AWS SDK for C++AWS SDK for C++ Version 1.11.617 |
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AWS SDK for C++AWS SDK for C++ Version 1.11.617 |
#include <SageMakerClient.h>
Provides APIs for creating and managing SageMaker resources.
Other Resources:
Definition at line 26 of file SageMakerClient.h.
Definition at line 29 of file SageMakerClient.h.
Definition at line 33 of file SageMakerClient.h.
Definition at line 34 of file SageMakerClient.h.
Aws::SageMaker::SageMakerClientConfiguration(),
nullptr
Initializes client to use DefaultCredentialProviderChain, with default http client factory, and optional client config. If client config is not specified, it will be initialized to default values.
nullptr,
Aws::SageMaker::SageMakerClientConfiguration()
Initializes client to use SimpleAWSCredentialsProvider, with default http client factory, and optional client config. If client config is not specified, it will be initialized to default values.
nullptr,
Aws::SageMaker::SageMakerClientConfiguration()
Initializes client to use specified credentials provider with specified client config. If http client factory is not supplied, the default http client factory will be used
Initializes client to use DefaultCredentialProviderChain, with default http client factory, and optional client config. If client config is not specified, it will be initialized to default values.
Initializes client to use SimpleAWSCredentialsProvider, with default http client factory, and optional client config. If client config is not specified, it will be initialized to default values.
Initializes client to use specified credentials provider with specified client config. If http client factory is not supplied, the default http client factory will be used
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.
nullptr
An Async wrapper for AddAssociation that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 109 of file SageMakerClient.h.
A Callable wrapper for AddAssociation that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 100 of file SageMakerClient.h.
Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.
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An Async wrapper for AddTags that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 160 of file SageMakerClient.h.
A Callable wrapper for AddTags that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 151 of file SageMakerClient.h.
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.
nullptr
An Async wrapper for AssociateTrialComponent that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 189 of file SageMakerClient.h.
A Callable wrapper for AssociateTrialComponent that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 180 of file SageMakerClient.h.
Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs.
To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod.
If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.
nullptr
An Async wrapper for BatchDeleteClusterNodes that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 228 of file SageMakerClient.h.
A Callable wrapper for BatchDeleteClusterNodes that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 219 of file SageMakerClient.h.
nullptr
An Async wrapper for BatchDescribeModelPackage that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 254 of file SageMakerClient.h.
A Callable wrapper for BatchDescribeModelPackage that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 245 of file SageMakerClient.h.
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.
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An Async wrapper for CreateAction that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 284 of file SageMakerClient.h.
A Callable wrapper for CreateAction that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 275 of file SageMakerClient.h.
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
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An Async wrapper for CreateAlgorithm that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 310 of file SageMakerClient.h.
A Callable wrapper for CreateAlgorithm that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 301 of file SageMakerClient.h.
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
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An Async wrapper for CreateApp that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 338 of file SageMakerClient.h.
A Callable wrapper for CreateApp that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 329 of file SageMakerClient.h.
Creates a configuration for running a SageMaker AI image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
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An Async wrapper for CreateAppImageConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 366 of file SageMakerClient.h.
A Callable wrapper for CreateAppImageConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 357 of file SageMakerClient.h.
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.
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An Async wrapper for CreateArtifact that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 395 of file SageMakerClient.h.
A Callable wrapper for CreateArtifact that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 386 of file SageMakerClient.h.
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.
We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
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An Async wrapper for CreateAutoMLJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 452 of file SageMakerClient.h.
A Callable wrapper for CreateAutoMLJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 443 of file SageMakerClient.h.
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.
AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
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An Async wrapper for CreateAutoMLJobV2 that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 517 of file SageMakerClient.h.
A Callable wrapper for CreateAutoMLJobV2 that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 508 of file SageMakerClient.h.
Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
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An Async wrapper for CreateCluster that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 548 of file SageMakerClient.h.
A Callable wrapper for CreateCluster that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 539 of file SageMakerClient.h.
Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.
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An Async wrapper for CreateClusterSchedulerConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 576 of file SageMakerClient.h.
A Callable wrapper for CreateClusterSchedulerConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 567 of file SageMakerClient.h.
Creates a Git repository as a resource in your SageMaker AI account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker AI account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
nullptr
An Async wrapper for CreateCodeRepository that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 610 of file SageMakerClient.h.
A Callable wrapper for CreateCodeRepository that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 601 of file SageMakerClient.h.
Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI 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 AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI 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.
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An Async wrapper for CreateCompilationJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 655 of file SageMakerClient.h.
