@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AbstractAmazonSageMaker extends Object implements AmazonSageMaker
AmazonSageMaker
. Convenient method forms pass through to the corresponding
overload that takes a request object, which throws an UnsupportedOperationException
.ENDPOINT_PREFIX
public AddAssociationResult addAssociation(AddAssociationRequest request)
AmazonSageMaker
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
public AddTagsResult addTags(AddTagsRequest request)
AmazonSageMaker
Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the
hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter
tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter
tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you
first create the tuning job by specifying them in the Tags
parameter of
CreateHyperParameterTuningJob
addTags
in interface AmazonSageMaker
public AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest request)
AmazonSageMaker
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
public CreateActionResult createAction(CreateActionRequest request)
AmazonSageMaker
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
public CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest request)
AmazonSageMaker
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
createAlgorithm
in interface AmazonSageMaker
public CreateAppResult createApp(CreateAppRequest request)
AmazonSageMaker
Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
createApp
in interface AmazonSageMaker
public CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest request)
AmazonSageMaker
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
createAppImageConfig
in interface AmazonSageMaker
public CreateArtifactResult createArtifact(CreateArtifactRequest request)
AmazonSageMaker
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
public CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest request)
AmazonSageMaker
Creates an Autopilot job.
Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
createAutoMLJob
in interface AmazonSageMaker
public CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest request)
AmazonSageMaker
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
createCodeRepository
in interface AmazonSageMaker
public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request)
AmazonSageMaker
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag
to track the model compilation job's resource use and costs. The response
body contains the CompilationJobArn
for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
createCompilationJob
in interface AmazonSageMaker
public CreateContextResult createContext(CreateContextRequest request)
AmazonSageMaker
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
public CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest request)
AmazonSageMaker
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
public CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest request)
AmazonSageMaker
Creates a device fleet.
createDeviceFleet
in interface AmazonSageMaker
public CreateDomainResult createDomain(CreateDomainRequest request)
AmazonSageMaker
Creates a Domain
used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic
File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon
Virtual Private Cloud (VPC) configurations. An AWS account is limited to one domain per region. Users within a
domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For
other Studio traffic, you can specify the AppNetworkAccessType
parameter.
AppNetworkAccessType
corresponds to the network access type that you choose when you onboard to
Studio. The following options are available:
PublicInternetOnly
- Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows
internet access. This is the default value.
VpcOnly
- All Studio traffic is through the specified VPC and subnets. Internet access is disabled
by default. To allow internet access, you must specify a NAT gateway.
When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
createDomain
in interface AmazonSageMaker
public CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest request)
AmazonSageMaker
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
public CreateEndpointResult createEndpoint(CreateEndpointRequest request)
AmazonSageMaker
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
You must not delete an EndpointConfig
that is in use by an endpoint that is live or while the
UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. To
update an endpoint, you must create a new EndpointConfig
.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating
. After it
creates the endpoint, it sets the status to InService
. Amazon SageMaker can then process incoming
requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
Option 1: For a full Amazon SageMaker access, search and attach the AmazonSageMakerFullAccess
policy.
Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.
createEndpoint
in interface AmazonSageMaker
public CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest request)
AmazonSageMaker
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the
configuration, you identify one or more models, created using the CreateModel
API, to deploy and the
resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant
, for each model that you want to deploy. Each
ProductionVariant
parameter also describes the resources that you want Amazon SageMaker to
provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight
to specify how much traffic you
want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign
traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model
A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration
exists. When you read data from a DynamoDB table supporting
Eventually Consistent Reads
, the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB,
this causes a validation error. If you repeat your read request after a short time, the response should return
the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers
call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a
DynamoDB eventually consistent read.
createEndpointConfig
in interface AmazonSageMaker
public CreateExperimentResult createExperiment(CreateExperimentRequest request)
AmazonSageMaker
Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description
parameter. To add a
description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
createExperiment
in interface AmazonSageMaker
public CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest request)
AmazonSageMaker
Create a new FeatureGroup
. A FeatureGroup
is a group of Features
defined
in the FeatureStore
to describe a Record
.
The FeatureGroup
defines the schema and features contained in the FeatureGroup. A
FeatureGroup
definition is composed of a list of Features
, a
RecordIdentifierFeatureName
, an EventTimeFeatureName
and configurations for its
OnlineStore
and OfflineStore
. Check AWS service quotas to see the
FeatureGroup
s quota for your AWS account.
