Module aws_sdk_sagemaker::model [−][src]
Expand description
Data structures used by operation inputs/outputs.
Modules
Structs
A structure describing the source of an action.
Lists the properties of an action. An action represents an action
Edge Manager agent version.
This API is not supported.
Specifies the training algorithm to use in a CreateTrainingJob
Specifies the validation and image scan statuses of the algorithm.
Represents the overall status of an algorithm.
Provides summary information about an algorithm.
Defines a training job and a batch transform job that Amazon SageMaker runs to validate your
Specifies configurations for one or more training jobs that Amazon SageMaker runs to test the
Configures how labels are consolidated across human workers and processes output data.
Details about an Amazon SageMaker app.
The configuration for running a SageMaker image as a KernelGateway app.
Configuration to run a processing job in a specified container image.
A structure describing the source of an artifact.
The ID and ID type of an artifact source.
Lists a summary of the properties of an artifact. An artifact represents a URI
Lists a summary of the properties of an association. An association is an entity that
Configures the behavior of the client used by Amazon SageMaker to interact with the
Specifies configuration for how an endpoint performs asynchronous inference.
Specifies the configuration for notifications of inference results for asynchronous inference.
Specifies the configuration for asynchronous inference invocation outputs.
Configuration for Athena Dataset Definition input.
Information about a candidate produced by an AutoML training job, including its status,
Information about the steps for a candidate and what step it is working on.
A channel is a named input source that training algorithms can consume. For more
A list of container definitions that describe the different containers that make up an
The data source for the Autopilot job.
The artifacts that are generated during an AutoML job.
How long a job is allowed to run, or how many candidates a job is allowed to
A collection of settings used for an AutoML job.
Specifies a metric to minimize or maximize as the objective of a job.
Provides a summary about an AutoML job.
The output data configuration.
The reason for a partial failure of an AutoML job.
Security options.
The Amazon S3 data source.
Currently, the AutoRollbackConfig API is not supported.
The error code and error description associated with the resource.
Provides summary information about the model package.
Contains bias metrics for a model.
Currently, the BlueGreenUpdatePolicy API is not supported.
Details on the cache hit of a pipeline execution step.
Metadata about a callback step.
The location of artifacts for an AutoML candidate job.
The properties of an AutoML candidate job.
Currently, the CapacitySize API is not supported.
A list of categorical hyperparameters to tune.
Defines the possible values for a categorical hyperparameter.
A channel is a named input source that training algorithms can consume.
Defines a named input source, called a channel, to be used by an algorithm.
Contains information about the output location for managed spot training checkpoint
Specifies summary information about a Git repository.
Use this parameter to configure your Amazon Cognito workforce.
Identifies a Amazon Cognito user group. A user group can be used in on or more work
Configuration information for the Debugger output tensor collections.
A summary of a model compilation job.
Metadata for a Condition step.
Describes the container, as part of model definition.
A structure describing the source of a context.
Lists a summary of the properties of a context. A context provides a logical grouping
A list of continuous hyperparameters to tune.
Defines the possible values for a continuous hyperparameter.
A custom SageMaker image. For more information, see
The meta data of the Glue table which serves as data catalog for the
The data structure used to specify the data to be used for inference in a batch
Information about the container that a data quality monitoring job runs.
Configuration for monitoring constraints and monitoring statistics. These baseline
The input for the data quality monitoring job. Currently endpoints are supported for
Describes the location of the channel data.
Configuration for Dataset Definition inputs. The Dataset Definition input must specify
Configuration information for the Debugger hook parameters, metric and tensor collections, and
Configuration information for SageMaker Debugger rules for debugging. To learn more about
Information about the status of the rule evaluation.
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.
Currently, the DeploymentConfig API is not supported.
Specifies weight and capacity values for a production variant.
Information of a particular device.
Summary of the device fleet.
Status of devices.
Summary of the device.
The domain's details.