A Callable wrapper for CreateCompilationJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 646 of file SageMakerClient.h.
Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.
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An Async wrapper for CreateComputeQuota that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 683 of file SageMakerClient.h.
A Callable wrapper for CreateComputeQuota that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 674 of file SageMakerClient.h.
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.
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An Async wrapper for CreateContext that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 712 of file SageMakerClient.h.
A Callable wrapper for CreateContext that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 703 of file SageMakerClient.h.
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
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An Async wrapper for CreateDataQualityJobDefinition that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 740 of file SageMakerClient.h.
A Callable wrapper for CreateDataQualityJobDefinition that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 731 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateDeviceFleet that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 765 of file SageMakerClient.h.
A Callable wrapper for CreateDeviceFleet that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 756 of file SageMakerClient.h.
Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker AI uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:
PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value.
VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.
When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker AI Studio app successfully.
For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.
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An Async wrapper for CreateDomain that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 823 of file SageMakerClient.h.
A Callable wrapper for CreateDomain that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 814 of file SageMakerClient.h.
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
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An Async wrapper for CreateEdgeDeploymentPlan that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 850 of file SageMakerClient.h.
A Callable wrapper for CreateEdgeDeploymentPlan that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 841 of file SageMakerClient.h.
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An Async wrapper for CreateEdgeDeploymentStage that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 876 of file SageMakerClient.h.
A Callable wrapper for CreateEdgeDeploymentStage that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 867 of file SageMakerClient.h.
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An Async wrapper for CreateEdgePackagingJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 904 of file SageMakerClient.h.
A Callable wrapper for CreateEdgePackagingJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 895 of file SageMakerClient.h.
Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
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An Async wrapper for CreateEndpoint that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 990 of file SageMakerClient.h.
A Callable wrapper for CreateEndpoint that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 981 of file SageMakerClient.h.
Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.
Use this API if you want to use SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
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An Async wrapper for CreateEndpointConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1045 of file SageMakerClient.h.
A Callable wrapper for CreateEndpointConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1036 of file SageMakerClient.h.
Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
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An Async wrapper for CreateExperiment that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1096 of file SageMakerClient.h.
A Callable wrapper for CreateExperiment that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1087 of file SageMakerClient.h.
Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.
The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType.
You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.
nullptr
An Async wrapper for CreateFeatureGroup that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1136 of file SageMakerClient.h.
A Callable wrapper for CreateFeatureGroup that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1127 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateFlowDefinition that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1161 of file SageMakerClient.h.
A Callable wrapper for CreateFlowDefinition that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1152 of file SageMakerClient.h.
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An Async wrapper for CreateHub that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1186 of file SageMakerClient.h.
A Callable wrapper for CreateHub that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1177 of file SageMakerClient.h.
Creates presigned URLs for accessing hub content artifacts. This operation generates time-limited, secure URLs that allow direct download of model artifacts and associated files from Amazon SageMaker hub content, including gated models that require end-user license agreement acceptance.
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An Async wrapper for CreateHubContentPresignedUrls that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1215 of file SageMakerClient.h.
A Callable wrapper for CreateHubContentPresignedUrls that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1206 of file SageMakerClient.h.
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
nullptr
An Async wrapper for CreateHubContentReference that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1241 of file SageMakerClient.h.
A Callable wrapper for CreateHubContentReference that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1232 of file SageMakerClient.h.
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.
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An Async wrapper for CreateHumanTaskUi that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1268 of file SageMakerClient.h.
A Callable wrapper for CreateHumanTaskUi that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1259 of file SageMakerClient.h.
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..
nullptr
An Async wrapper for CreateHyperParameterTuningJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1308 of file SageMakerClient.h.
A Callable wrapper for CreateHyperParameterTuningJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1299 of file SageMakerClient.h.
Creates a custom SageMaker AI image. A SageMaker AI image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker AI image.
nullptr
An Async wrapper for CreateImage that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1337 of file SageMakerClient.h.
A Callable wrapper for CreateImage that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1328 of file SageMakerClient.h.
Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
nullptr
An Async wrapper for CreateImageVersion that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1364 of file SageMakerClient.h.
A Callable wrapper for CreateImageVersion that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1355 of file SageMakerClient.h.
Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
nullptr
An Async wrapper for CreateInferenceComponent that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1398 of file SageMakerClient.h.
A Callable wrapper for CreateInferenceComponent that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1389 of file SageMakerClient.h.
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
nullptr
An Async wrapper for CreateInferenceExperiment that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1434 of file SageMakerClient.h.