You must include at least one of OnlineStoreConfig
and OfflineStoreConfig
to create a
FeatureGroup
.
createFeatureGroup
in interface AmazonSageMaker
public CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest request)
AmazonSageMaker
Creates a flow definition.
createFlowDefinition
in interface AmazonSageMaker
public CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest request)
AmazonSageMaker
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
public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request)
AmazonSageMaker
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.
createHyperParameterTuningJob
in interface AmazonSageMaker
public CreateImageResult createImage(CreateImageRequest request)
AmazonSageMaker
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
createImage
in interface AmazonSageMaker
public CreateImageVersionResult createImageVersion(CreateImageVersionRequest request)
AmazonSageMaker
Creates a version of the SageMaker image specified by ImageName
. The version represents the Amazon
Container Registry (ECR) container image specified by BaseImage
.
createImageVersion
in interface AmazonSageMaker
public CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest request)
AmazonSageMaker
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
createLabelingJob
in interface AmazonSageMaker
public CreateModelResult createModel(CreateModelRequest request)
AmazonSageMaker
Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig
API, and then
create an endpoint with the CreateEndpoint
API. Amazon SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob
API. Amazon
SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel
request, you must define a container with the PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
createModel
in interface AmazonSageMaker
public CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest request)
AmazonSageMaker
Creates the definition for a model bias job.
createModelBiasJobDefinition
in interface AmazonSageMaker
public CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest request)
AmazonSageMaker
Creates the definition for a model explainability job.
createModelExplainabilityJobDefinition
in interface AmazonSageMaker
public CreateModelPackageResult createModelPackage(CreateModelPackageRequest request)
AmazonSageMaker
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3
location of your model artifacts, provide values for InferenceSpecification
. To create a model from
an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for
SourceAlgorithmSpecification
.
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
createModelPackage
in interface AmazonSageMaker
public CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest request)
AmazonSageMaker
Creates a model group. A model group contains a group of model versions.
createModelPackageGroup
in interface AmazonSageMaker
public CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest request)
AmazonSageMaker
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
public CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest request)
AmazonSageMaker
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createMonitoringSchedule
in interface AmazonSageMaker
public CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest request)
AmazonSageMaker
Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance
request, specify the type of ML compute instance that you want to run.
Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model
training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId
, Amazon SageMaker creates a network interface in your own VPC,
which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon
SageMaker attaches the security group that you specified in the request to the network interface that it creates
in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon SageMaker specifies both network interfaces when launching this
instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security
groups allow it.
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
createNotebookInstance
in interface AmazonSageMaker
public CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMaker
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH
environment variable that is available to both scripts is
/sbin:bin:/usr/sbin:/usr/bin
.
View CloudWatch Logs for notebook instance lifecycle configurations in log group
/aws/sagemaker/NotebookInstances
in log stream
[notebook-instance-name]/[LifecycleConfigHook]
.
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
createNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
public CreatePipelineResult createPipeline(CreatePipelineRequest request)
AmazonSageMaker
Creates a pipeline using a JSON pipeline definition.
createPipeline
in interface AmazonSageMaker
public CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest request)
AmazonSageMaker
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
The URL that you get from a call to CreatePresignedDomainUrl
is valid only for 5 minutes. If you try
to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
createPresignedDomainUrl
in interface AmazonSageMaker
public CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest request)
AmazonSageMaker
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker
console, when you choose Open
next to a notebook instance, Amazon SageMaker opens a new tab showing
the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the
page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify.
Use the NotIpAddress
condition operator and the aws:SourceIP
condition context key to
specify the list of IP addresses that you want to have access to the notebook instance. For more information, see
Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
createPresignedNotebookInstanceUrl
in interface AmazonSageMaker
public CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest request)
AmazonSageMaker
Creates a processing job.
createProcessingJob
in interface AmazonSageMaker
public CreateProjectResult createProject(CreateProjectRequest request)
AmazonSageMaker
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
public CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest request)
AmazonSageMaker
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification
- Identifies the training algorithm to use.
HyperParameters
- Specify these algorithm-specific parameters to enable the estimation of model
parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of
hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig
- Describes the training dataset and the Amazon S3, EFS, or FSx location where it is
stored.
OutputDataConfig
- Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the
results of model training.
ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy
for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining
- Optimize the cost of training machine learning models by up to 80% by
using Amazon EC2 Spot instances. For more information, see Managed Spot
Training.
RoleArn
- The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your
behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can
successfully complete model training.
StoppingCondition
- To help cap training costs, use MaxRuntimeInSeconds
to set a time
limit for training. Use MaxWaitTimeInSeconds
to specify how long you are willing to wait for a
managed spot training job to complete.
For more information about Amazon SageMaker, see How It Works.
createTrainingJob
in interface AmazonSageMaker
public CreateTransformJobResult createTransformJob(CreateTransformJobRequest request)
AmazonSageMaker
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
TransformJobName
- Identifies the transform job. The name must be unique within an AWS Region in an
AWS account.
