AWS SDK Version 3 for .NET
API Reference

AWS services or capabilities described in AWS Documentation may vary by region/location. Click Getting Started with Amazon AWS to see specific differences applicable to the China (Beijing) Region.

Classes

NameDescription
Class AddTagsRequest

Container for the parameters to the AddTags operation. Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Class AddTagsResponse

This is the response object from the AddTags operation.

Class AlgorithmSpecification

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see your-algorithms.

Class CategoricalParameterRange

A list of categorical hyperparameters to tune.

Class Channel

A channel is a named input source that training algorithms can consume.

Class ContainerDefinition

Describes the container, as part of model definition.

Class ContinuousParameterRange

A list of continuous hyperparameters to tune.

Class CreateEndpointConfigRequest

Container for the parameters to the CreateEndpointConfig operation. Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.

Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define one or more ProductionVariants, each of which identifies a model. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Class CreateEndpointConfigResponse

This is the response object from the CreateEndpointConfig operation.

Class CreateEndpointRequest

Container for the parameters to the CreateEndpoint operation. Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API only for hosting models using Amazon SageMaker hosting services.

The endpoint name must be unique within an AWS Region in your AWS account.

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

Class CreateEndpointResponse

This is the response object from the CreateEndpoint operation.

Class CreateHyperParameterTuningJobRequest

Container for the parameters to the CreateHyperParameterTuningJob operation. Starts a hyperparameter tuning job.

Class CreateHyperParameterTuningJobResponse

This is the response object from the CreateHyperParameterTuningJob operation.

Class CreateModelRequest

Container for the parameters to the CreateModel operation. Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production.

Use this API to create a model only if you want to use Amazon SageMaker hosting services. 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.

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. 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.

Class CreateModelResponse

This is the response object from the CreateModel operation.

Class CreateNotebookInstanceLifecycleConfigRequest

Container for the parameters to the CreateNotebookInstanceLifecycleConfig operation. Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Class CreateNotebookInstanceLifecycleConfigResponse

This is the response object from the CreateNotebookInstanceLifecycleConfig operation.

Class CreateNotebookInstanceRequest

Container for the parameters to the CreateNotebookInstance operation. 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:

  1. Creates a network interface in the Amazon SageMaker VPC.

  2. (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.

  3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

Class CreateNotebookInstanceResponse

This is the response object from the CreateNotebookInstance operation.

Class CreatePresignedNotebookInstanceUrlRequest

Container for the parameters to the CreatePresignedNotebookInstanceUrl operation. 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.

Class CreatePresignedNotebookInstanceUrlResponse

This is the response object from the CreatePresignedNotebookInstanceUrl operation.

Class CreateTrainingJobRequest

Container for the parameters to the CreateTrainingJob operation. Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

  • AlgorithmSpecification - Identifies the training algorithm to use.

  • HyperParameters - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

  • InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.

  • OutputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

  • ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

  • RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.

  • StoppingCondition - Sets a duration for training. Use this parameter to cap model training costs.

For more information about Amazon SageMaker, see How It Works.

Class CreateTrainingJobResponse

This is the response object from the CreateTrainingJob operation.

Class CreateTransformJobRequest

Container for the parameters to the CreateTransformJob operation. 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 within an AWS Region in an AWS account.

  • TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • TransformResources - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works Amazon SageMaker, see How It Works.

Class CreateTransformJobResponse

This is the response object from the CreateTransformJob operation.

Class DataSource

Describes the location of the channel data.

Class DeleteEndpointConfigRequest

Container for the parameters to the DeleteEndpointConfig operation. Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

Class DeleteEndpointConfigResponse

This is the response object from the DeleteEndpointConfig operation.

Class DeleteEndpointRequest

Container for the parameters to the DeleteEndpoint operation. 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.

Class DeleteEndpointResponse

This is the response object from the DeleteEndpoint operation.

Class DeleteModelRequest

Container for the parameters to the DeleteModel operation. 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.

Class DeleteModelResponse

This is the response object from the DeleteModel operation.

Class DeleteNotebookInstanceLifecycleConfigRequest

Container for the parameters to the DeleteNotebookInstanceLifecycleConfig operation. Deletes a notebook instance lifecycle configuration.

Class DeleteNotebookInstanceLifecycleConfigResponse

This is the response object from the DeleteNotebookInstanceLifecycleConfig operation.

Class DeleteNotebookInstanceRequest

Container for the parameters to the DeleteNotebookInstance operation. 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.

Class DeleteNotebookInstanceResponse

This is the response object from the DeleteNotebookInstance operation.

