Module aws_sdk_sagemaker::types
source · Expand description
Data structures used by operation inputs/outputs.
Modules§
- Builders
- Error types that Amazon SageMaker Service can respond with.
Structs§
A structure describing the source of an action.
Lists the properties of an action. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Data sources that are available to your model in addition to the one that you specify for
ModelDataSourcewhen you use theCreateModelaction.A data source used for training or inference that is in addition to the input dataset or model data.
Edge Manager agent version.
An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.
Specifies the training algorithm to use in a CreateTrainingJob request.
For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
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 SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.
A collection of settings that configure the Amazon Q experience within the domain.
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 addressable object or data. Some examples are a dataset and a model.
Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
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.
The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.
Stores the configuration information for how a candidate is generated (optional).
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. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel.
A validation dataset must contain the same headers as the training dataset.
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.
The data source for the Autopilot job.
This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling
CreateAutoMLJob, the validation dataset must be less than 2 GB in size.The artifacts that are generated during an AutoML job.
A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).
How long a job is allowed to run, or how many candidates a job is allowed to generate.
A collection of settings used for an AutoML job.
Specifies a metric to minimize or maximize as the objective of an AutoML job.
Metadata for an AutoML job step.
Provides a summary about an AutoML job.
The output data configuration.
The reason for a partial failure of an AutoML job.
The resolved attributes used to configure an AutoML job V2.
Security options.
Describes the Amazon S3 data source.
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Automatic rollback configuration for handling endpoint deployment failures and recovery.
A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields:
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ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.
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ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
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TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
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RetryStrategy: The number of times to retry a training job.
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Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
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ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
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Configuration to control how SageMaker captures inference data for batch transform jobs.
The error code and error description associated with the resource.
Provides summary information about the model package.
Input object for the batch transform job.
A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.
Contains bias metrics for a model.
Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
Details on the cache hit of a pipeline execution step.
Metadata about a callback step.
The location of artifacts for an AutoML candidate job.
Stores the configuration information for how model candidates are generated using an AutoML job V2.
The properties of an AutoML candidate job.
The SageMaker Canvas application settings.
Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet.
For a rollback strategy, if you don't specify the fields in this object, or if you set the
Valueto 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker will by default base64 encode when capturing the data.
Specifies data Model Monitor will capture.
Environment parameters you want to benchmark your load test against.
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 data.
The container for the metadata for the ClarifyCheck step. For more information, see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.
The configuration parameters for the SageMaker Clarify explainer.
The inference configuration parameter for the model container.
The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm.
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The number of records in the baseline data determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.
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ShapBaselineandShapBaselineUriare mutually exclusive parameters. One or the either is required to configure a SHAP baseline.
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The configuration for SHAP analysis using SageMaker Clarify Explainer.
A parameter used to configure the SageMaker Clarify explainer to treat text features as text so that explanations are provided for individual units of text. Required only for natural language processing (NLP) explainability.
Defines the configuration for attaching an additional Amazon Elastic Block Store (EBS) volume to each instance of the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.
Details of an instance group in a SageMaker HyperPod cluster.
The specifications of an instance group that you need to define.
Specifies the placement details for the node in the SageMaker HyperPod cluster, including the Availability Zone and the unique identifier (ID) of the Availability Zone.
Details of an instance in a SageMaker HyperPod cluster.
The lifecycle configuration for a SageMaker HyperPod cluster.
Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.
Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.
Lists a summary of the properties of a SageMaker HyperPod cluster.
The configuration for the file system and kernels in a SageMaker image running as a Code Editor app. The
FileSystemConfigobject is not supported.The Code Editor application settings.
For more information about Code Editor, see Get started with Code Editor in Amazon SageMaker.
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
Specifies summary information about a Git repository.
Use this parameter to configure your Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.
Configuration information for the Amazon SageMaker Debugger output tensor collections.
A summary of a model compilation job.
Metadata for a Condition step.
The configuration used to run the application image container.
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 of other entities.
A list of continuous hyperparameters to tune.
Defines the possible values for a continuous hyperparameter.
A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.
A custom SageMaker image. For more information, see Bring your own SageMaker image.
Details about the POSIX identity that is used for file system operations.
A customized metric.
Configuration to control how SageMaker captures inference data.
The currently active data capture configuration used by your Endpoint.
The meta data of the Glue table which serves as data catalog for the
OfflineStore.The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
Information about the container that a data quality monitoring job runs.
Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
The input for the data quality monitoring job. Currently endpoints are supported for input.
Describes the location of the channel data.
Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either
AthenaDatasetDefinitionorRedshiftDatasetDefinitiontypes.Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the
DebugHookConfigparameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the
DebugRuleConfigurationparameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.Information about the status of the rule evaluation.
A collection of default EBS storage settings that apply to spaces created within a domain or user profile.
A collection of settings that apply to spaces created in the domain.
The default storage settings for a space.
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 specify the image path of the primary container when you created the model hosted in thisProductionVariant, the path resolves to a path of the formregistry/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.The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.
Contains information about a stage in an edge deployment plan.
Contains information summarizing the deployment stage results.
Information that SageMaker Neo automatically derived about the model.
Specifies weight and capacity values for a production variant.
Information of a particular device.
Contains information summarizing device details and deployment status.
Summary of the device fleet.
Contains information about the configurations of selected devices.
Status of devices.
Summary of the device.
The model deployment settings for the SageMaker Canvas application.
In order to enable model deployment for Canvas, the SageMaker Domain's or user profile's Amazon Web Services IAM execution role must have the
AmazonSageMakerCanvasDirectDeployAccesspolicy attached. You can also turn on model deployment permissions through the SageMaker Domain's or user profile's settings in the SageMaker console.A collection of settings that configure the domain's Docker interaction.
The domain's details.
A collection of settings that apply to the
SageMaker Domain. These settings are specified through theCreateDomainAPI call.A collection of
Domainconfiguration settings to update.Represents the drift check baselines that can be used when the model monitor is set using the model package.
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
An object with the recommended values for you to specify when creating an autoscaling policy.
A collection of EBS storage settings that apply to both private and shared spaces.
A directed edge connecting two lineage entities.
Contains information about the configuration of a deployment.
Contains information about the configuration of a model in a deployment.
Contains information summarizing an edge deployment plan.
Contains information summarizing the deployment stage results.
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 file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker Domain.
The configurations and outcomes of an Amazon EMR step execution.
A hosted endpoint for real-time inference.
Provides summary information for an endpoint configuration.
Details about a customer endpoint that was compared in an Inference Recommender job.
Input object for the endpoint
The endpoint configuration for the load test.
The metadata of the endpoint.
The endpoint configuration made by Inference Recommender during a recommendation job.
The performance results from running an Inference Recommender job on an existing endpoint.
Provides summary information for an endpoint.
A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
Specifies the range of environment parameters
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 you call the following APIs:
The source of the experiment.
A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the
ExperimentName.Contains explainability metrics for a model.
A parameter to activate explainers.
The container for the metadata for Fail step.
A list of features. You must include
FeatureNameandFeatureType. Valid featureFeatureTypes areIntegral,FractionalandString.Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
The name, ARN,
CreationTime,FeatureGroupvalues,LastUpdatedTimeandEnableOnlineStoragestatus of aFeatureGroup.The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.
A key-value pair that you specify to describe the feature.
Contains details regarding the file source.
The Amazon Elastic File System 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 Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a
Value, but not anOperator, SageMaker uses the equals operator.In search, there are several property types:
- Metrics
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To define a metric filter, enter a value using the form
"Metrics., where" "accuracy"metric greater than"0.9":{"Name": "Metrics.accuracy","Operator": "GreaterThan","Value": "0.9"} - HyperParameters
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To define a hyperparameter filter, enter a value with the form
"HyperParameters.. Decimal hyperparameter values are treated as a decimal in a comparison if the specified" Valueis also a decimal value. If the specifiedValueis an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a"learning_rate"hyperparameter that is less than"0.5":{"Name": "HyperParameters.learning_rate","Operator": "LessThan","Value": "0.5"} - Tags
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To define a tag filter, enter a value with the form
Tags..
The best candidate result from an AutoML training job.
Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the
HyperParameterTuningJobObjectiveparameter of HyperParameterTuningJobConfig.Contains information about where human output will be stored.
Contains summary information about the flow definition.
The generative AI settings for the SageMaker Canvas application.
Configure these settings for Canvas users starting chats with generative AI foundation models. For more information, see Use generative AI with foundation models.
Specifies configuration details for a Git repository in your Amazon Web Services account.
Specifies configuration details for a Git repository when the repository is updated.
Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.
Information about hub content.
Information about a hub.
The Amazon S3 storage configuration of a hub.
Defines under what conditions SageMaker creates a human loop. Used within CreateFlowDefinition. See HumanLoopActivationConditionsConfig for the required format of activation conditions.
Provides information about how and under what conditions SageMaker creates a human loop. If
HumanLoopActivationConfigis 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 which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.