A collection of settings that apply to the SageMaker Domain. These settings are specified through the CreateDomain API call.
A collection of Domain configuration settings to update.
The model on the edge device.
Status of edge devices with this model.
Summary of model on edge device.
The output configuration.
Summary of edge packaging job.
The output of a SageMaker Edge Manager deployable resource.
A hosted endpoint for real-time inference.
Provides summary information for an endpoint configuration.
Input object for the endpoint
Provides summary information for an endpoint.
The properties of an experiment as returned by the Search API.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when
The source of the experiment.
A summary of the properties of an experiment. To get the complete set of properties, call
Contains explainability metrics for a model.
A list of features. You must include FeatureName and
Amazon SageMaker Feature Store stores features in a collection called Feature Group.
The name, Arn, CreationTime, FeatureGroup values,
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
Specifies a file system data source for a channel.
A conditional statement for a search expression that includes a resource property, a
The best candidate result from an AutoML training job.
Shows the final value for the
Contains information about where human output will be stored.
Contains summary information about the flow definition.
Specifies configuration details for a Git repository in your Amazon Web Services account.
Specifies configuration details for a Git repository when the repository is
Defines under what conditions SageMaker creates a human loop. Used within . See for the required
Provides information about how and under what conditions SageMaker creates a human loop. If HumanLoopActivationConfig is not given, then all requests go to humans.
Describes the work to be performed by human workers.
Container for configuring the source of human task requests.
Information required for human workers to complete a labeling task.
Container for human task user interface information.
Specifies
Defines a hyperparameter to be used by an algorithm.
Specifies
Configures a hyperparameter tuning job.
Defines the objective metric for a hyperparameter tuning job.
Provides summary information about a hyperparameter tuning job.
Specifies the configuration for a hyperparameter tuning job that uses one or more
A SageMaker image. A SageMaker image represents a set of container images that are derived from
Specifies whether the model container is in Amazon ECR or a private Docker registry
A version of a SageMaker Image. A version represents an existing container
Specifies details about how containers in a multi-container endpoint are run.
Defines how to perform inference generation after a training job is run.
Contains information about the location of input model artifacts, the name and
For a hyperparameter of the integer type, specifies the range
Defines the possible values for an integer hyperparameter.
The JupyterServer app settings.
The KernelGateway app settings.
The configuration for the file system and kernels in a SageMaker image running as a
The specification of a Jupyter kernel.
Provides a breakdown of the number of objects labeled.
Provides counts for human-labeled tasks in the labeling job.
Provides configuration information for auto-labeling of your data objects. A
Attributes of the data specified by the customer. Use these to describe the data to be
Provides information about the location of input data.
Provides summary information for a work team.
Input configuration information for a labeling job.
Specifies the location of the output produced by the labeling job.
Output configuration information for a labeling job.
Configure encryption on the storage volume attached to the ML compute instance used to
The Amazon S3 location of the input data objects.
An Amazon SNS data source used for streaming labeling jobs.
A set of conditions for stopping a labeling job. If any of the conditions are met, the
Provides summary information about a labeling job.
Metadata for a Lambda step.
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
Metadata properties of the tracking entity, trial, or trial component.
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
Information about the metric for a candidate produced by an AutoML job.
Specifies a metric that the training algorithm
Provides information about the location that is configured for storing model
Docker container image configuration object for the model bias job.
The configuration for a baseline model bias job.
Inputs for the model bias job.
Configures the timeout and maximum number of retries for processing a transform job
Data quality constraints and statistics for a model.
Specifies how to generate the endpoint name for an automatic one-click Autopilot model
Provides information about the endpoint of the model deployment.
Provides information to verify the integrity of stored model artifacts.
Docker container image configuration object for the model explainability job.
The configuration for a baseline model explainability job.
Inputs for the model explainability job.
Contains metrics captured from a model.
A versioned model that can be deployed for SageMaker inference.
Describes the Docker container for the model package.
