[−][src]Crate rusoto_sagemaker

Provides APIs for creating and managing Amazon SageMaker resources.

If you're using the service, you're probably looking for SageMakerClient and SageMaker.

Structs

AddTagsInput
AddTagsOutput
AlgorithmSpecification	Specifies the training algorithm to use in a CreateTrainingJob request. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
AlgorithmStatusDetails	Specifies the validation and image scan statuses of the algorithm.
AlgorithmStatusItem	Represents the overall status of an algorithm.
AlgorithmSummary	Provides summary information about an algorithm.
AlgorithmValidationProfile	Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm. The data provided in the validation profile is made available to your buyers on AWS Marketplace.
AlgorithmValidationSpecification	Specifies configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.
AnnotationConsolidationConfig	Configures how labels are consolidated across human workers.
CategoricalParameterRange	A list of categorical hyperparameters to tune.
CategoricalParameterRangeSpecification	Defines the possible values for a categorical hyperparameter.
Channel	A channel is a named input source that training algorithms can consume.
ChannelSpecification	Defines a named input source, called a channel, to be used by an algorithm.
CodeRepositorySummary	Specifies summary information about a Git repository.
CognitoMemberDefinition	Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.
CompilationJobSummary	A summary of a model compilation job.
ContainerDefinition	Describes the container, as part of model definition.
ContinuousParameterRange	A list of continuous hyperparameters to tune.
ContinuousParameterRangeSpecification	Defines the possible values for a continuous hyperparameter.
CreateAlgorithmInput
CreateAlgorithmOutput
CreateCodeRepositoryInput
CreateCodeRepositoryOutput
CreateCompilationJobRequest
CreateCompilationJobResponse
CreateEndpointConfigInput
CreateEndpointConfigOutput
CreateEndpointInput
CreateEndpointOutput
CreateHyperParameterTuningJobRequest
CreateHyperParameterTuningJobResponse
CreateLabelingJobRequest
CreateLabelingJobResponse
CreateModelInput
CreateModelOutput
CreateModelPackageInput
CreateModelPackageOutput
CreateNotebookInstanceInput
CreateNotebookInstanceLifecycleConfigInput
CreateNotebookInstanceLifecycleConfigOutput
CreateNotebookInstanceOutput
CreatePresignedNotebookInstanceUrlInput
CreatePresignedNotebookInstanceUrlOutput
CreateTrainingJobRequest
CreateTrainingJobResponse
CreateTransformJobRequest
CreateTransformJobResponse
CreateWorkteamRequest
CreateWorkteamResponse
DataSource	Describes the location of the channel data.
DeleteAlgorithmInput
DeleteCodeRepositoryInput
DeleteEndpointConfigInput
DeleteEndpointInput
DeleteModelInput
DeleteModelPackageInput
DeleteNotebookInstanceInput
DeleteNotebookInstanceLifecycleConfigInput
DeleteTagsInput
DeleteTagsOutput
DeleteWorkteamRequest
DeleteWorkteamResponse
DeployedImage	Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant. If you used the `registry/repository[:tag]` form to specify the image path of the primary container when you created the model hosted in this `ProductionVariant`, the path resolves to a path of the form `registry/repository[@digest]`. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.
DescribeAlgorithmInput
DescribeAlgorithmOutput
DescribeCodeRepositoryInput
DescribeCodeRepositoryOutput
DescribeCompilationJobRequest
DescribeCompilationJobResponse
DescribeEndpointConfigInput
DescribeEndpointConfigOutput
DescribeEndpointInput
DescribeEndpointOutput
DescribeHyperParameterTuningJobRequest
DescribeHyperParameterTuningJobResponse
DescribeLabelingJobRequest
DescribeLabelingJobResponse
DescribeModelInput
DescribeModelOutput
DescribeModelPackageInput
DescribeModelPackageOutput
DescribeNotebookInstanceInput
DescribeNotebookInstanceLifecycleConfigInput
DescribeNotebookInstanceLifecycleConfigOutput
DescribeNotebookInstanceOutput
DescribeSubscribedWorkteamRequest
DescribeSubscribedWorkteamResponse
DescribeTrainingJobRequest
DescribeTrainingJobResponse
DescribeTransformJobRequest
DescribeTransformJobResponse
DescribeWorkteamRequest
DescribeWorkteamResponse
DesiredWeightAndCapacity	Specifies weight and capacity values for a production variant.
EndpointConfigSummary	Provides summary information for an endpoint configuration.
EndpointSummary	Provides summary information for an endpoint.