Defines a hyperparameter to be used by an algorithm.
Defines the training jobs launched by a hyperparameter tuning job.
The container for the summary information about a training job.
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
A structure that contains runtime information about both current and completed hyperparameter tuning jobs.
Configures a hyperparameter tuning job.
The total resources consumed by your hyperparameter tuning job.
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
Typeparameter. If you want to define a custom objective metric, see Define metrics and environment variables.An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.
The configuration for a training job launched by a hyperparameter tuning job. Choose
Bayesianfor Bayesian optimization, andRandomfor random search optimization. For more advanced use cases, useHyperband, which evaluates objective metrics for training jobs after every epoch. For more information about strategies, see How Hyperparameter Tuning Works.Provides summary information about a hyperparameter tuning job.
Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs.
HyperParameterTuningResourceConfigis similar toResourceConfig, but has the additionalInstanceConfigsandAllocationStrategyfields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection.HyperParameterTuningResourceConfigsupports the capabilities ofResourceConfigwith the exception ofKeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.The configuration for
Hyperband, a multi-fidelity based hyperparameter tuning strategy.Hyperbanduses the final and intermediate results of a training job to dynamically allocate resources to utilized hyperparameter configurations while automatically stopping under-performing configurations. This parameter should be provided only ifHyperbandis selected as theStrategyConfigunder theHyperParameterTuningJobConfigAPI.The IAM Identity details associated with the user. These details are associated with model package groups, model packages and project entities only.
Use this parameter to specify a supported global condition key that is added to the IAM policy.
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
A SageMaker image. A SageMaker image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker
ImageVersion.The collection of settings used by an AutoML job V2 for the image classification problem type.
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
A version of a SageMaker
Image. A version represents an existing container image.Defines the compute resources to allocate to run a model that you assign to an inference component. These resources include CPU cores, accelerators, and memory.
Defines a container that provides the runtime environment for a model that you deploy with an inference component.
Details about the resources that are deployed with this inference component.
Runtime settings for a model that is deployed with an inference component.
Details about the runtime settings for the model that is deployed with the inference component.
Details about the resources to deploy with this inference component, including the model, container, and compute resources.
Details about the resources that are deployed with this inference component.
Settings that take effect while the model container starts up.
A summary of the properties of an inference component.
Specifies details about how containers in a multi-container endpoint are run.
The Amazon S3 location and configuration for storing inference request and response data.
The start and end times of an inference experiment.
The maximum duration that you can set for an inference experiment is 30 days.
Lists a summary of properties of an inference experiment.
Configuration information specifying which hub contents have accessible deployment options.
The metrics for an existing endpoint compared in an Inference Recommender job.
A list of recommendations made by Amazon SageMaker Inference Recommender.
A structure that contains a list of recommendation jobs.
A returned array object for the
Stepsresponse field in the ListInferenceRecommendationsJobSteps API command.Defines how to perform inference generation after a training job is run.
Configuration information for the infrastructure health check of a training job. A SageMaker-provided health check tests the health of instance hardware and cluster network connectivity.
Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
Information on the IMDS configuration of the notebook instance
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Defines the possible values for an integer hyperparameter.
The configuration for the file system and kernels in a SageMaker image running as a JupyterLab app. The
FileSystemConfigobject is not supported.The settings for the JupyterLab application.
The JupyterServer app settings.
The Amazon SageMaker Canvas application setting where you configure document querying.
The KernelGateway app settings.
The configuration for the file system and kernels in a SageMaker image running as a KernelGateway app.
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
LabelingJobAlgorithmsConfigobject must be supplied in order to use auto-labeling.Attributes of the data specified by the customer. Use these to describe the data to be labeled.
Provides information about the location of input data.
You must specify at least one of the following:
S3DataSourceorSnsDataSource.Use
SnsDataSourceto specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job.Use
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto create a streaming labeling job.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 run automated data labeling model training and inference.
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 job is automatically stopped. You can use these conditions to control the cost of data labeling.
Labeling jobs fail after 30 days with an appropriate client error message.
Provides summary information about a labeling job.
Metadata for a Lambda step.
A value that indicates whether the update was successful.
Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity resources.
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 writes to
stderrorstdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in theHyperParameterTrainingJobDefinitionAPI to evaluate job performance during hyperparameter tuning.Details about the metrics source.
The properties of a model as returned by the Search API.
The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the
ModelAccessConfig.-
If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.
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If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.
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Provides information about the location that is configured for storing model artifacts.
Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the
/opt/ml/modeldirectory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.Docker container image configuration object for the model bias job.
