineExecution(RetryPipelineExecutionRequest request)
Retry the execution of the pipeline.
retryPipelineExecution
in interface AmazonSageMaker
retryPipelineExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public SearchResult search(SearchRequest request)
Finds SageMaker resources that match a search query. Matching resources are returned as a list of
SearchRecord
objects in the response. You can sort the search results by any resource property in a
ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
search
in interface AmazonSageMaker
searchRequest
- public SendPipelineExecutionStepFailureResult sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest request)
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
sendPipelineExecutionStepFailure
in interface AmazonSageMaker
sendPipelineExecutionStepFailureRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public SendPipelineExecutionStepSuccessResult sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest request)
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
sendPipelineExecutionStepSuccess
in interface AmazonSageMaker
sendPipelineExecutionStepSuccessRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public StartEdgeDeploymentStageResult startEdgeDeploymentStage(StartEdgeDeploymentStageRequest request)
Starts a stage in an edge deployment plan.
startEdgeDeploymentStage
in interface AmazonSageMaker
startEdgeDeploymentStageRequest
- public StartInferenceExperimentResult startInferenceExperiment(StartInferenceExperimentRequest request)
Starts an inference experiment.
startInferenceExperiment
in interface AmazonSageMaker
startInferenceExperimentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public StartMlflowTrackingServerResult startMlflowTrackingServer(StartMlflowTrackingServerRequest request)
Programmatically start an MLflow Tracking Server.
startMlflowTrackingServer
in interface AmazonSageMaker
startMlflowTrackingServerRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest request)
Starts a previously stopped monitoring schedule.
By default, when you successfully create a new schedule, the status of a monitoring schedule is
scheduled
.
startMonitoringSchedule
in interface AmazonSageMaker
startMonitoringScheduleRequest
- ResourceNotFoundException
- Resource being access is not found.public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
After configuring the notebook instance, SageMaker sets the notebook instance status to InService
. A
notebook instance's status must be InService
before you can connect to your Jupyter notebook.
startNotebookInstance
in interface AmazonSageMaker
startNotebookInstanceRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest request)
Starts a pipeline execution.
startPipelineExecution
in interface AmazonSageMaker
startPipelineExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest request)
A method for forcing a running job to shut down.
stopAutoMLJob
in interface AmazonSageMaker
stopAutoMLJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest request)
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a StopCompilationJob
request, Amazon SageMaker changes the
CompilationJobStatus
of the job to Stopping
. After Amazon SageMaker stops the job, it
sets the CompilationJobStatus
to Stopped
.
stopCompilationJob
in interface AmazonSageMaker
stopCompilationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopEdgeDeploymentStageResult stopEdgeDeploymentStage(StopEdgeDeploymentStageRequest request)
Stops a stage in an edge deployment plan.
stopEdgeDeploymentStage
in interface AmazonSageMaker
stopEdgeDeploymentStageRequest
- public StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest request)
Request to stop an edge packaging job.
stopEdgePackagingJob
in interface AmazonSageMaker
stopEdgePackagingJobRequest
- public StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All
data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning
job moves to the Stopped
state, it releases all reserved resources for the tuning job.
stopHyperParameterTuningJob
in interface AmazonSageMaker
stopHyperParameterTuningJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopInferenceExperimentResult stopInferenceExperiment(StopInferenceExperimentRequest request)
Stops an inference experiment.
stopInferenceExperiment
in interface AmazonSageMaker
stopInferenceExperimentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public StopInferenceRecommendationsJobResult stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest request)
Stops an Inference Recommender job.
stopInferenceRecommendationsJob
in interface AmazonSageMaker
stopInferenceRecommendationsJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest request)
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
stopLabelingJob
in interface AmazonSageMaker
stopLabelingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopMlflowTrackingServerResult stopMlflowTrackingServer(StopMlflowTrackingServerRequest request)
Programmatically stop an MLflow Tracking Server.
stopMlflowTrackingServer
in interface AmazonSageMaker
stopMlflowTrackingServerRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest request)
Stops a previously started monitoring schedule.
stopMonitoringSchedule
in interface AmazonSageMaker
stopMonitoringScheduleRequest
- ResourceNotFoundException
- Resource being access is not found.public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume
from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when
you call StopNotebookInstance
.
To access data on the ML storage volume for a notebook instance that has been terminated, call the
StartNotebookInstance
API. StartNotebookInstance
launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can continue your work.
stopNotebookInstance
in interface AmazonSageMaker
stopNotebookInstanceRequest
- public StopOptimizationJobResult stopOptimizationJob(StopOptimizationJobRequest request)
Ends a running inference optimization job.
stopOptimizationJob
in interface AmazonSageMaker
stopOptimizationJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest request)
Stops a pipeline execution.
Callback Step
A pipeline execution won't stop while a callback step is running. When you call
StopPipelineExecution
on a pipeline execution with a running callback step, SageMaker Pipelines
sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a
"Status" field which is set to "Stopping".
You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource
cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess
or
SendPipelineExecutionStepFailure
.
Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
Lambda Step
A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the
lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the
pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then
stops. If the Lambda function finishes, the pipeline execution status is Stopped
. If the timeout is
hit the pipeline execution status is Failed
.
stopPipelineExecution
in interface AmazonSageMaker
stopPipelineExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest request)
Stops a processing job.
stopProcessingJob
in interface AmazonSageMaker
stopProcessingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM
signal, which delays
job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the
results of the training is not lost.
When it receives a StopTrainingJob
request, SageMaker changes the status of the job to
Stopping
. After SageMaker stops the job, it sets the status to Stopped
.
stopTrainingJob
in interface AmazonSageMaker
stopTrainingJobRequest
- ResourceNotFoundException
- Resource being access is not found.public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
Stops a batch transform job.
When Amazon SageMaker receives a StopTransformJob
request, the status of the job changes to
Stopping
. After Amazon SageMaker stops the job, the status is set to Stopped
. When you
stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
stopTransformJob
in interface AmazonSageMaker
stopTransformJobRequest
- ResourceNotFoundException
- Resource being access is not found.public UpdateActionResult updateAction(UpdateActionRequest request)
Updates an action.
updateAction
in interface AmazonSageMaker
updateActionRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest request)
Updates the properties of an AppImageConfig.
updateAppImageConfig
in interface AmazonSageMaker
updateAppImageConfigRequest
- ResourceNotFoundException
- Resource being access is not found.public UpdateArtifactResult updateArtifact(UpdateArtifactRequest request)
Updates an artifact.
updateArtifact
in interface AmazonSageMaker
updateArtifactRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateClusterResult updateCluster(UpdateClusterRequest request)
Updates a SageMaker HyperPod cluster.
updateCluster
in interface AmazonSageMaker
updateClusterRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateClusterSoftwareResult updateClusterSoftware(UpdateClusterSoftwareRequest request)
Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
updateClusterSoftware
in interface AmazonSageMaker
updateClusterSoftwareRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest request)
Updates the specified Git repository with the specified values.
updateCodeRepository
in interface AmazonSageMaker
updateCodeRepositoryRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateContextResult updateContext(UpdateContextRequest request)
Updates a context.
updateContext
in interface AmazonSageMaker
updateContextRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest request)
Updates a fleet of devices.
updateDeviceFleet
in interface AmazonSageMaker
updateDeviceFleetRequest
- ResourceInUseException
- Resource being accessed is in use.public UpdateDevicesResult updateDevices(UpdateDevicesRequest request)
Updates one or more devices in a fleet.
updateDevices
in interface AmazonSageMaker
updateDevicesRequest
- public UpdateDomainResult updateDomain(UpdateDomainRequest request)
Updates the default settings for new user profiles in the domain.
updateDomain
in interface AmazonSageMaker
updateDomainRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
Deploys the EndpointConfig
specified in the request to a new fleet of instances. SageMaker shifts
endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances
using the previous EndpointConfig
(there is no availability loss). For more information about how to
control the update and traffic shifting process, see Update models in
production.
When SageMaker receives the request, it sets the endpoint status to Updating
. After updating the
endpoint, it sets the status to InService
. To check the status of an endpoint, use the DescribeEndpoint
API.
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. To
update an endpoint, you must create a new EndpointConfig
.
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.
updateEndpoint
in interface AmazonSageMaker
updateEndpointRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant
associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to
Updating
. After updating the endpoint, it sets the status to InService
. To check the
status of an endpoint, use the DescribeEndpoint
API.
updateEndpointWeightsAndCapacities
in interface AmazonSageMaker
updateEndpointWeightsAndCapacitiesRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateExperimentResult updateExperiment(UpdateExperimentRequest request)
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
updateExperiment
in interface AmazonSageMaker
updateExperimentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateFeatureGroupResult updateFeatureGroup(UpdateFeatureGroupRequest request)
Updates the feature group by either adding features or updating the online store configuration. Use one of the
following request parameters at a time while using the UpdateFeatureGroup
API.
You can add features for your feature group using the FeatureAdditions
request parameter. Features
cannot be removed from a feature group.
You can update the online store configuration by using the OnlineStoreConfig
request parameter. If a
TtlDuration
is specified, the default TtlDuration
applies for all records added to the
feature group after the feature group is updated. If a record level TtlDuration
exists from
using the PutRecord
API, the record level TtlDuration
applies to that record instead of
the default TtlDuration
. To remove the default TtlDuration
from an existing feature
group, use the UpdateFeatureGroup
API and set the TtlDuration
Unit
and
Value
to null
.
updateFeatureGroup
in interface AmazonSageMaker
updateFeatureGroupRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateFeatureMetadataResult updateFeatureMetadata(UpdateFeatureMetadataRequest request)
Updates the description and parameters of the feature group.
updateFeatureMetadata
in interface AmazonSageMaker
updateFeatureMetadataRequest
- ResourceNotFoundException
- Resource being access is not found.public UpdateHubResult updateHub(UpdateHubRequest request)
Update a hub.
updateHub
in interface AmazonSageMaker
updateHubRequest
- ResourceNotFoundException
- Resource being access is not found.public UpdateImageResult updateImage(UpdateImageRequest request)
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
updateImage
in interface AmazonSageMaker
updateImageRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public UpdateImageVersionResult updateImageVersion(UpdateImageVersionRequest request)
Updates the properties of a SageMaker image version.