Filter	A conditional statement for a search expression that includes a Boolean operator, a resource property, and a value. If you don't specify an `Operator` and a `Value`, the filter searches for only the specified property. For example, defining a `Filter` for the `FailureReason` for the `TrainingJob` `Resource` searches for training job objects that have a value in the `FailureReason` field. If you specify a `Value`, but not an `Operator`, Amazon SageMaker uses the equals operator as the default. In search, there are several property types: Metrics To define a metric filter, enter a value using the form `"Metrics.<name>"`, where `<name>` is a metric name. For example, the following filter searches for training jobs with an `"accuracy"` metric greater than `"0.9"`: `{` `"Name": "Metrics.accuracy",` `"Operator": "GREATERTHAN",` `"Value": "0.9"` `}` HyperParameters To define a hyperparameter filter, enter a value with the form `"HyperParameters.<name>"`. Decimal hyperparameter values are treated as a decimal in a comparison if the specified `Value` is also a decimal value. If the specified `Value` is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a `"learningrate"` hyperparameter that is less than `"0.5"`: `{` `"Name": "HyperParameters.learningrate",` `"Operator": "LESS``THAN",` `"Value": "0.5"` `}` Tags To define a tag filter, enter a value with the form `"Tags.<key>"`.
FinalHyperParameterTuningJobObjectiveMetric	Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the `HyperParameterTuningJobObjective` parameter of HyperParameterTuningJobConfig.
GetSearchSuggestionsRequest
GetSearchSuggestionsResponse
GitConfig	Specifies configuration details for a Git repository in your AWS account.
GitConfigForUpdate	Specifies configuration details for a Git repository when the repository is updated.
HumanTaskConfig	Information required for human workers to complete a labeling task.
HyperParameterAlgorithmSpecification	Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.
HyperParameterSpecification	Defines a hyperparameter to be used by an algorithm.
HyperParameterTrainingJobDefinition	Defines the training jobs launched by a hyperparameter tuning job.
HyperParameterTrainingJobSummary	Specifies summary information about a training job.
HyperParameterTuningJobConfig	Configures a hyperparameter tuning job.
HyperParameterTuningJobObjective	Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the `Type` parameter.
HyperParameterTuningJobSummary	Provides summary information about a hyperparameter tuning job.
HyperParameterTuningJobWarmStartConfig	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.
InferenceSpecification	Defines how to perform inference generation after a training job is run.
InputConfig	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.
IntegerParameterRange	For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
IntegerParameterRangeSpecification	Defines the possible values for an integer hyperparameter.
LabelCounters	Provides a breakdown of the number of objects labeled.
LabelCountersForWorkteam	Provides counts for human-labeled tasks in the labeling job.
LabelingJobAlgorithmsConfig	Provides configuration information for auto-labeling of your data objects. A `LabelingJobAlgorithmsConfig` object must be supplied in order to use auto-labeling.
LabelingJobDataAttributes	Attributes of the data specified by the customer. Use these to describe the data to be labeled.
LabelingJobDataSource	Provides information about the location of input data.
LabelingJobForWorkteamSummary	Provides summary information for a work team.
LabelingJobInputConfig	Input configuration information for a labeling job.
LabelingJobOutput	Specifies the location of the output produced by the labeling job.
LabelingJobOutputConfig	Output configuration information for a labeling job.
LabelingJobResourceConfig	Provides configuration information for labeling jobs.
LabelingJobS3DataSource	The Amazon S3 location of the input data objects.
LabelingJobStoppingConditions	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.
LabelingJobSummary	Provides summary information about a labeling job.
ListAlgorithmsInput
ListAlgorithmsOutput
ListCodeRepositoriesInput
ListCodeRepositoriesOutput
ListCompilationJobsRequest
ListCompilationJobsResponse
ListEndpointConfigsInput
ListEndpointConfigsOutput
ListEndpointsInput
ListEndpointsOutput
ListHyperParameterTuningJobsRequest
ListHyperParameterTuningJobsResponse
ListLabelingJobsForWorkteamRequest
ListLabelingJobsForWorkteamResponse