ModelName
- Identifies the model to use. ModelName
must be the name of an existing
Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see
CreateModel.
TransformInput
- Describes the dataset to be transformed and the Amazon S3 location where it is
stored.
TransformOutput
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the
results from the transform job.
TransformResources
- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
createTransformJob
in interface AmazonSageMaker
public CreateTrialResult createTrial(CreateTrialRequest request)
AmazonSageMaker
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
createTrial
in interface AmazonSageMaker
public CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest request)
AmazonSageMaker
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
CreateTrialComponent
can only be invoked from within an Amazon SageMaker managed environment. This
includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call
to CreateTrialComponent
from outside one of these environments results in an error.
createTrialComponent
in interface AmazonSageMaker
public CreateUserProfileResult createUserProfile(CreateUserProfileRequest request)
AmazonSageMaker
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
createUserProfile
in interface AmazonSageMaker
public CreateWorkforceResult createWorkforce(CreateWorkforceRequest request)
AmazonSageMaker
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
If you want to create a new workforce in an AWS Region where a workforce already exists, use the API operation to
delete the existing workforce and then use CreateWorkforce
to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in
CognitoConfig
. You can also create an Amazon Cognito workforce using the Amazon SageMaker console.
For more information, see Create a Private
Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in
OidcConfig
. Your OIDC IdP must support groups because groups are used by Ground Truth and
Amazon A2I to create work teams. For more information, see Create a Private
Workforce (OIDC IdP).
createWorkforce
in interface AmazonSageMaker
public CreateWorkteamResult createWorkteam(CreateWorkteamRequest request)
AmazonSageMaker
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
public DeleteActionResult deleteAction(DeleteActionRequest request)
AmazonSageMaker
Deletes an action.
deleteAction
in interface AmazonSageMaker
public DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest request)
AmazonSageMaker
Removes the specified algorithm from your account.
deleteAlgorithm
in interface AmazonSageMaker
public DeleteAppResult deleteApp(DeleteAppRequest request)
AmazonSageMaker
Used to stop and delete an app.
deleteApp
in interface AmazonSageMaker
public DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest request)
AmazonSageMaker
Deletes an AppImageConfig.
deleteAppImageConfig
in interface AmazonSageMaker
public DeleteArtifactResult deleteArtifact(DeleteArtifactRequest request)
AmazonSageMaker
Deletes an artifact. Either ArtifactArn
or Source
must be specified.
deleteArtifact
in interface AmazonSageMaker
public DeleteAssociationResult deleteAssociation(DeleteAssociationRequest request)
AmazonSageMaker
Deletes an association.
deleteAssociation
in interface AmazonSageMaker
public DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest request)
AmazonSageMaker
Deletes the specified Git repository from your account.
deleteCodeRepository
in interface AmazonSageMaker
public DeleteContextResult deleteContext(DeleteContextRequest request)
AmazonSageMaker
Deletes an context.
deleteContext
in interface AmazonSageMaker
public DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest request)
AmazonSageMaker
Deletes a data quality monitoring job definition.
deleteDataQualityJobDefinition
in interface AmazonSageMaker
public DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest request)
AmazonSageMaker
Deletes a fleet.
deleteDeviceFleet
in interface AmazonSageMaker
public DeleteDomainResult deleteDomain(DeleteDomainRequest request)
AmazonSageMaker
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteDomain
in interface AmazonSageMaker
public DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest request)
AmazonSageMaker
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 AmazonSageMaker
public DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest request)
AmazonSageMaker
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
public DeleteExperimentResult deleteExperiment(DeleteExperimentRequest request)
AmazonSageMaker
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
deleteExperiment
in interface AmazonSageMaker
public DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest request)
AmazonSageMaker
Delete the FeatureGroup
and any data that was written to the OnlineStore
of the
FeatureGroup
. Data cannot be accessed from the OnlineStore
immediately after
DeleteFeatureGroup
is called.
