@ThreadSafe @Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AmazonSageMakerClient extends AmazonWebServiceClient implements AmazonSageMaker
Definition of the public APIs exposed by SageMaker
LOGGING_AWS_REQUEST_METRICENDPOINT_PREFIXaddRequestHandler, addRequestHandler, configureRegion, getClientConfiguration, getEndpointPrefix, getMonitoringListeners, getRequestMetricsCollector, getServiceName, getSignerByURI, getSignerOverride, getSignerRegionOverride, getTimeOffset, makeImmutable, removeRequestHandler, removeRequestHandler, setEndpoint, setEndpoint, setRegion, setServiceNameIntern, setSignerRegionOverride, setTimeOffset, withEndpoint, withRegion, withRegion, withTimeOffsetpublic static AmazonSageMakerClientBuilder builder()
public AddTagsResult addTags(AddTagsRequest request)
Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, 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 Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
addTags in interface AmazonSageMakeraddTagsRequest - public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API only for hosting models using Amazon SageMaker hosting services.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it
creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.
createEndpoint in interface AmazonSageMakercreateEndpointRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
configuration, you identify one or more models, created using the CreateModel API, to deploy and the
resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define one or more ProductionVariants, each of which identifies a model. Each
ProductionVariant parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
createEndpointConfig in interface AmazonSageMakercreateEndpointConfigRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
Starts a hyperparameter tuning job.
createHyperParameterTuningJob in interface AmazonSageMakercreateHyperParameterTuningJobRequest - ResourceInUseException - Resource being accessed is in use.ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public CreateModelResult createModel(CreateModelRequest request)
Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then
create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
createModel in interface AmazonSageMakercreateModelRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run.
Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model
training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC,
which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon
SageMaker attaches the security group that you specified in the request to the network interface that it creates
in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this
instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
groups allow it.
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstance in interface AmazonSageMakercreateNotebookInstanceRequest - ResourceLimitExceededException - You have exceeded an Amazon 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 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 notebook-lifecycle-config.
createNotebookInstanceLifecycleConfig in interface AmazonSageMakercreateNotebookInstanceLifecycleConfigRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. To
restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in
the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook
instance. 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 nbi-ip-filter.
createPresignedNotebookInstanceUrl in interface AmazonSageMakercreatePresignedNotebookInstanceUrlRequest - public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to influence the quality of the final
model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.
OutputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition - Sets a duration for training. Use this parameter to cap model training costs.
For more information about Amazon SageMaker, see How It Works.
createTrainingJob in interface AmazonSageMakercreateTrainingJobRequest - ResourceInUseException - Resource being accessed is in use.ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.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 AWS Region in an
AWS account.
ModelName - Identifies the model to use. ModelName must be the name of an existing
Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
CreateModel.
TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is
stored.
TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results from the transform job.
TransformResources - Identifies the ML compute instances for the transform job.
For more information about how batch transformation works Amazon SageMaker, see How It Works.
createTransformJob in interface AmazonSageMakercreateTransformJobRequest - ResourceInUseException - Resource being accessed is in use.ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
deleteEndpoint in interface AmazonSageMakerdeleteEndpointRequest - 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.
deleteEndpointConfig in interface AmazonSageMakerdeleteEndpointConfigRequest - public DeleteModelResult deleteModel(DeleteModelRequest request)
Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon
SageMaker when you called the CreateModel API. It does not
delete model artifacts, inference code, or the IAM role that you specified when creating the model.
deleteModel in interface AmazonSageMakerdeleteModelRequest - public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the
StopNotebookInstance API.