Class DeleteTagsRequest

Container for the parameters to the DeleteTags operation. Deletes the specified tags from an Amazon SageMaker resource.

To list a resource's tags, use the ListTags API.

Class DeleteTagsResponse

This is the response object from the DeleteTags operation.

Class DeployedImage

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.

If you used the registry/repository[:tag] form to to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

Class DescribeEndpointConfigRequest

Container for the parameters to the DescribeEndpointConfig operation. Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Class DescribeEndpointConfigResponse

This is the response object from the DescribeEndpointConfig operation.

Class DescribeEndpointRequest

Container for the parameters to the DescribeEndpoint operation. Returns the description of an endpoint.

Class DescribeEndpointResponse

This is the response object from the DescribeEndpoint operation.

Class DescribeHyperParameterTuningJobRequest

Container for the parameters to the DescribeHyperParameterTuningJob operation. Gets a description of a hyperparameter tuning job.

Class DescribeHyperParameterTuningJobResponse

This is the response object from the DescribeHyperParameterTuningJob operation.

Class DescribeModelRequest

Container for the parameters to the DescribeModel operation. Describes a model that you created using the CreateModel API.

Class DescribeModelResponse

This is the response object from the DescribeModel operation.

Class DescribeNotebookInstanceLifecycleConfigRequest

Container for the parameters to the DescribeNotebookInstanceLifecycleConfig operation. Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Class DescribeNotebookInstanceLifecycleConfigResponse

This is the response object from the DescribeNotebookInstanceLifecycleConfig operation.

Class DescribeNotebookInstanceRequest

Container for the parameters to the DescribeNotebookInstance operation. Returns information about a notebook instance.

Class DescribeNotebookInstanceResponse

This is the response object from the DescribeNotebookInstance operation.

Class DescribeTrainingJobRequest

Container for the parameters to the DescribeTrainingJob operation. Returns information about a training job.

Class DescribeTrainingJobResponse

This is the response object from the DescribeTrainingJob operation.

Class DescribeTransformJobRequest

Container for the parameters to the DescribeTransformJob operation. Returns information about a transform job.

Class DescribeTransformJobResponse

This is the response object from the DescribeTransformJob operation.

Class DesiredWeightAndCapacity

Specifies weight and capacity values for a production variant.

Class EndpointConfigSummary

Provides summary information for an endpoint configuration.

Class EndpointSummary

Provides summary information for an endpoint.

Class FinalHyperParameterTuningJobObjectiveMetric

Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

Class HyperParameterAlgorithmSpecification

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Class HyperParameterTrainingJobDefinition

Defines the training jobs launched by a hyperparameter tuning job.

Class HyperParameterTrainingJobSummary

Specifies summary information about a training job.

Class HyperParameterTuningJobConfig

Configures a hyperparameter tuning job.

Class HyperParameterTuningJobObjective

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

Class HyperParameterTuningJobSummary

Provides summary information about a hyperparameter tuning job.

Class IntegerParameterRange

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

Class ListEndpointConfigsRequest

Container for the parameters to the ListEndpointConfigs operation. Lists endpoint configurations.

Class ListEndpointConfigsResponse

This is the response object from the ListEndpointConfigs operation.

Class ListEndpointsRequest

Container for the parameters to the ListEndpoints operation. Lists endpoints.

Class ListEndpointsResponse

This is the response object from the ListEndpoints operation.

Class ListHyperParameterTuningJobsRequest

Container for the parameters to the ListHyperParameterTuningJobs operation. Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Class ListHyperParameterTuningJobsResponse

This is the response object from the ListHyperParameterTuningJobs operation.

Class ListModelsRequest

Container for the parameters to the ListModels operation. Lists models created with the CreateModel API.

Class ListModelsResponse

This is the response object from the ListModels operation.

Class ListNotebookInstanceLifecycleConfigsRequest

Container for the parameters to the ListNotebookInstanceLifecycleConfigs operation. Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Class ListNotebookInstanceLifecycleConfigsResponse

This is the response object from the ListNotebookInstanceLifecycleConfigs operation.

Class ListNotebookInstancesRequest

Container for the parameters to the ListNotebookInstances operation. Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.

Class ListNotebookInstancesResponse

This is the response object from the ListNotebookInstances operation.

Class ListTagsRequest

Container for the parameters to the ListTags operation. Returns the tags for the specified Amazon SageMaker resource.

Class ListTagsResponse

This is the response object from the ListTags operation.

Class ListTrainingJobsForHyperParameterTuningJobRequest

Container for the parameters to the ListTrainingJobsForHyperParameterTuningJob operation. Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Class ListTrainingJobsForHyperParameterTuningJobResponse

This is the response object from the ListTrainingJobsForHyperParameterTuningJob operation.