The configuration for a baseline model bias job.
Inputs for the model bias job.
An Amazon SageMaker Model Card.
The artifacts of the model card export job.
The summary of the Amazon SageMaker Model Card export job.
Configure the export output details for an Amazon SageMaker Model Card.
Configure the security settings to protect model card data.
A summary of the model card.
A summary of a specific version of the model card.
Configures the timeout and maximum number of retries for processing a transform job invocation.
Settings for the model compilation technique that's applied by a model optimization job.
Defines the model configuration. Includes the specification name and environment parameters.
An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into
InAlertstatus.A model displayed in the Amazon SageMaker Model Dashboard.
The model card for a model displayed in the Amazon SageMaker Model Dashboard.
A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
Data quality constraints and statistics for a model.
Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
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.
The configuration for the infrastructure that the model will be deployed to.
Input object for the model.
The model latency threshold.
Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model).
One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results
A summary of the model metadata.
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.
The model card associated with the model package. Since
ModelPackageModelCardis tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema ofModelCard. TheModelPackageModelCardschema does not includemodel_package_details, andmodel_overviewis composed of themodel_creatorandmodel_artifactproperties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.
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 process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
Specifies batch transform jobs that 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 resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
The input for the model quality monitoring job. Currently endpoints are supported for input for model quality monitoring jobs.
Settings for the model quantization technique that's applied by a model optimization job.
The model registry settings for the SageMaker Canvas application.
Metadata for Model steps.
Provides summary information about a model.
Contains information about the deployment options of a model.
Summary of the deployment configuration of a model.
A list of alert actions taken in response to an alert going into
InAlertstatus.Provides summary information of an alert's history.
Provides summary information about a monitor alert.
Container image configuration object for the monitoring job.
Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.
Configuration for the cluster used to run model monitoring jobs.
The constraints resource for a monitoring job.
Represents the CSV dataset format used when running a monitoring job.
Represents the dataset format used when running 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.
Represents the JSON dataset format used when running a monitoring job.
The networking configuration for the monitoring job.
The output object for a monitoring job.
The output configuration for monitoring jobs.
Represents the Parquet dataset format used when running a monitoring job.
Identifies the resources to deploy for a monitoring job.
Information about where and how you want to store the results of a monitoring job.
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor.
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 want the compilation jobs to connect to. For more information on controlling access to your Amazon S3 buckets used for compilation job, see Give Amazon SageMaker Compilation Jobs Access to Resources in Your Amazon VPC.
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's
InputDataConfigproperty with a specific channel name andS3Uriprefix, define the following filters:-
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}', -
'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
-
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Provides a summary of a notebook instance lifecycle configuration.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the
$PATHenvironment 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/NotebookInstancesin 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.
Provides summary information for an SageMaker notebook instance.
Configures Amazon SNS notifications of available or expiring work items for work teams.
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.
The configuration of an
OfflineStore.Provide an
OfflineStoreConfigin a request toCreateFeatureGroupto create anOfflineStore.To encrypt an
OfflineStoreusing at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, orKMSKeyId, inS3StorageConfig.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). One to ten groups can be used to create a single private work team. When you add a user group to the list of
Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or
KMSKeyId, for at rest data encryption. You can turnOnlineStoreon or off by specifying theEnableOnlineStoreflag at General Assembly.The default value is
False.Updates the feature group online store configuration.
The security configuration for
OnlineStore.The location of the source model to optimize with an optimization job.
The Amazon S3 location of a source model to optimize with an optimization job.
Details for where to store the optimized model that you create with the optimization job.
Summarizes an optimization job by providing some of its key properties.
The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
Output values produced by an optimization job.
A VPC in Amazon VPC that's accessible to an optimized that you create with an optimization job. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
Contains information about the output location for the compiled model and the target device that the model runs on.
TargetDeviceandTargetPlatformare mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from theTargetDevicelist, useTargetPlatformto describe the platform of your edge device andCompilerOptionsif there are specific settings that are required or recommended to use for particular TargetPlatform.Provides information about how to store model training results (model artifacts).
An output parameter of a pipeline step.
The collection of ownership settings for a space.
Specifies summary information about the ownership settings.
Configuration that controls the parallelism of the pipeline. By default, the parallelism configuration specified applies to all executions of the pipeline unless overridden.
Assigns a value to a named Pipeline parameter.
Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
The maximum number of items specified for
Array Membersrefers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.
The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the
VariantStatus, weight and capacity for a production variant associated with an endpoint.Defines the traffic pattern.