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
deleteNotebookInstance in interface AmazonSageMakerdeleteNotebookInstanceRequest - public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfig in interface AmazonSageMakerdeleteNotebookInstanceLifecycleConfigRequest - public DeleteTagsResult deleteTags(DeleteTagsRequest request)
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags API.
deleteTags in interface AmazonSageMakerdeleteTagsRequest - public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
Returns the description of an endpoint.
describeEndpoint in interface AmazonSageMakerdescribeEndpointRequest - public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
describeEndpointConfig in interface AmazonSageMakerdescribeEndpointConfigRequest - public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
Gets a description of a hyperparameter tuning job.
describeHyperParameterTuningJob in interface AmazonSageMakerdescribeHyperParameterTuningJobRequest - 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 AmazonSageMakerdescribeModelRequest - public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
Returns information about a notebook instance.
describeNotebookInstance in interface AmazonSageMakerdescribeNotebookInstanceRequest - public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.
describeNotebookInstanceLifecycleConfig in interface AmazonSageMakerdescribeNotebookInstanceLifecycleConfigRequest - public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
Returns information about a training job.
describeTrainingJob in interface AmazonSageMakerdescribeTrainingJobRequest - ResourceNotFoundException - Resource being access is not found.public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
Returns information about a transform job.
describeTransformJob in interface AmazonSageMakerdescribeTransformJobRequest - ResourceNotFoundException - Resource being access is not found.public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
Lists endpoint configurations.
listEndpointConfigs in interface AmazonSageMakerlistEndpointConfigsRequest - public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
Lists endpoints.
listEndpoints in interface AmazonSageMakerlistEndpointsRequest - public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobs in interface AmazonSageMakerlistHyperParameterTuningJobsRequest - public ListModelsResult listModels(ListModelsRequest request)
Lists models created with the CreateModel API.
listModels in interface AmazonSageMakerlistModelsRequest - public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigs in interface AmazonSageMakerlistNotebookInstanceLifecycleConfigsRequest - public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstances in interface AmazonSageMakerlistNotebookInstancesRequest - public ListTagsResult listTags(ListTagsRequest request)
Returns the tags for the specified Amazon SageMaker resource.
listTags in interface AmazonSageMakerlistTagsRequest - public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
Lists training jobs.
listTrainingJobs in interface AmazonSageMakerlistTrainingJobsRequest - 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 AmazonSageMakerlistTrainingJobsForHyperParameterTuningJobRequest - ResourceNotFoundException - Resource being access is not found.public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
Lists transform jobs.
listTransformJobs in interface AmazonSageMakerlistTransformJobsRequest - 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, Amazon 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 AmazonSageMakerstartNotebookInstanceRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.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 AmazonSageMakerstopHyperParameterTuningJobRequest - ResourceNotFoundException - Resource being access is not found.public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume.
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 AmazonSageMakerstopNotebookInstanceRequest - public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
Stops a training job. To stop a job, Amazon 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.
Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.
When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to
Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.
stopTrainingJob in interface AmazonSageMakerstopTrainingJobRequest - ResourceNotFoundException - Resource being access is not found.public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
Stops a 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 transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
stopTransformJob in interface AmazonSageMakerstopTransformJobRequest - ResourceNotFoundException - Resource being access is not found.public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint,
and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is
no availability loss).
When Amazon 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 cannot update an endpoint with the current EndpointConfig. To update an endpoint, you must
create a new EndpointConfig.
updateEndpoint in interface AmazonSageMakerupdateEndpointRequest - ResourceLimitExceededException - You have exceeded an Amazon 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, Amazon 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 AmazonSageMakerupdateEndpointWeightsAndCapacitiesRequest - ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.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. You can also update the VPC security groups.
updateNotebookInstance in interface AmazonSageMakerupdateNotebookInstanceRequest - ResourceLimitExceededException - You have exceeded an Amazon 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 AmazonSageMakerupdateNotebookInstanceLifecycleConfigRequest - ResourceLimitExceededException - You have exceeded an Amazon 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 AmazonSageMakerrequest - The originally executed requestpublic AmazonSageMakerWaiters waiters()
waiters in interface AmazonSageMakerpublic void shutdown()
AmazonWebServiceClientshutdown in interface AmazonSageMakershutdown in class AmazonWebServiceClientCopyright © 2013 Amazon Web Services, Inc. All Rights Reserved.