ListLabelingJobsRequest
ListLabelingJobsResponse
ListModelPackagesInput
ListModelPackagesOutput
ListModelsInput
ListModelsOutput
ListNotebookInstanceLifecycleConfigsInput
ListNotebookInstanceLifecycleConfigsOutput
ListNotebookInstancesInput
ListNotebookInstancesOutput
ListSubscribedWorkteamsRequest
ListSubscribedWorkteamsResponse
ListTagsInput
ListTagsOutput
ListTrainingJobsForHyperParameterTuningJobRequest
ListTrainingJobsForHyperParameterTuningJobResponse
ListTrainingJobsRequest
ListTrainingJobsResponse
ListTransformJobsRequest
ListTransformJobsResponse
ListWorkteamsRequest
ListWorkteamsResponse
MemberDefinition	Defines the Amazon Cognito user group that is part of a work team.
MetricData	The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricDefinition	Specifies a metric that the training algorithm writes to `stderr` or `stdout`. Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
ModelArtifacts	Provides information about the location that is configured for storing model artifacts.
ModelPackageContainerDefinition	Describes the Docker container for the model package.
ModelPackageStatusDetails	Specifies the validation and image scan statuses of the model package.
ModelPackageStatusItem	Represents the overall status of a model package.
ModelPackageSummary	Provides summary information about a model package.
ModelPackageValidationProfile	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 AWS Marketplace.
ModelPackageValidationSpecification	Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
ModelSummary	Provides summary information about a model.
NestedFilters	Defines a list of `NestedFilters` objects. To satisfy the conditions specified in the `NestedFilters` call, a resource must satisfy the conditions of all of the filters. For example, you could define a `NestedFilters` using the training job's `InputDataConfig` property to filter on `Channel` objects. A `NestedFilters` object contains multiple filters. For example, to find all training jobs whose name contains `train` and that have `cat/data` in their `S3Uri` (specified in `InputDataConfig`), you need to create a `NestedFilters` object that specifies the `InputDataConfig` property with the following `Filter` objects: `'{Name:"InputDataConfig.ChannelName", "Operator":"EQUALS", "Value":"train"}',` `'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"CONTAINS", "Value":"cat/data"}'`
NotebookInstanceLifecycleConfigSummary	Provides a summary of a notebook instance lifecycle configuration.
NotebookInstanceLifecycleHook	Contains the notebook instance lifecycle configuration script. Each lifecycle configuration script has a limit of 16384 characters. The value of the `$PATH` environment variable that is available to both scripts is `/sbin:bin:/usr/sbin:/usr/bin`. View CloudWatch Logs for notebook instance lifecycle configurations in log group `/aws/sagemaker/NotebookInstances` in log stream `[notebook-instance-name]/[LifecycleConfigHook]`. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
NotebookInstanceSummary	Provides summary information for an Amazon SageMaker notebook instance.
NotificationConfiguration	Configures SNS notifications of available or expiring work items for work teams.
ObjectiveStatusCounters	Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
OutputConfig	Contains information about the output location for the compiled model and the device (target) that the model runs on.
OutputDataConfig	Provides information about how to store model training results (model artifacts).
ParameterRange	Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.
ParameterRanges	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. You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.
ParentHyperParameterTuningJob	A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
ProductionVariant	Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.
ProductionVariantSummary	Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the `UpdateEndpointWeightsAndCapacities` API and the endpoint status is `Updating`, you get different desired and current values.
PropertyNameQuery	A type of `SuggestionQuery`. A suggestion query for retrieving property names that match the specified hint.
PropertyNameSuggestion	A property name returned from a `GetSearchSuggestions` call that specifies a value in the `PropertyNameQuery` field.
PublicWorkforceTaskPrice	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. 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