updateImageVersion
in interface AmazonSageMaker
updateImageVersionRequest
- ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public UpdateInferenceComponentResult updateInferenceComponent(UpdateInferenceComponentRequest request)
Updates an inference component.
updateInferenceComponent
in interface AmazonSageMaker
updateInferenceComponentRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateInferenceComponentRuntimeConfigResult updateInferenceComponentRuntimeConfig(UpdateInferenceComponentRuntimeConfigRequest request)
Runtime settings for a model that is deployed with an inference component.
updateInferenceComponentRuntimeConfig
in interface AmazonSageMaker
updateInferenceComponentRuntimeConfigRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateInferenceExperimentResult updateInferenceExperiment(UpdateInferenceExperimentRequest request)
Updates an inference experiment that you created. The status of the inference experiment has to be either
Created
, Running
. For more information on the status of an inference experiment, see
DescribeInferenceExperiment.
updateInferenceExperiment
in interface AmazonSageMaker
updateInferenceExperimentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateMlflowTrackingServerResult updateMlflowTrackingServer(UpdateMlflowTrackingServerRequest request)
Updates properties of an existing MLflow Tracking Server.
updateMlflowTrackingServer
in interface AmazonSageMaker
updateMlflowTrackingServerRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateModelCardResult updateModelCard(UpdateModelCardRequest request)
Update an Amazon SageMaker Model Card.
You cannot update both model card content and model card status in a single call.
updateModelCard
in interface AmazonSageMaker
updateModelCardRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest request)
Updates a versioned model.
updateModelPackage
in interface AmazonSageMaker
updateModelPackageRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateMonitoringAlertResult updateMonitoringAlert(UpdateMonitoringAlertRequest request)
Update the parameters of a model monitor alert.
updateMonitoringAlert
in interface AmazonSageMaker
updateMonitoringAlertRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)
Updates a previously created schedule.
updateMonitoringSchedule
in interface AmazonSageMaker
updateMonitoringScheduleRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceNotFoundException
- Resource being access is not found.public UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
updateNotebookInstance
in interface AmazonSageMaker
updateNotebookInstanceRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
updateNotebookInstanceLifecycleConfigRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdatePipelineResult updatePipeline(UpdatePipelineRequest request)
Updates a pipeline.
updatePipeline
in interface AmazonSageMaker
updatePipelineRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest request)
Updates a pipeline execution.
updatePipelineExecution
in interface AmazonSageMaker
updatePipelineExecutionRequest
- ResourceNotFoundException
- Resource being access is not found.ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateProjectResult updateProject(UpdateProjectRequest request)
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
You must not update a project that is in use. If you update the
ServiceCatalogProvisioningUpdateDetails
of a project that is active or being created, or updated,
you may lose resources already created by the project.
updateProject
in interface AmazonSageMaker
updateProjectRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateSpaceResult updateSpace(UpdateSpaceRequest request)
Updates the settings of a space.
updateSpace
in interface AmazonSageMaker
updateSpaceRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest request)
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
updateTrainingJob
in interface AmazonSageMaker
updateTrainingJobRequest
- ResourceNotFoundException
- Resource being access is not found.ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public UpdateTrialResult updateTrial(UpdateTrialRequest request)
Updates the display name of a trial.
updateTrial
in interface AmazonSageMaker
updateTrialRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest request)
Updates one or more properties of a trial component.
updateTrialComponent
in interface AmazonSageMaker
updateTrialComponentRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.ResourceNotFoundException
- Resource being access is not found.public UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest request)
Updates a user profile.
updateUserProfile
in interface AmazonSageMaker
updateUserProfileRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.ResourceInUseException
- Resource being accessed is in use.ResourceNotFoundException
- Resource being access is not found.public UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest request)
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
The worker portal is now supported in VPC and public internet.
Use SourceIpConfig
to restrict worker access to tasks to a specific range of IP addresses. You
specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't
restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks
using any IP address outside the specified range are denied and get a Not Found
error message on the
worker portal.
To restrict access to all the workers in public internet, add the SourceIpConfig
CIDR value as
"10.0.0.0/16".
Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.
Use OidcConfig
to update the configuration of a workforce created using your own OIDC IdP.
You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.
This operation only applies to private workforces.
updateWorkforce
in interface AmazonSageMaker
updateWorkforceRequest
- ConflictException
- There was a conflict when you attempted to modify a SageMaker entity such as an Experiment
or Artifact
.public UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest request)
Updates an existing work team with new member definitions or description.
updateWorkteam
in interface AmazonSageMaker
updateWorkteamRequest
- ResourceLimitExceededException
- You have exceeded an SageMaker resource limit. For example, you might have too many training jobs
created.public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing the request.
getCachedResponseMetadata
in interface AmazonSageMaker
request
- The originally executed requestpublic AmazonSageMakerWaiters waiters()
waiters
in interface AmazonSageMaker
public void shutdown()
AmazonWebServiceClient
shutdown
in interface AmazonSageMaker
shutdown
in class AmazonWebServiceClient