Data written into the OfflineStore
will not be deleted. The AWS Glue database and tables that are
automatically created for your OfflineStore
are not deleted.
deleteFeatureGroup
in interface AmazonSageMaker
public DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest request)
AmazonSageMaker
Deletes the specified flow definition.
deleteFlowDefinition
in interface AmazonSageMaker
public DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest request)
AmazonSageMaker
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker
task template, it no longer appears when you call ListHumanTaskUis
.
deleteHumanTaskUi
in interface AmazonSageMaker
public DeleteImageResult deleteImage(DeleteImageRequest request)
AmazonSageMaker
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImage
in interface AmazonSageMaker
public DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest request)
AmazonSageMaker
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteImageVersion
in interface AmazonSageMaker
public DeleteModelResult deleteModel(DeleteModelRequest request)
AmazonSageMaker
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 AmazonSageMaker
public DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest request)
AmazonSageMaker
Deletes an Amazon SageMaker model bias job definition.
deleteModelBiasJobDefinition
in interface AmazonSageMaker
public DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest request)
AmazonSageMaker
Deletes an Amazon SageMaker model explainability job definition.
deleteModelExplainabilityJobDefinition
in interface AmazonSageMaker
public DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest request)
AmazonSageMaker
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
deleteModelPackage
in interface AmazonSageMaker
public DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest request)
AmazonSageMaker
Deletes the specified model group.
deleteModelPackageGroup
in interface AmazonSageMaker
public DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest request)
AmazonSageMaker
Deletes a model group resource policy.
deleteModelPackageGroupPolicy
in interface AmazonSageMaker
public DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest request)
AmazonSageMaker
Deletes the secified model quality monitoring job definition.
deleteModelQualityJobDefinition
in interface AmazonSageMaker
public DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest request)
AmazonSageMaker
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
public DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest request)
AmazonSageMaker
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 AmazonSageMaker
public DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest request)
AmazonSageMaker
Deletes a notebook instance lifecycle configuration.
deleteNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
public DeletePipelineResult deletePipeline(DeletePipelineRequest request)
AmazonSageMaker
Deletes a pipeline if there are no in-progress executions.
deletePipeline
in interface AmazonSageMaker
public DeleteProjectResult deleteProject(DeleteProjectRequest request)
AmazonSageMaker
Delete the specified project.
deleteProject
in interface AmazonSageMaker
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
AmazonSageMaker
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags
API.
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
deleteTags
in interface AmazonSageMaker
public DeleteTrialResult deleteTrial(DeleteTrialRequest request)
AmazonSageMaker
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
public DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest request)
AmazonSageMaker
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
public DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest request)
AmazonSageMaker
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
public DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest request)
AmazonSageMaker
Use this operation to delete a workforce.
If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
If a private workforce contains one or more work teams, you must use the operation to delete all work teams
before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will
recieve a ResourceInUse
error.
deleteWorkforce
in interface AmazonSageMaker
public DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest request)
AmazonSageMaker
Deletes an existing work team. This operation can't be undone.
deleteWorkteam
in interface AmazonSageMaker
public DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest request)
AmazonSageMaker
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
deregisterDevices
in interface AmazonSageMaker
public DescribeActionResult describeAction(DescribeActionRequest request)
AmazonSageMaker
Describes an action.
describeAction
in interface AmazonSageMaker
public DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest request)
AmazonSageMaker
Returns a description of the specified algorithm that is in your account.
describeAlgorithm
in interface AmazonSageMaker
public DescribeAppResult describeApp(DescribeAppRequest request)
AmazonSageMaker
Describes the app.
describeApp
in interface AmazonSageMaker
public DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest request)
AmazonSageMaker
Describes an AppImageConfig.
describeAppImageConfig
in interface AmazonSageMaker
public DescribeArtifactResult describeArtifact(DescribeArtifactRequest request)
AmazonSageMaker
Describes an artifact.
describeArtifact
in interface AmazonSageMaker
public DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest request)
AmazonSageMaker
Returns information about an Amazon SageMaker job.
describeAutoMLJob
in interface AmazonSageMaker
public DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest request)
AmazonSageMaker
Gets details about the specified Git repository.
describeCodeRepository
in interface AmazonSageMaker
public DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest request)
AmazonSageMaker
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
public DescribeContextResult describeContext(DescribeContextRequest request)
AmazonSageMaker
Describes a context.
describeContext
in interface AmazonSageMaker
public DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest request)
AmazonSageMaker
Gets the details of a data quality monitoring job definition.
describeDataQualityJobDefinition
in interface AmazonSageMaker
public DescribeDeviceResult describeDevice(DescribeDeviceRequest request)
AmazonSageMaker
Describes the device.
describeDevice
in interface AmazonSageMaker
public DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest request)
AmazonSageMaker
A description of the fleet the device belongs to.
describeDeviceFleet
in interface AmazonSageMaker
public DescribeDomainResult describeDomain(DescribeDomainRequest request)
AmazonSageMaker
The description of the domain.
describeDomain
in interface AmazonSageMaker
public DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest request)
AmazonSageMaker
A description of edge packaging jobs.
describeEdgePackagingJob
in interface AmazonSageMaker
public DescribeEndpointResult describeEndpoint(DescribeEndpointRequest request)
AmazonSageMaker
Returns the description of an endpoint.