Class ListTrainingJobsRequest

Container for the parameters to the ListTrainingJobs operation. Lists training jobs.

Class ListTrainingJobsResponse

This is the response object from the ListTrainingJobs operation.

Class ListTransformJobsRequest

Container for the parameters to the ListTransformJobs operation. Lists transform jobs.

Class ListTransformJobsResponse

This is the response object from the ListTransformJobs operation.

Class MetricDefinition

Specifies a metric that the training algorithm writes to stderr or stdout. Amazon SageMakerHyperparamter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

Class ModelArtifacts

Provides information about the location that is configured for storing model artifacts.

Class ModelSummary

Provides summary information about a model.

Class NotebookInstanceLifecycleConfigSummary

Provides a summary of a notebook instance lifecycle configuration.

Class NotebookInstanceLifecycleHook

Contains the notebook instance lifecycle configuration script.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Class NotebookInstanceSummary

Provides summary information for an Amazon SageMaker notebook instance.

Class ObjectiveStatusCounters

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

Class OutputDataConfig

Provides information about how to store model training results (model artifacts).

Class ParameterRanges

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.

Class ProductionVariant

Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

Class ProductionVariantSummary

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

Class ResourceConfig

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

Class ResourceInUseException

SageMaker exception

Class ResourceLimitExceededException

SageMaker exception

Class ResourceLimits

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

Class ResourceNotFoundException

SageMaker exception

Class S3DataSource

Describes the S3 data source.

Class SecondaryStatusTransition

Specifies a secondary status the job has transitioned into. It includes a start timestamp and later an end timestamp. The end timestamp is added either after the job transitions to a different secondary status or after the job has ended.

Class StartNotebookInstanceRequest

Container for the parameters to the StartNotebookInstance operation. 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.

Class StartNotebookInstanceResponse

This is the response object from the StartNotebookInstance operation.

Class StopHyperParameterTuningJobRequest

Container for the parameters to the StopHyperParameterTuningJob operation. 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.

Class StopHyperParameterTuningJobResponse

This is the response object from the StopHyperParameterTuningJob operation.

Class StopNotebookInstanceRequest

Container for the parameters to the StopNotebookInstance operation. Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume.

To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

Class StopNotebookInstanceResponse

This is the response object from the StopNotebookInstance operation.

Class StoppingCondition

Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost.

Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (CreateModel).

Class StopTrainingJobRequest

Container for the parameters to the StopTrainingJob operation. Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.

When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.

Class StopTrainingJobResponse

This is the response object from the StopTrainingJob operation.

Class StopTransformJobRequest

Container for the parameters to the StopTransformJob operation. 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.

Class StopTransformJobResponse

This is the response object from the StopTransformJob operation.

Class Tag

Describes a tag.

Class TrainingJobStatusCounters

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

Class TrainingJobSummary

Provides summary information about a training job.

Class TransformDataSource

Describes the location of the channel data.

Class TransformInput

Describes the input source of a transform job and the way the transform job consumes it.

Class TransformJobSummary

Provides a summary information for a transform job. Multiple TransformJobSummary objects are returned as a list after calling ListTransformJobs.

Class TransformOutput

Describes the results of a transform job output.

Class TransformResources

Describes the resources, including ML instance types and ML instance count, to use for transform job.

Class TransformS3DataSource

Describes the S3 data source.

Class UpdateEndpointRequest

Container for the parameters to the UpdateEndpoint operation. Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).

When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

You cannot update an endpoint with the current EndpointConfig. To update an endpoint, you must create a new EndpointConfig.

Class UpdateEndpointResponse

This is the response object from the UpdateEndpoint operation.

Class UpdateEndpointWeightsAndCapacitiesRequest

Container for the parameters to the UpdateEndpointWeightsAndCapacities operation. 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.

Class UpdateEndpointWeightsAndCapacitiesResponse

This is the response object from the UpdateEndpointWeightsAndCapacities operation.

Class UpdateNotebookInstanceLifecycleConfigRequest

Container for the parameters to the UpdateNotebookInstanceLifecycleConfig operation. Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Class UpdateNotebookInstanceLifecycleConfigResponse

This is the response object from the UpdateNotebookInstanceLifecycleConfig operation.

Class UpdateNotebookInstanceRequest

Container for the parameters to the UpdateNotebookInstance operation. Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.

Class UpdateNotebookInstanceResponse

This is the response object from the UpdateNotebookInstance operation.

Class VpcConfig

Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see host-vpc and train-vpc.