A SageMaker Model Building Pipeline instance.
The location of the pipeline definition stored in Amazon S3.
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.
A specification for a predefined metric.
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
S3InputorDatasetDefinitiontypes.An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.
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 either
S3OutputorFeatureStoreOutputtypes.Configuration for uploading output from the processing container.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
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 the processing job has been running. After the condition is met, the processing job is stopped.
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants.
Specifies configuration for a core dump from the model container when the process crashes.
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
Specifies the serverless configuration for an endpoint variant.
Specifies the serverless update concurrency configuration for an endpoint variant.
Describes the status of the production variant.
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the
UpdateEndpointWeightsAndCapacitiesAPI and the endpoint status isUpdating, you get different desired and current values.Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.
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
SuggestionQuerytype. Specifies a hint for retrieving property names that begin with the specified text.A property name returned from a
GetSearchSuggestionscall that specifies a value in thePropertyNameQueryfield.A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
-
0.012
-
0.024
-
0.036
-
0.048
-
0.060
-
0.072
-
0.120
-
0.240
-
0.360
-
0.480
-
0.600
-
0.720
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
-
0.840
-
0.960
-
1.080
-
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
-
2.400
-
2.280
-
2.160
-
2.040
-
1.920
-
1.800
-
1.680
-
1.560
-
1.440
-
1.320
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
-
1.200
-
1.080
-
0.960
-
0.840
-
0.720
-
0.600
-
0.480
-
0.360
-
0.240
-
0.120
-
0.072
-
0.060
-
0.048
-
0.036
-
0.024
-
0.012
-
Container for the metadata for a Quality check step. For more information, see the topic on QualityCheck step in the Amazon SageMaker Developer Guide.
A set of filters to narrow the set of lineage entities connected to the
StartArn(s) returned by theQueryLineageAPI action.A collection of settings that apply to an
RSessionGatewayapp.A collection of settings that configure user interaction with the
RStudioServerProapp.A collection of settings that configure the
RStudioServerProDomain-level app.A collection of settings that update the current configuration for the
RStudioServerProDomain-level app.The infrastructure configuration for deploying the model to a real-time inference endpoint.
The recommended configuration to use for Real-Time Inference.
Provides information about the output configuration for the compiled model.
Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in
ContainerConfigoverride the corresponding fields in the model package. UseContainerConfigif you want to specify these fields for the recommendation job but don't want to edit them in your model package.The details for a specific benchmark from an Inference Recommender job.
The input configuration of the recommendation job.
Provides information about the output configuration for the compiled model.
The configuration for the payload for a recommendation job.
Specifies the maximum number of jobs that can run in parallel and the maximum number of jobs that can run.
Specifies conditions for stopping a job. When a job reaches a stopping condition limit, SageMaker ends the job.
Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
The metrics of recommendations.
Configuration for Redshift Dataset Definition input.
Metadata for a register model job step.
Configuration for remote debugging for the CreateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
Configuration for remote debugging for the UpdateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
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 model image is hosted. Specify a value for this property only if you specified
Vpcas the value for theRepositoryAccessModefield of theImageConfigobject that you passed to a call toCreateModeland the private Docker registry where the model image is hosted requires authentication.The resolved attributes.
A resource catalog containing all of the resources of a specific resource type within a resource owner account. For an example on sharing the Amazon SageMaker Feature Store
DefaultFeatureGroupCatalog, see Share Amazon SageMaker Catalog resource type in the Amazon SageMaker Developer Guide.Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.
The
ResourceConfigto updateKeepAlivePeriodInSeconds. Other fields in theResourceConfigcannot be updated.Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
The retention policy for data stored on an Amazon Elastic File System volume.
The retry strategy to use when a training job fails due to an
InternalServerError.RetryStrategyis specified as part of theCreateTrainingJobandCreateHyperParameterTuningJobrequests. You can add theStoppingConditionparameter to the request to limit the training time for the complete job.Specifies a rolling deployment strategy for updating a SageMaker endpoint.
Describes the S3 data source.
Your input bucket must be in the same Amazon Web Services region as your training job.
Specifies the S3 location of ML model data to deploy.
This object defines the access restrictions to Amazon S3 resources that are included in custom worker task templates using the Liquid filter,
grant_read_access.To learn more about how custom templates are created, see Create custom worker task templates.
The Amazon Simple Storage (Amazon S3) location and security configuration for
OfflineStore.The metric for a scaling policy.
An object where you specify the anticipated traffic pattern for an endpoint.
Configuration details about the monitoring schedule.