describeEndpoint
in interface AmazonSageMaker
public DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest request)
AmazonSageMaker
Returns the description of an endpoint configuration created using the CreateEndpointConfig
API.
describeEndpointConfig
in interface AmazonSageMaker
public DescribeExperimentResult describeExperiment(DescribeExperimentRequest request)
AmazonSageMaker
Provides a list of an experiment's properties.
describeExperiment
in interface AmazonSageMaker
public DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest request)
AmazonSageMaker
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
public DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest request)
AmazonSageMaker
Returns information about the specified flow definition.
describeFlowDefinition
in interface AmazonSageMaker
public DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest request)
AmazonSageMaker
Returns information about the requested human task user interface (worker task template).
describeHumanTaskUi
in interface AmazonSageMaker
public DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest request)
AmazonSageMaker
Gets a description of a hyperparameter tuning job.
describeHyperParameterTuningJob
in interface AmazonSageMaker
public DescribeImageResult describeImage(DescribeImageRequest request)
AmazonSageMaker
Describes a SageMaker image.
describeImage
in interface AmazonSageMaker
public DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest request)
AmazonSageMaker
Describes a version of a SageMaker image.
describeImageVersion
in interface AmazonSageMaker
public DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest request)
AmazonSageMaker
Gets information about a labeling job.
describeLabelingJob
in interface AmazonSageMaker
public DescribeModelResult describeModel(DescribeModelRequest request)
AmazonSageMaker
Describes a model that you created using the CreateModel
API.
describeModel
in interface AmazonSageMaker
public DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest request)
AmazonSageMaker
Returns a description of a model bias job definition.
describeModelBiasJobDefinition
in interface AmazonSageMaker
public DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest request)
AmazonSageMaker
Returns a description of a model explainability job definition.
describeModelExplainabilityJobDefinition
in interface AmazonSageMaker
public DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest request)
AmazonSageMaker
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
describeModelPackage
in interface AmazonSageMaker
public DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest request)
AmazonSageMaker
Gets a description for the specified model group.
describeModelPackageGroup
in interface AmazonSageMaker
public DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest request)
AmazonSageMaker
Returns a description of a model quality job definition.
describeModelQualityJobDefinition
in interface AmazonSageMaker
public DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest request)
AmazonSageMaker
Describes the schedule for a monitoring job.
describeMonitoringSchedule
in interface AmazonSageMaker
public DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest request)
AmazonSageMaker
Returns information about a notebook instance.
describeNotebookInstance
in interface AmazonSageMaker
public DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest request)
AmazonSageMaker
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
public DescribePipelineResult describePipeline(DescribePipelineRequest request)
AmazonSageMaker
Describes the details of a pipeline.
describePipeline
in interface AmazonSageMaker
public DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest request)
AmazonSageMaker
Describes the details of an execution's pipeline definition.
describePipelineDefinitionForExecution
in interface AmazonSageMaker
public DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest request)
AmazonSageMaker
Describes the details of a pipeline execution.
describePipelineExecution
in interface AmazonSageMaker
public DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest request)
AmazonSageMaker
Returns a description of a processing job.
describeProcessingJob
in interface AmazonSageMaker
public DescribeProjectResult describeProject(DescribeProjectRequest request)
AmazonSageMaker
Describes the details of a project.
describeProject
in interface AmazonSageMaker
public DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest request)
AmazonSageMaker
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
describeSubscribedWorkteam
in interface AmazonSageMaker
public DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest request)
AmazonSageMaker
Returns information about a training job.
describeTrainingJob
in interface AmazonSageMaker
public DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest request)
AmazonSageMaker
Returns information about a transform job.
describeTransformJob
in interface AmazonSageMaker
public DescribeTrialResult describeTrial(DescribeTrialRequest request)
AmazonSageMaker
Provides a list of a trial's properties.
describeTrial
in interface AmazonSageMaker
public DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest request)
AmazonSageMaker
Provides a list of a trials component's properties.
describeTrialComponent
in interface AmazonSageMaker
public DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest request)
AmazonSageMaker
Describes a user profile. For more information, see CreateUserProfile
.
describeUserProfile
in interface AmazonSageMaker
public DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest request)
AmazonSageMaker
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
public DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest request)
AmazonSageMaker
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
describeWorkteam
in interface AmazonSageMaker
public DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest request)
AmazonSageMaker
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
disableSagemakerServicecatalogPortfolio
in interface AmazonSageMaker
public DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest request)
AmazonSageMaker
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
public EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest request)
AmazonSageMaker
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
enableSagemakerServicecatalogPortfolio
in interface AmazonSageMaker
public GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest request)
AmazonSageMaker
Describes a fleet.