A Callable wrapper for CreateInferenceExperiment that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1425 of file SageMakerClient.h.
Starts a recommendation job. You can create either an instance recommendation or load test job.
nullptr
An Async wrapper for CreateInferenceRecommendationsJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1460 of file SageMakerClient.h.
A Callable wrapper for CreateInferenceRecommendationsJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1451 of file SageMakerClient.h.
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
nullptr
An Async wrapper for CreateLabelingJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1518 of file SageMakerClient.h.
A Callable wrapper for CreateLabelingJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1509 of file SageMakerClient.h.
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
nullptr
An Async wrapper for CreateMlflowTrackingServer that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1546 of file SageMakerClient.h.
A Callable wrapper for CreateMlflowTrackingServer that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1537 of file SageMakerClient.h.
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
nullptr
An Async wrapper for CreateModel that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1588 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateModelBiasJobDefinition that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1613 of file SageMakerClient.h.
A Callable wrapper for CreateModelBiasJobDefinition that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1604 of file SageMakerClient.h.
A Callable wrapper for CreateModel that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1579 of file SageMakerClient.h.
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
nullptr
An Async wrapper for CreateModelCard that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1641 of file SageMakerClient.h.
A Callable wrapper for CreateModelCard that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1632 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateModelCardExportJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1667 of file SageMakerClient.h.
A Callable wrapper for CreateModelCardExportJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1658 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateModelExplainabilityJobDefinition that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1693 of file SageMakerClient.h.
A Callable wrapper for CreateModelExplainabilityJobDefinition that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1684 of file SageMakerClient.h.
{})
const
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
nullptr,
{}
An Async wrapper for CreateModelPackage that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1730 of file SageMakerClient.h.
{})
const
A Callable wrapper for CreateModelPackage that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1721 of file SageMakerClient.h.
Creates a model group. A model group contains a group of model versions.
nullptr
An Async wrapper for CreateModelPackageGroup that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1756 of file SageMakerClient.h.
A Callable wrapper for CreateModelPackageGroup that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1747 of file SageMakerClient.h.
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
nullptr
An Async wrapper for CreateModelQualityJobDefinition that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1784 of file SageMakerClient.h.
A Callable wrapper for CreateModelQualityJobDefinition that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1775 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateMonitoringSchedule that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1811 of file SageMakerClient.h.
A Callable wrapper for CreateMonitoringSchedule that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1802 of file SageMakerClient.h.
Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker AI launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker AI does the following:
Creates a network interface in the SageMaker AI VPC.
(Option) If you specified SubnetId, SageMaker AI creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker AI attaches the security group that you specified in the request to the network interface that it creates in your VPC.
Launches an EC2 instance of the type specified in the request in the SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker AI creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker AI endpoints, and validate hosted models.
For more information, see How It Works.
nullptr
An Async wrapper for CreateNotebookInstance that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1863 of file SageMakerClient.h.
A Callable wrapper for CreateNotebookInstance that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1854 of file SageMakerClient.h.
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
nullptr
An Async wrapper for CreateNotebookInstanceLifecycleConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1902 of file SageMakerClient.h.
A Callable wrapper for CreateNotebookInstanceLifecycleConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1893 of file SageMakerClient.h.
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
nullptr
An Async wrapper for CreateOptimizationJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1934 of file SageMakerClient.h.
A Callable wrapper for CreateOptimizationJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1925 of file SageMakerClient.h.
nullptr
An Async wrapper for CreatePartnerApp that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1959 of file SageMakerClient.h.
A Callable wrapper for CreatePartnerApp that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1950 of file SageMakerClient.h.
nullptr
An Async wrapper for CreatePartnerAppPresignedUrl that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 1985 of file SageMakerClient.h.
A Callable wrapper for CreatePartnerAppPresignedUrl that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 1976 of file SageMakerClient.h.
nullptr
An Async wrapper for CreatePipeline that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2011 of file SageMakerClient.h.
A Callable wrapper for CreatePipeline that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2002 of file SageMakerClient.h.
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint .
The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.
nullptr
An Async wrapper for CreatePresignedDomainUrl that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2056 of file SageMakerClient.h.
A Callable wrapper for CreatePresignedDomainUrl that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2047 of file SageMakerClient.h.
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
nullptr
An Async wrapper for CreatePresignedMlflowTrackingServerUrl that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2084 of file SageMakerClient.h.
A Callable wrapper for CreatePresignedMlflowTrackingServerUrl that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2075 of file SageMakerClient.h.
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
nullptr
An Async wrapper for CreatePresignedNotebookInstanceUrl that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2129 of file SageMakerClient.h.