A group of versioned models in the model registry.
Summary information about a model group.
Specifies the validation and image scan statuses of the model package.
Represents the overall status of a model package.
Provides summary information about a model package.
Contains data, such as the inputs and targeted instance types that are used in the
Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
Model quality statistics and constraints.
Container image configuration object for the monitoring job.
Configuration for monitoring constraints and monitoring statistics. These baseline
The input for the model quality monitoring job. Currently endponts are supported for
Metadata for Model steps.
Provides summary information about a model.
Container image configuration object for the monitoring job.
Configuration for monitoring constraints and monitoring statistics. These baseline
Configuration for the cluster used to run model monitoring jobs.
The constraints resource for a monitoring job.
Summary of information about the last monitoring job to run.
The ground truth labels for the dataset used for the monitoring job.
The inputs for a monitoring job.
Defines the monitoring job.
Summary information about a monitoring job.
The networking configuration for the monitoring job.
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Information about where and how you want to store the results of a monitoring
A schedule for a model monitoring job. For information about model monitor, see
Configures the monitoring schedule and defines the monitoring job.
Summarizes the monitoring schedule.
The statistics resource for a monitoring job.
A time limit for how long the monitoring job is allowed to run before stopping.
Specifies additional configuration for hosting multi-model endpoints.
The VpcConfig configuration object that specifies the VPC that you
A list of nested Filter objects. A resource must satisfy the conditions
Networking options for a job, such as network traffic encryption between containers,
Provides a summary of a notebook instance lifecycle configuration.
Contains the notebook instance lifecycle configuration script.
Provides summary information for an Amazon SageMaker notebook instance.
Configures Amazon SNS notifications of available or expiring work items for work
Specifies the number of training jobs that this hyperparameter tuning job launched,
The configuration of an OfflineStore.
The status of OfflineStore.
Use this parameter to configure your OIDC Identity Provider (IdP).
Your OIDC IdP workforce configuration.
A list of user groups that exist in your OIDC Identity Provider (IdP).
Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or
The security configuration for OnlineStore.
Contains information about the output location for the compiled model and the target
Provides information about how to store model training results (model
An output parameter of a pipeline step.
Assigns a value to a named Pipeline parameter.
Defines the possible values for categorical, continuous, and integer hyperparameters
Specifies ranges of integer, continuous, and categorical hyperparameters that a
The trial that a trial component is associated with and the experiment the trial is part
A previously completed or stopped hyperparameter tuning job to be used as a starting
A SageMaker Model Building Pipeline instance.
An execution of a pipeline.
An execution of a step in a pipeline.
Metadata for a step execution.
A pipeline execution summary.
Specifies the names of the experiment and trial created by a pipeline.
A summary of a pipeline.
Configuration for the cluster used to run a processing job.
Configuration for processing job outputs in Amazon SageMaker Feature Store.
The inputs for a processing job. The processing input must specify exactly one of either
An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information,
Metadata for a processing job step.
Summary of information about a processing job.
Describes the results of a processing job. The processing output must specify exactly one of
Configuration for uploading output from the processing container.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a
Configuration for downloading input data from Amazon S3 into the processing container.
Configuration for uploading output data to Amazon S3 from the processing container.
Configures conditions under which the processing job should be stopped, such as how long
Identifies a model that you want to host and the resources chosen to deploy for
Specifies configuration for a core dump from the model container when the process
Describes weight and capacities for a production variant associated with an
Configuration information for Debugger system monitoring, framework profiling, and
Configuration information for updating the Debugger profile parameters, system and framework metrics configurations, and
Configuration information for profiling rules.
Information about the status of the rule evaluation.
The properties of a project as returned by the Search API.
Information about a project.
Part of the SuggestionQuery type. Specifies a hint for retrieving property
A property name returned from a GetSearchSuggestions call that specifies
A key value pair used when you provision a project as a service catalog product. For
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
A collection of settings that apply to an RSessionGateway app.