getDeviceFleetReport
in interface AmazonSageMaker
public GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest request)
AmazonSageMaker
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 AWS Identity and Access Management User Guide..
getModelPackageGroupPolicy
in interface AmazonSageMaker
public GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest request)
AmazonSageMaker
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
getSagemakerServicecatalogPortfolioStatus
in interface AmazonSageMaker
public GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest request)
AmazonSageMaker
An auto-complete API for the search functionality in the Amazon 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
public ListActionsResult listActions(ListActionsRequest request)
AmazonSageMaker
Lists the actions in your account and their properties.
listActions
in interface AmazonSageMaker
public ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest request)
AmazonSageMaker
Lists the machine learning algorithms that have been created.
listAlgorithms
in interface AmazonSageMaker
public ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest request)
AmazonSageMaker
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
public ListAppsResult listApps(ListAppsRequest request)
AmazonSageMaker
Lists apps.
listApps
in interface AmazonSageMaker
public ListArtifactsResult listArtifacts(ListArtifactsRequest request)
AmazonSageMaker
Lists the artifacts in your account and their properties.
listArtifacts
in interface AmazonSageMaker
public ListAssociationsResult listAssociations(ListAssociationsRequest request)
AmazonSageMaker
Lists the associations in your account and their properties.
listAssociations
in interface AmazonSageMaker
public ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest request)
AmazonSageMaker
Request a list of jobs.
listAutoMLJobs
in interface AmazonSageMaker
public ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest request)
AmazonSageMaker
List the Candidates created for the job.
listCandidatesForAutoMLJob
in interface AmazonSageMaker
public ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest request)
AmazonSageMaker
Gets a list of the Git repositories in your account.
listCodeRepositories
in interface AmazonSageMaker
public ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest request)
AmazonSageMaker
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
public ListContextsResult listContexts(ListContextsRequest request)
AmazonSageMaker
Lists the contexts in your account and their properties.
listContexts
in interface AmazonSageMaker
public ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest request)
AmazonSageMaker
Lists the data quality job definitions in your account.
listDataQualityJobDefinitions
in interface AmazonSageMaker
public ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest request)
AmazonSageMaker
Returns a list of devices in the fleet.
listDeviceFleets
in interface AmazonSageMaker
public ListDevicesResult listDevices(ListDevicesRequest request)
AmazonSageMaker
A list of devices.
listDevices
in interface AmazonSageMaker
public ListDomainsResult listDomains(ListDomainsRequest request)
AmazonSageMaker
Lists the domains.
listDomains
in interface AmazonSageMaker
public ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest request)
AmazonSageMaker
Returns a list of edge packaging jobs.
listEdgePackagingJobs
in interface AmazonSageMaker
public ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest request)
AmazonSageMaker
Lists endpoint configurations.
listEndpointConfigs
in interface AmazonSageMaker
public ListEndpointsResult listEndpoints(ListEndpointsRequest request)
AmazonSageMaker
Lists endpoints.
listEndpoints
in interface AmazonSageMaker
public ListExperimentsResult listExperiments(ListExperimentsRequest request)
AmazonSageMaker
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
public ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest request)
AmazonSageMaker
List FeatureGroup
s based on given filter and order.
listFeatureGroups
in interface AmazonSageMaker
public ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest request)
AmazonSageMaker
Returns information about the flow definitions in your account.
listFlowDefinitions
in interface AmazonSageMaker
public ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest request)
AmazonSageMaker
Returns information about the human task user interfaces in your account.
listHumanTaskUis
in interface AmazonSageMaker
public ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest request)
AmazonSageMaker
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
listHyperParameterTuningJobs
in interface AmazonSageMaker
public ListImageVersionsResult listImageVersions(ListImageVersionsRequest request)
AmazonSageMaker
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
public ListImagesResult listImages(ListImagesRequest request)
AmazonSageMaker
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
public ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest request)
AmazonSageMaker
Gets a list of labeling jobs.
listLabelingJobs
in interface AmazonSageMaker
public ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest request)
AmazonSageMaker
Gets a list of labeling jobs assigned to a specified work team.
listLabelingJobsForWorkteam
in interface AmazonSageMaker
public ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest request)
AmazonSageMaker
Lists model bias jobs definitions that satisfy various filters.
listModelBiasJobDefinitions
in interface AmazonSageMaker
public ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest request)
AmazonSageMaker
Lists model explainability job definitions that satisfy various filters.
listModelExplainabilityJobDefinitions
in interface AmazonSageMaker
public ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest request)
AmazonSageMaker
Gets a list of the model groups in your AWS account.