RenderUiTemplateRequest
RenderUiTemplateResponse
RenderableTask	Contains input values for a task.
RenderingError	A description of an error that occurred while rendering the template.
ResourceConfig	Describes the resources, including ML compute instances and ML storage volumes, to use for model training.
ResourceLimits	Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.
S3DataSource	Describes the S3 data source.
SageMakerClient	A client for the SageMaker API.
SearchExpression	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 `SearchExpression` can contain up to twenty elements. A `SearchExpression` contains the following components: A list of `Filter` objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. A list of `NestedFilter` objects. 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 `SearchExpression` objects. A search expression object can be nested in a list of search expression objects. A Boolean operator: `And` or `Or`.
SearchRecord	An individual search result record that contains a single resource object.
SearchRequest
SearchResponse
SecondaryStatusTransition	An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. 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, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
ShuffleConfig	A configuration for a shuffle option for input data in a channel. If you use `S3Prefix` for `S3DataType`, the results of the S3 key prefix matches are shuffled. If you use `ManifestFile`, the order of the S3 object references in the `ManifestFile` is shuffled. If you use `AugmentedManifestFile`, the order of the JSON lines in the `AugmentedManifestFile` is shuffled. The shuffling order is determined using the `Seed` value. For Pipe input mode, 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 when `ShuffleConfig` is combined with `S3DataDistributionType` of `ShardedByS3Key`, 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.
SourceAlgorithm	Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
SourceAlgorithmSpecification	A list of algorithms that were used to create a model package.
StartNotebookInstanceInput
StopCompilationJobRequest
StopHyperParameterTuningJobRequest
StopLabelingJobRequest
StopNotebookInstanceInput
StopTrainingJobRequest
StopTransformJobRequest
StoppingCondition	Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost. To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM` signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost. Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (`CreateModel`).
SubscribedWorkteam	Describes a work team of a vendor that does the a labelling job.
SuggestionQuery	Limits the property names that are included in the response.
Tag	Describes a tag.
TrainingJob	Contains information about a training job.
TrainingJobDefinition	Defines the input needed to run a training job using the algorithm.
TrainingJobStatusCounters	The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
TrainingJobSummary	Provides summary information about a training job.
TrainingSpecification	Defines how the algorithm is used for a training job.
TransformDataSource	Describes the location of the channel data.
TransformInput	Describes the input source of a transform job and the way the transform job consumes it.
TransformJobDefinition	Defines the input needed to run a transform job using the inference specification specified in the algorithm.
TransformJobSummary	Provides a summary of a transform job. Multiple `TransformJobSummary` objects are returned as a list after in response to a ListTransformJobs call.
TransformOutput	Describes the results of a transform job.
TransformResources	Describes the resources, including ML instance types and ML instance count, to use for transform job.
TransformS3DataSource	Describes the S3 data source.
USD	Represents an amount of money in United States dollars/
UiConfig	Provided configuration information for the worker UI for a labeling job.
UiTemplate	The Liquid template for the worker user interface.
UpdateCodeRepositoryInput
UpdateCodeRepositoryOutput
UpdateEndpointInput
UpdateEndpointOutput
UpdateEndpointWeightsAndCapacitiesInput
UpdateEndpointWeightsAndCapacitiesOutput
UpdateNotebookInstanceInput
UpdateNotebookInstanceLifecycleConfigInput
UpdateNotebookInstanceLifecycleConfigOutput
UpdateNotebookInstanceOutput
UpdateWorkteamRequest
UpdateWorkteamResponse
VpcConfig	Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.
Workteam	Provides details about a labeling work team.