A collection of settings that configure user interaction with the RStudioServerPro app. RStudioServerProAppSettings cannot be updated. The RStudioServerPro app must be deleted and a new one created to make any changes.
A collection of settings that configure the RStudioServerPro Domain-level app.
A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.
Configuration for Redshift Dataset Definition input.
Metadata for a register model job step.
Contains input values for a task.
A description of an error that occurred while rendering the template.
Specifies an authentication configuration for the private docker registry where your
The resolved attributes.
Describes the resources, including ML compute instances and ML storage volumes, to
Specifies the maximum number of
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that
The retention policy for data stored on an Amazon Elastic File System (EFS) volume.
The retry strategy to use when a training job fails due to an
Describes the S3 data source.
The Amazon Simple Storage (Amazon S3) location and and security configuration for OfflineStore.
Configuration details about the monitoring schedule.
A multi-expression that searches for the specified resource or resources in a search. All resource
A single resource returned as part of the Search API response.
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides
Details of a provisioned service catalog product. For information about service catalog,
Details that you specify to provision a service catalog product. For information about
Details that you specify to provision a service catalog product.
Specifies options for sharing SageMaker Studio notebooks. These settings are
A configuration for a shuffle option for input data in a channel. If you use
Specifies an algorithm that was used to create the model package. The algorithm must
A list of algorithms that were used to create a model package.
A list of IP address ranges (CIDRs). Used to create an allow
Specifies a limit to how long a model training job or model compilation job
Details of the Studio Lifecycle Configuration.
Describes a work team of a vendor that does the a labelling job.
Specified in the GetSearchSuggestions request.
A tag object that consists of a key and an optional value, used to manage metadata
Contains information about a target platform that you want your model to run on, such
The TensorBoard app settings.
Configuration of storage locations for the Debugger TensorBoard output data.
Currently, the TrafficRoutingConfig API is not supported.
Contains information about a training job.
Defines the input needed to run a training job using the algorithm.
The numbers of training jobs launched by a hyperparameter tuning job, categorized by
Metadata for a training job step.
Provides summary information about a training job.
Defines how the algorithm is used for a training job.
Describes the location of the channel data.
Describes the input source of a transform job and the way the transform job consumes
A batch transform job. For information about SageMaker batch transform, see Use Batch
Defines the input needed to run a transform job using the inference specification
Metadata for a transform job step.
Provides a
Describes the results of a transform job.
Describes the resources, including ML instance types and ML instance count, to use for
Describes the S3 data source.
The properties of a trial component as returned by the Search
Represents an input or output artifact of a trial component. You specify
A summary of the metrics of a trial component.
A short summary of a trial component.
The Amazon Resource Name (ARN) and job type of the source of a trial component.
Detailed information about the source of a trial component. Either
The status of the trial component.
A summary of the properties of a trial component. To get all the properties, call the
The source of the trial.
A summary of the properties of a trial. To get the complete set of properties, call the
The job completion criteria.
Metadata for a tuning step.
Provided configuration information for the worker UI for a labeling job. Provide
The Liquid template for the worker user interface.
Container for user interface template information.
Represents an amount of money in United States dollars.
Information about the user who created or modified an experiment, trial, trial
The user profile details.
A collection of settings that apply to users of Amazon SageMaker Studio. These settings are
Specifies a production variant property type for an Endpoint.
Specifies a VPC that your training jobs and hosted models have access to. Control
A single private workforce, which is automatically created when you create your first
Provides details about a labeling work team.
Enums
Note: ActionStatus::Unknown has been renamed to ::UnknownValue.
The compression used for Athena query results.
The data storage format for Athena query results.
The strategy hyperparameter tuning uses to
The compression used for Redshift query results.
The data storage format for Redshift query results.
The training input mode that the algorithm supports. For more information about input modes, see
The value of a hyperparameter. Only one of NumberValue or