listModelPackageGroups
in interface AmazonSageMaker
public ListModelPackagesResult listModelPackages(ListModelPackagesRequest request)
AmazonSageMaker
Lists the model packages that have been created.
listModelPackages
in interface AmazonSageMaker
public ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest request)
AmazonSageMaker
Gets a list of model quality monitoring job definitions in your account.
listModelQualityJobDefinitions
in interface AmazonSageMaker
public ListModelsResult listModels(ListModelsRequest request)
AmazonSageMaker
Lists models created with the CreateModel API.
listModels
in interface AmazonSageMaker
public ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest request)
AmazonSageMaker
Returns list of all monitoring job executions.
listMonitoringExecutions
in interface AmazonSageMaker
public ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest request)
AmazonSageMaker
Returns list of all monitoring schedules.
listMonitoringSchedules
in interface AmazonSageMaker
public ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest request)
AmazonSageMaker
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
listNotebookInstanceLifecycleConfigs
in interface AmazonSageMaker
public ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest request)
AmazonSageMaker
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
listNotebookInstances
in interface AmazonSageMaker
public ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest request)
AmazonSageMaker
Gets a list of PipeLineExecutionStep
objects.
listPipelineExecutionSteps
in interface AmazonSageMaker
public ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest request)
AmazonSageMaker
Gets a list of the pipeline executions.
listPipelineExecutions
in interface AmazonSageMaker
public ListPipelineParametersForExecutionResult listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest request)
AmazonSageMaker
Gets a list of parameters for a pipeline execution.
listPipelineParametersForExecution
in interface AmazonSageMaker
public ListPipelinesResult listPipelines(ListPipelinesRequest request)
AmazonSageMaker
Gets a list of pipelines.
listPipelines
in interface AmazonSageMaker
public ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest request)
AmazonSageMaker
Lists processing jobs that satisfy various filters.
listProcessingJobs
in interface AmazonSageMaker
public ListProjectsResult listProjects(ListProjectsRequest request)
AmazonSageMaker
Gets a list of the projects in an AWS account.
listProjects
in interface AmazonSageMaker
public ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest request)
AmazonSageMaker
Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work
team satisfies the filter specified in the NameContains
parameter.
listSubscribedWorkteams
in interface AmazonSageMaker
public ListTagsResult listTags(ListTagsRequest request)
AmazonSageMaker
Returns the tags for the specified Amazon SageMaker resource.
listTags
in interface AmazonSageMaker
public ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest request)
AmazonSageMaker
Lists training jobs.
listTrainingJobs
in interface AmazonSageMaker
public ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest request)
AmazonSageMaker
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
listTrainingJobsForHyperParameterTuningJob
in interface AmazonSageMaker
public ListTransformJobsResult listTransformJobs(ListTransformJobsRequest request)
AmazonSageMaker
Lists transform jobs.
listTransformJobs
in interface AmazonSageMaker
public ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest request)
AmazonSageMaker
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
public ListTrialsResult listTrials(ListTrialsRequest request)
AmazonSageMaker
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
public ListUserProfilesResult listUserProfiles(ListUserProfilesRequest request)
AmazonSageMaker
Lists user profiles.
listUserProfiles
in interface AmazonSageMaker
public ListWorkforcesResult listWorkforces(ListWorkforcesRequest request)
AmazonSageMaker
Use this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region.
listWorkforces
in interface AmazonSageMaker
public ListWorkteamsResult listWorkteams(ListWorkteamsRequest request)
AmazonSageMaker
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
public PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest request)
AmazonSageMaker
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 AWS Identity and Access Management User Guide..
putModelPackageGroupPolicy
in interface AmazonSageMaker
public RegisterDevicesResult registerDevices(RegisterDevicesRequest request)
AmazonSageMaker
Register devices.
registerDevices
in interface AmazonSageMaker
public RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest request)
AmazonSageMaker
Renders the UI template so that you can preview the worker's experience.
renderUiTemplate
in interface AmazonSageMaker
public SearchResult search(SearchRequest request)
AmazonSageMaker
Finds Amazon 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.
search
in interface AmazonSageMaker
public StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest request)
AmazonSageMaker
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
public StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest request)
AmazonSageMaker
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 AmazonSageMaker
public StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest request)
AmazonSageMaker
Starts a pipeline execution.
startPipelineExecution
in interface AmazonSageMaker
public StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest request)
AmazonSageMaker
A method for forcing the termination of a running job.
stopAutoMLJob
in interface AmazonSageMaker
public StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest request)
AmazonSageMaker
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
CompilationJobSummary$CompilationJobStatus of the job to Stopping
. After Amazon SageMaker
stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped
.