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A
SearchExpressioncan contain up to twenty elements.A
SearchExpressioncontains the following components:-
A list of
Filterobjects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. -
A list of
NestedFilterobjects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions. -
A list of
SearchExpressionobjects. A search expression object can be nested in a list of search expression objects. -
A Boolean operator:
AndorOr.
-
A single resource returned as part of the Search API response.
An array element of
SecondaryStatusTransitionsfor DescribeTrainingJob. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.A step selected to run in selective execution mode.
The selective execution configuration applied to the pipeline run.
The ARN from an execution of the current pipeline.
Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.
Contains information about attribute-based access control (ABAC) for a training job. The session chaining configuration uses Amazon Security Token Service (STS) for your training job to request temporary, limited-privilege credentials to tenants. For more information, see Attribute-based access control (ABAC) for multi-tenancy training.
The configuration of
ShadowModeinference experiment type, which specifies a production variant to take all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also specifies the percentage of requests that Amazon SageMaker replicates.The name and sampling percentage of a shadow variant.
Specifies options for sharing Amazon SageMaker Studio notebooks. These settings are specified as part of
DefaultUserSettingswhen theCreateDomainAPI is called, and as part ofUserSettingswhen theCreateUserProfileAPI is called. WhenSharingSettingsis not specified, notebook sharing isn't allowed.A configuration for a shuffle option for input data in a channel. If you use
S3PrefixforS3DataType, the results of the S3 key prefix matches are shuffled. If you useManifestFile, the order of the S3 object references in theManifestFileis shuffled. If you useAugmentedManifestFile, the order of the JSON lines in theAugmentedManifestFileis shuffled. The shuffling order is determined using theSeedvalue.For Pipe input mode, when
ShuffleConfigis specified shuffling is done at the start of every epoch. With large datasets, this ensures that the order of the training data is different for each epoch, and it helps reduce bias and possible overfitting. In a multi-node training job whenShuffleConfigis combined withS3DataDistributionTypeofShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
A list of algorithms that were used to create a model package.
A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
The application settings for a Code Editor space.
The space's details.
The settings for the JupyterLab application within a space.
A collection of space settings.
Specifies summary information about the space settings.
A collection of space sharing settings.
Specifies summary information about the space sharing settings.
The storage settings for a space.
Defines the stairs traffic pattern for an Inference Recommender load test. This pattern type consists of multiple steps where the number of users increases at each step.
Specify either the stairs or phases traffic pattern.
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the
SIGTERMsignal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with
CreateModel.The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
Details of the Amazon SageMaker Studio Lifecycle Configuration.
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
Describes a work team of a vendor that does the labelling job.
Specified in the GetSearchSuggestions request. Limits the property names that are included in the response.
The collection of settings used by an AutoML job V2 for the tabular problem type.
The resolved attributes specific to the tabular problem type.
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of
TargetDevice.A target tracking scaling policy. Includes support for predefined or customized metrics.
When using the PutScalingPolicy API, this parameter is required when you are creating a policy with the policy type
TargetTrackingScaling.The TensorBoard app settings.
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
The collection of settings used by an AutoML job V2 for the text classification problem type.
The collection of settings used by an AutoML job V2 for the text generation problem type.
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.
The resolved attributes specific to the text generation problem type.
Used to set feature group throughput configuration. There are two modes:
ON_DEMANDandPROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.Note:
PROVISIONEDthroughput mode is supported only for feature groups that are offline-only, or use theStandardtier online store.Active throughput configuration of the feature group. There are two modes:
ON_DEMANDandPROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled.Note:
PROVISIONEDthroughput mode is supported only for feature groups that are offline-only, or use theStandardtier online store.The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.
The collection of components that defines the time-series.
The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.
Time series forecast settings for the SageMaker Canvas application.
Transformations allowed on the dataset. Supported transformations are
FillingandAggregation.Fillingspecifies how to add values to missing values in the dataset.Aggregationdefines how to aggregate data that does not align with forecast frequency.The summary of the tracking server to list.
Defines the traffic pattern of the load test.
Defines the traffic routing strategy during an endpoint deployment to shift traffic from the old fleet to the new fleet.
The configuration to use an image from a private Docker registry for a training job.
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 status.
Metadata for a training job step.
Provides summary information about a training job.
An object containing authentication information for a private Docker registry.
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 it.
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.
Defines the input needed to run a transform job using the inference specification specified in the algorithm.
Metadata for a transform job step.
Provides a summary of a transform job. Multiple
TransformJobSummaryobjects are returned as a list after in response to a ListTransformJobs call.Describes the results of a transform job.