Enums

AddTagsError	Errors returned by AddTags
CreateAlgorithmError	Errors returned by CreateAlgorithm
CreateCodeRepositoryError	Errors returned by CreateCodeRepository
CreateCompilationJobError	Errors returned by CreateCompilationJob
CreateEndpointConfigError	Errors returned by CreateEndpointConfig
CreateEndpointError	Errors returned by CreateEndpoint
CreateHyperParameterTuningJobError	Errors returned by CreateHyperParameterTuningJob
CreateLabelingJobError	Errors returned by CreateLabelingJob
CreateModelError	Errors returned by CreateModel
CreateModelPackageError	Errors returned by CreateModelPackage
CreateNotebookInstanceError	Errors returned by CreateNotebookInstance
CreateNotebookInstanceLifecycleConfigError	Errors returned by CreateNotebookInstanceLifecycleConfig
CreatePresignedNotebookInstanceUrlError	Errors returned by CreatePresignedNotebookInstanceUrl
CreateTrainingJobError	Errors returned by CreateTrainingJob
CreateTransformJobError	Errors returned by CreateTransformJob
CreateWorkteamError	Errors returned by CreateWorkteam
DeleteAlgorithmError	Errors returned by DeleteAlgorithm
DeleteCodeRepositoryError	Errors returned by DeleteCodeRepository
DeleteEndpointConfigError	Errors returned by DeleteEndpointConfig
DeleteEndpointError	Errors returned by DeleteEndpoint
DeleteModelError	Errors returned by DeleteModel
DeleteModelPackageError	Errors returned by DeleteModelPackage
DeleteNotebookInstanceError	Errors returned by DeleteNotebookInstance
DeleteNotebookInstanceLifecycleConfigError	Errors returned by DeleteNotebookInstanceLifecycleConfig
DeleteTagsError	Errors returned by DeleteTags
DeleteWorkteamError	Errors returned by DeleteWorkteam
DescribeAlgorithmError	Errors returned by DescribeAlgorithm
DescribeCodeRepositoryError	Errors returned by DescribeCodeRepository
DescribeCompilationJobError	Errors returned by DescribeCompilationJob
DescribeEndpointConfigError	Errors returned by DescribeEndpointConfig
DescribeEndpointError	Errors returned by DescribeEndpoint
DescribeHyperParameterTuningJobError	Errors returned by DescribeHyperParameterTuningJob
DescribeLabelingJobError	Errors returned by DescribeLabelingJob
DescribeModelError	Errors returned by DescribeModel
DescribeModelPackageError	Errors returned by DescribeModelPackage
DescribeNotebookInstanceError	Errors returned by DescribeNotebookInstance
DescribeNotebookInstanceLifecycleConfigError	Errors returned by DescribeNotebookInstanceLifecycleConfig
DescribeSubscribedWorkteamError	Errors returned by DescribeSubscribedWorkteam
DescribeTrainingJobError	Errors returned by DescribeTrainingJob
DescribeTransformJobError	Errors returned by DescribeTransformJob
DescribeWorkteamError	Errors returned by DescribeWorkteam
GetSearchSuggestionsError	Errors returned by GetSearchSuggestions
ListAlgorithmsError	Errors returned by ListAlgorithms
ListCodeRepositoriesError	Errors returned by ListCodeRepositories
ListCompilationJobsError	Errors returned by ListCompilationJobs
ListEndpointConfigsError	Errors returned by ListEndpointConfigs
ListEndpointsError	Errors returned by ListEndpoints
ListHyperParameterTuningJobsError	Errors returned by ListHyperParameterTuningJobs
ListLabelingJobsError	Errors returned by ListLabelingJobs
ListLabelingJobsForWorkteamError	Errors returned by ListLabelingJobsForWorkteam
ListModelPackagesError	Errors returned by ListModelPackages
ListModelsError	Errors returned by ListModels
ListNotebookInstanceLifecycleConfigsError	Errors returned by ListNotebookInstanceLifecycleConfigs
ListNotebookInstancesError	Errors returned by ListNotebookInstances
ListSubscribedWorkteamsError	Errors returned by ListSubscribedWorkteams
ListTagsError	Errors returned by ListTags
ListTrainingJobsError	Errors returned by ListTrainingJobs
ListTrainingJobsForHyperParameterTuningJobError	Errors returned by ListTrainingJobsForHyperParameterTuningJob
ListTransformJobsError	Errors returned by ListTransformJobs
ListWorkteamsError	Errors returned by ListWorkteams
RenderUiTemplateError	Errors returned by RenderUiTemplate
SearchError	Errors returned by Search
StartNotebookInstanceError	Errors returned by StartNotebookInstance
StopCompilationJobError	Errors returned by StopCompilationJob
StopHyperParameterTuningJobError	Errors returned by StopHyperParameterTuningJob
StopLabelingJobError	Errors returned by StopLabelingJob
StopNotebookInstanceError	Errors returned by StopNotebookInstance
StopTrainingJobError	Errors returned by StopTrainingJob
StopTransformJobError	Errors returned by StopTransformJob
UpdateCodeRepositoryError	Errors returned by UpdateCodeRepository
UpdateEndpointError	Errors returned by UpdateEndpoint
UpdateEndpointWeightsAndCapacitiesError	Errors returned by UpdateEndpointWeightsAndCapacities
UpdateNotebookInstanceError	Errors returned by UpdateNotebookInstance
UpdateNotebookInstanceLifecycleConfigError	Errors returned by UpdateNotebookInstanceLifecycleConfig
UpdateWorkteamError	Errors returned by UpdateWorkteam

Traits

SageMaker

Trait representing the capabilities of the SageMaker API. SageMaker clients implement this trait.