stopCompilationJob
in interface AmazonSageMaker
public StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest request)
AmazonSageMaker
Request to stop an edge packaging job.
stopEdgePackagingJob
in interface AmazonSageMaker
public StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest request)
AmazonSageMaker
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
public StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest request)
AmazonSageMaker
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
public StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest request)
AmazonSageMaker
Stops a previously started monitoring schedule.
stopMonitoringSchedule
in interface AmazonSageMaker
public StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest request)
AmazonSageMaker
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. Amazon 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
public StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest request)
AmazonSageMaker
Stops a pipeline execution.
stopPipelineExecution
in interface AmazonSageMaker
public StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest request)
AmazonSageMaker
Stops a processing job.
stopProcessingJob
in interface AmazonSageMaker
public StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest request)
AmazonSageMaker
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.
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 AmazonSageMaker
public StopTransformJobResult stopTransformJob(StopTransformJobRequest request)
AmazonSageMaker
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 AmazonSageMaker
public UpdateActionResult updateAction(UpdateActionRequest request)
AmazonSageMaker
Updates an action.
updateAction
in interface AmazonSageMaker
public UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest request)
AmazonSageMaker
Updates the properties of an AppImageConfig.
updateAppImageConfig
in interface AmazonSageMaker
public UpdateArtifactResult updateArtifact(UpdateArtifactRequest request)
AmazonSageMaker
Updates an artifact.
updateArtifact
in interface AmazonSageMaker
public UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest request)
AmazonSageMaker
Updates the specified Git repository with the specified values.
updateCodeRepository
in interface AmazonSageMaker
public UpdateContextResult updateContext(UpdateContextRequest request)
AmazonSageMaker
Updates a context.
updateContext
in interface AmazonSageMaker
public UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest request)
AmazonSageMaker
Updates a fleet of devices.
updateDeviceFleet
in interface AmazonSageMaker
public UpdateDevicesResult updateDevices(UpdateDevicesRequest request)
AmazonSageMaker
Updates one or more devices in a fleet.
updateDevices
in interface AmazonSageMaker
public UpdateDomainResult updateDomain(UpdateDomainRequest request)
AmazonSageMaker
Updates the default settings for new user profiles in the domain.
updateDomain
in interface AmazonSageMaker
public UpdateEndpointResult updateEndpoint(UpdateEndpointRequest request)
AmazonSageMaker
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 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
public UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest request)
AmazonSageMaker
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 AmazonSageMaker
public UpdateExperimentResult updateExperiment(UpdateExperimentRequest request)
AmazonSageMaker
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
updateExperiment
in interface AmazonSageMaker
public UpdateImageResult updateImage(UpdateImageRequest request)
AmazonSageMaker
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
updateImage
in interface AmazonSageMaker
public UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest request)
AmazonSageMaker
Updates a versioned model.
updateModelPackage
in interface AmazonSageMaker
public UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest request)
AmazonSageMaker
Updates a previously created schedule.
updateMonitoringSchedule
in interface AmazonSageMaker
public UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest request)
AmazonSageMaker
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
public UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest request)
AmazonSageMaker
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
updateNotebookInstanceLifecycleConfig
in interface AmazonSageMaker
public UpdatePipelineResult updatePipeline(UpdatePipelineRequest request)
AmazonSageMaker
Updates a pipeline.
updatePipeline
in interface AmazonSageMaker
public UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest request)
AmazonSageMaker
Updates a pipeline execution.
updatePipelineExecution
in interface AmazonSageMaker
public UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest request)
AmazonSageMaker
Update a model training job to request a new Debugger profiling configuration.
updateTrainingJob
in interface AmazonSageMaker
public UpdateTrialResult updateTrial(UpdateTrialRequest request)
AmazonSageMaker
Updates the display name of a trial.
updateTrial
in interface AmazonSageMaker
public UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest request)
AmazonSageMaker
Updates one or more properties of a trial component.
updateTrialComponent
in interface AmazonSageMaker
public UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest request)
AmazonSageMaker
Updates a user profile.
updateUserProfile
in interface AmazonSageMaker
public UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest request)
AmazonSageMaker
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.
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.
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 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 operation.
This operation only applies to private workforces.
updateWorkforce
in interface AmazonSageMaker
public UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest request)
AmazonSageMaker
Updates an existing work team with new member definitions or description.
updateWorkteam
in interface AmazonSageMaker
public void shutdown()
AmazonSageMaker
shutdown
in interface AmazonSageMaker
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
AmazonSageMaker
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 a request.
getCachedResponseMetadata
in interface AmazonSageMaker
request
- The originally executed request.public AmazonSageMakerWaiters waiters()
waiters
in interface AmazonSageMaker