A Callable wrapper for CreatePresignedNotebookInstanceUrl that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2120 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateProcessingJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2154 of file SageMakerClient.h.
A Callable wrapper for CreateProcessingJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2145 of file SageMakerClient.h.
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.
nullptr
An Async wrapper for CreateProject that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2181 of file SageMakerClient.h.
A Callable wrapper for CreateProject that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2172 of file SageMakerClient.h.
Creates a private space or a space used for real time collaboration in a domain.
nullptr
An Async wrapper for CreateSpace that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2207 of file SageMakerClient.h.
A Callable wrapper for CreateSpace that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2198 of file SageMakerClient.h.
nullptr
An Async wrapper for CreateStudioLifecycleConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2233 of file SageMakerClient.h.
A Callable wrapper for CreateStudioLifecycleConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2224 of file SageMakerClient.h.
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request hyperparameter variable or plain text fields.
InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.
RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.
StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
Environment - The environment variables to set in the Docker container.
Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.
For more information about SageMaker, see How It Works.
nullptr
An Async wrapper for CreateTrainingJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2307 of file SageMakerClient.h.
A Callable wrapper for CreateTrainingJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2298 of file SageMakerClient.h.
Creates a new training plan in SageMaker to reserve compute capacity.
Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure.
How it works
Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures.
Plan creation workflow
Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation.
They create a plan that best matches their needs using the ID of the plan offering they want to use.
After successful upfront payment, the plan's status becomes Scheduled.
The plan can be used to:
Queue training jobs.
Allocate to an instance group of a SageMaker HyperPod cluster.
When the plan start date arrives, it becomes Active. Based on available reserved capacity:
Training jobs are launched.
Instance groups are provisioned.
Plan composition
A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .
nullptr
An Async wrapper for CreateTrainingPlan that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2359 of file SageMakerClient.h.
A Callable wrapper for CreateTrainingPlan that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2350 of file SageMakerClient.h.
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.
TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources - Identifies the ML compute instances and AMI image versions for the transform job.
For more information about how batch transformation works, see Batch Transform.
nullptr
An Async wrapper for CreateTransformJob that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2404 of file SageMakerClient.h.
A Callable wrapper for CreateTransformJob that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2395 of file SageMakerClient.h.
Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
nullptr
An Async wrapper for CreateTrial that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2443 of file SageMakerClient.h.
A Callable wrapper for CreateTrial that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2434 of file SageMakerClient.h.
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
nullptr
An Async wrapper for CreateTrialComponent that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2478 of file SageMakerClient.h.
A Callable wrapper for CreateTrialComponent that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2469 of file SageMakerClient.h.
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
nullptr
An Async wrapper for CreateUserProfile that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2510 of file SageMakerClient.h.
A Callable wrapper for CreateUserProfile that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2501 of file SageMakerClient.h.
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
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An Async wrapper for CreateWorkforce that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2553 of file SageMakerClient.h.
A Callable wrapper for CreateWorkforce that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2544 of file SageMakerClient.h.
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.
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An Async wrapper for CreateWorkteam that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2581 of file SageMakerClient.h.
A Callable wrapper for CreateWorkteam that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2572 of file SageMakerClient.h.
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An Async wrapper for DeleteAction that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2606 of file SageMakerClient.h.
A Callable wrapper for DeleteAction that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2597 of file SageMakerClient.h.
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An Async wrapper for DeleteAlgorithm that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2632 of file SageMakerClient.h.
A Callable wrapper for DeleteAlgorithm that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2623 of file SageMakerClient.h.
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An Async wrapper for DeleteApp that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2657 of file SageMakerClient.h.
A Callable wrapper for DeleteApp that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2648 of file SageMakerClient.h.
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An Async wrapper for DeleteAppImageConfig that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2682 of file SageMakerClient.h.
A Callable wrapper for DeleteAppImageConfig that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2673 of file SageMakerClient.h.
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const
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An Async wrapper for DeleteArtifact that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2708 of file SageMakerClient.h.
{})
const
A Callable wrapper for DeleteArtifact that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2699 of file SageMakerClient.h.
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An Async wrapper for DeleteAssociation that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2733 of file SageMakerClient.h.
A Callable wrapper for DeleteAssociation that returns a future to the operation so that it can be executed in parallel to other requests.
Definition at line 2724 of file SageMakerClient.h.
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An Async wrapper for DeleteCluster that queues the request into a thread executor and triggers associated callback when operation has finished.
Definition at line 2758 of file SageMakerClient.h.