Describes the resources, including ML instance types and ML instance count, to use for transform job.
Describes the S3 data source.
The properties of a trial as returned by the Search API.
The properties of a trial component as returned by the Search API.
Represents an input or output artifact of a trial component. You specify
TrialComponentArtifactas part of theInputArtifactsandOutputArtifactsparameters in the CreateTrialComponent request.Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
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
ProcessingJoborTrainingJobis returned.The status of the trial component.
A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the
TrialComponentName.The source of the trial.
A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the
TrialName.Time to live duration, where the record is hard deleted after the expiration time is reached;
ExpiresAt=EventTime+TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.The job completion criteria.
Metadata for a tuning step.
Provided configuration information for the worker UI for a labeling job. Provide either
HumanTaskUiArnorUiTemplateS3Uri.For named entity recognition, 3D point cloud and video frame labeling jobs, use
HumanTaskUiArn.For all other Ground Truth built-in task types and custom task types, use
UiTemplateS3Urito specify the location of a worker task template in Amazon S3.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 component, lineage group, project, or model card.
The user profile details.
A collection of settings that apply to users in a domain. These settings are specified when the
CreateUserProfileAPI is called, and asDefaultUserSettingswhen theCreateDomainAPI is called.SecurityGroupsis aggregated when specified in both calls. For all other settings inUserSettings, the values specified inCreateUserProfiletake precedence over those specified inCreateDomain.Specifies a production variant property type for an Endpoint.
If you are updating an endpoint with the
RetainAllVariantPropertiesoption of UpdateEndpointInput set totrue, theVariantPropertyobjects listed in theExcludeRetainedVariantPropertiesparameter of UpdateEndpointInput override the existing variant properties of the endpoint.Configuration for your vector collection type.
A lineage entity connected to the starting entity(ies).
The list of key-value pairs used to filter your search results. If a search result contains a key from your list, it is included in the final search response if the value associated with the key in the result matches the value you specified. If the value doesn't match, the result is excluded from the search response. Any resources that don't have a key from the list that you've provided will also be included in the search response.
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.
Status and billing information about the warm pool.
Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
The VPC object you use to create or update a workforce.
A VpcConfig object that specifies the VPC that you want your workforce to connect to.
The workspace settings for the SageMaker Canvas application.
Provides details about a labeling work team.
Enums§
- When writing a match expression against
ActionStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AdditionalS3DataSourceDataType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AggregationTransformationValue, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AlgorithmSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AlgorithmStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppImageConfigSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppNetworkAccessType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppSecurityGroupManagement, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AppType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ArtifactSourceIdType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AssemblyType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AssociationEdgeType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AsyncNotificationTopicTypes, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AthenaResultCompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AthenaResultFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AuthMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlAlgorithm, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlChannelType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlJobObjectiveType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlJobSecondaryStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlMetricEnum, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlMetricExtendedEnum, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. A collection of settings specific to the problem type used to configure an AutoML job V2. There must be one and only one config of the following type.
- When writing a match expression against
AutoMlProblemTypeConfigName, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. Stores resolved attributes specific to the problem type of an AutoML job V2.
- When writing a match expression against
AutoMlProcessingUnit, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMlSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutoMls3DataType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AutotuneMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
AwsManagedHumanLoopRequestSource, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
BatchStrategy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
BooleanOperator, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CandidateSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CandidateStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CandidateStepType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CapacitySizeType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CaptureMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CaptureStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClarifyFeatureType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClarifyTextGranularity, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClarifyTextLanguage, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClusterInstanceStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.
- When writing a match expression against
ClusterInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClusterSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ClusterStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CodeRepositorySortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CodeRepositorySortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. Configuration for your collection.
- When writing a match expression against
CollectionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CompilationJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CompleteOnConvergence, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ConditionOutcome, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ContainerMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ContentClassifier, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
CrossAccountFilterOption, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
- When writing a match expression against
DataDistributionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DataSourceName, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DetailedAlgorithmStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DetailedModelPackageStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DeviceDeploymentStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DeviceSubsetType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DirectInternetAccess, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
Direction, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
DomainStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EdgePackagingJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EdgePresetDeploymentStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EdgePresetDeploymentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EnabledOrDisabled, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EndpointConfigSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EndpointSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
EndpointStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ExecutionRoleIdentityConfig, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ExecutionStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FailureHandlingPolicy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FeatureGroupSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FeatureGroupSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FeatureGroupStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FeatureStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FeatureType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FileSystemAccessMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FileSystemType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FillingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FlatInvocations, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
FlowDefinitionStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
Framework, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubContentSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubContentStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubContentSupportStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubContentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HubStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HumanTaskUiStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterScalingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningAllocationStrategy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningJobObjectiveType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningJobSortByOptions, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningJobStrategyType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
HyperParameterTuningJobWarmStartType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageVersionSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageVersionSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ImageVersionStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceComponentSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceComponentStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceExecutionMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceExperimentStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceExperimentStopDesiredState, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InferenceExperimentType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InputMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
InstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
IsTrackingServerActive, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
JobType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
JoinSource, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
LabelingJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
LastUpdateStatusValue, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
LineageType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListCompilationJobsSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListDeviceFleetsSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListEdgeDeploymentPlansSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListEdgePackagingJobsSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListInferenceRecommendationsJobsSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListLabelingJobsForWorkteamSortByOptions, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListOptimizationJobsSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListWorkforcesSortByOptions, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ListWorkteamsSortByOptions, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ManagedInstanceScalingStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MetricSetSource, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. An object containing information about a metric.
- When writing a match expression against
MlTools, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelApprovalStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCacheSetting, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardExportJobSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardExportJobSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardExportJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardProcessingStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCardVersionSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelCompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelInfrastructureType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelMetadataFilterType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelPackageGroupSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelPackageGroupStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelPackageSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelPackageStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelPackageType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelVariantAction, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ModelVariantStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringAlertHistorySortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringAlertStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringExecutionSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringJobDefinitionSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringProblemType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringScheduleSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
MonitoringType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceAcceleratorType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceLifecycleConfigSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceLifecycleConfigSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookInstanceStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
NotebookOutputOption, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ObjectiveStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
OfflineStoreStatusValue, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
Operator, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. Settings for an optimization technique that you apply with a model optimization job.
- When writing a match expression against
OptimizationJobDeploymentInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
OptimizationJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
OrderKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
OutputCompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ParameterType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
PipelineExecutionStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
PipelineStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProblemType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingS3CompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingS3DataDistributionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingS3DataType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingS3InputMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProcessingS3UploadMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
Processor, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProductionVariantAcceleratorType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProductionVariantInferenceAmiVersion, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProductionVariantInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProfilingStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProjectSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProjectSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ProjectStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RStudioServerProAccessStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RStudioServerProUserGroup, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecommendationJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecommendationJobSupportedEndpointType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecommendationJobType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecommendationStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecommendationStepType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RecordWrapper, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RedshiftResultCompressionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RedshiftResultFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RepositoryAccessMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ResourceCatalogSortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ResourceCatalogSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ResourceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RetentionType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RootAccess, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RoutingStrategy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
RuleEvaluationStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
S3DataDistribution, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
S3DataType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
S3ModelDataType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SagemakerServicecatalogStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. An object containing a recommended scaling policy.
- When writing a match expression against
ScheduleStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SearchSortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SecondaryStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SharingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SkipModelValidation, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortActionsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortArtifactsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortAssociationsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortContextsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortExperimentsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortInferenceExperimentsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortLineageGroupsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortPipelineExecutionsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortPipelinesBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortTrackingServerBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortTrialComponentsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SortTrialsBy, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SpaceSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SpaceStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
SplitType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StageStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
Statistic, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StepStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StorageType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StudioLifecycleConfigAppType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StudioLifecycleConfigSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
StudioWebPortal, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TableFormat, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TargetDevice, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TargetPlatformAccelerator, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TargetPlatformArch, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TargetPlatformOs, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
ThroughputMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrackingServerSize, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrackingServerStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrafficRoutingConfigType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrafficType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingInputMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingJobEarlyStoppingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingJobSortByOptions, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TrainingRepositoryAccessMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TransformInstanceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TransformJobStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. The value of a hyperparameter. Only one of
NumberValueorStringValuecan be specified.This object is specified in the CreateTrialComponent request.
- When writing a match expression against
TrialComponentPrimaryStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
TtlDurationUnit, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
UserProfileSortKey, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
UserProfileStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
VariantPropertyType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
VariantStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
VendorGuidance, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
WarmPoolResourceStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature. - When writing a match expression against
WorkforceStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.