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{:ListImageVersions {:documentation "
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
", :request {:CreationTimeBefore timestamp, :NextToken string, :SortBy [:one-of ["CREATION_TIME" "LAST_MODIFIED_TIME" "VERSION"]], :SortOrder [:one-of ["ASCENDING" "DESCENDING"]], :ImageName string, :CreationTimeAfter timestamp, :MaxResults integer, :LastModifiedTimeBefore timestamp, :LastModifiedTimeAfter timestamp}, :required [:ImageName], :response {:ImageVersions [:seq-of {:CreationTime timestamp, :FailureReason string, :ImageArn string, :ImageVersionArn string, :ImageVersionStatus [:one-of ["CREATING" "CREATED" "CREATE_FAILED" "DELETING" "DELETE_FAILED"]], :LastModifiedTime timestamp, :Version integer}], :NextToken string}}, :CreateAutoMLJob {:documentation "
Creates an Autopilot job.
 
Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
 
For information about how to use Autopilot, see  Automate Model Development with Amazon SageMaker Autopilot.
", :request {:AutoMLJobName string, :ProblemType [:one-of ["BinaryClassification" "MulticlassClassification" "Regression"]], :AutoMLJobObjective {:MetricName [:one-of ["Accuracy" "MSE" "F1" "F1macro" "AUC"]]}, :Tags [:seq-of {:Key string, :Value string}], :OutputDataConfig {:KmsKeyId string, :S3OutputPath string}, :RoleArn string, :GenerateCandidateDefinitionsOnly boolean, :AutoMLJobConfig {:CompletionCriteria {:MaxCandidates integer, :MaxRuntimePerTrainingJobInSeconds integer, :MaxAutoMLJobRuntimeInSeconds integer}, :SecurityConfig {:VolumeKmsKeyId string, :EnableInterContainerTrafficEncryption boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}}}, :InputDataConfig [:seq-of {:DataSource {:S3DataSource {:S3DataType [:one-of ["ManifestFile" "S3Prefix"]], :S3Uri string}}, :CompressionType [:one-of ["None" "Gzip"]], :TargetAttributeName string}]}, :required [:AutoMLJobName :InputDataConfig :OutputDataConfig :RoleArn], :response {:AutoMLJobArn string}}, :CreateNotebookInstanceLifecycleConfig {:documentation "
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
 
Each lifecycle configuration script has a limit of 16384 characters.
 
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
 
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
 
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
 
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
", :request {:NotebookInstanceLifecycleConfigName string, :OnCreate [:seq-of {:Content string}], :OnStart [:seq-of {:Content string}]}, :required [:NotebookInstanceLifecycleConfigName], :response {:NotebookInstanceLifecycleConfigArn string}}, :DescribeCodeRepository {:documentation "
Gets details about the specified Git repository.
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Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
 
 Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal.
 
Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP. 
  
You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.
  
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.
  
This operation only applies to private workforces.
 ", :request {:WorkforceName string, :SourceIpConfig {:Cidrs [:seq-of string]}, :OidcConfig {:ClientId string, :ClientSecret string, :Issuer string, :AuthorizationEndpoint string, :TokenEndpoint string, :UserInfoEndpoint string, :LogoutEndpoint string, :JwksUri string}}, :required [:WorkforceName], :response {:Workforce {:WorkforceName string, :WorkforceArn string, :LastUpdatedDate timestamp, :SourceIpConfig {:Cidrs [:seq-of string]}, :SubDomain string, :CognitoConfig {:UserPool string, :ClientId string}, :OidcConfig {:ClientId string, :Issuer string, :AuthorizationEndpoint string, :TokenEndpoint string, :UserInfoEndpoint string, :LogoutEndpoint string, :JwksUri string}, :CreateDate timestamp}}}, :CreateTrainingJob {:documentation "
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. 
 
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inference. 
 
In the request body, you provide the following: 
 
 
 
 AlgorithmSpecification - Identifies the training algorithm to use. 
 
 
 
 HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms. 
 
 
 
 InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
 
 
 
 OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of model training. 
 
 
 
 
 ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. 
 
 
 
 EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. 
 
 
 
 RoleArn - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training. 
 
 
 
 StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a managed spot training job to complete. 
 
 
 
 For more information about Amazon SageMaker, see How It Works. 
", :request {:EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}, :TrainingJobName string, :ResourceConfig {:InstanceType [:one-of ["ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.g4dn.xlarge" "ml.g4dn.2xlarge" "ml.g4dn.4xlarge" "ml.g4dn.8xlarge" "ml.g4dn.12xlarge" "ml.g4dn.16xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.p3dn.24xlarge" "ml.p4d.24xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.c5n.xlarge" "ml.c5n.2xlarge" "ml.c5n.4xlarge" "ml.c5n.9xlarge" "ml.c5n.18xlarge"]], :InstanceCount integer, :VolumeSizeInGB integer, :VolumeKmsKeyId string}, :DebugRuleConfigurations [:seq-of {:RuleConfigurationName string, :LocalPath string, :S3OutputPath string, :RuleEvaluatorImage string, :InstanceType [:one-of ["ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.8xlarge" "ml.r5.12xlarge" "ml.r5.16xlarge" "ml.r5.24xlarge"]], :VolumeSizeInGB integer, :RuleParameters [:map-of string string]}], :Tags [:seq-of {:Key string, :Value string}], :EnableInterContainerTrafficEncryption boolean, :AlgorithmSpecification {:TrainingImage string, :AlgorithmName string, :TrainingInputMode [:one-of ["Pipe" "File"]], :MetricDefinitions [:seq-of {:Name string, :Regex string}], :EnableSageMakerMetricsTimeSeries boolean}, :OutputDataConfig {:KmsKeyId string, :S3OutputPath string}, :CheckpointConfig {:S3Uri string, :LocalPath string}, :StoppingCondition {:MaxRuntimeInSeconds integer, :MaxWaitTimeInSeconds integer}, :ExperimentConfig {:ExperimentName string, :TrialName string, :TrialComponentDisplayName string}, :RoleArn string, :EnableManagedSpotTraining boolean, :HyperParameters [:map-of string string], :DebugHookConfig {:LocalPath string, :S3OutputPath string, :HookParameters [:map-of string string], :CollectionConfigurations [:seq-of {:CollectionName string, :CollectionParameters [:map-of string string]}]}, :InputDataConfig [:seq-of {:ChannelName string, :DataSource {:S3DataSource {:S3DataType [:one-of ["ManifestFile" "S3Prefix" "AugmentedManifestFile"]], :S3Uri string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]], :AttributeNames [:seq-of string]}, :FileSystemDataSource {:FileSystemId string, :FileSystemAccessMode [:one-of ["rw" "ro"]], :FileSystemType [:one-of ["EFS" "FSxLustre"]], :DirectoryPath string}}, :ContentType string, :CompressionType [:one-of ["None" "Gzip"]], :RecordWrapperType [:one-of ["None" "RecordIO"]], :InputMode [:one-of ["Pipe" "File"]], :ShuffleConfig {:Seed long}}], :ProfilerConfig {:S3OutputPath string, :ProfilingIntervalInMilliseconds long, :ProfilingParameters [:map-of string string]}, :TensorBoardOutputConfig {:LocalPath string, :S3OutputPath string}, :ProfilerRuleConfigurations [:seq-of {:RuleConfigurationName string, :LocalPath string, :S3OutputPath string, :RuleEvaluatorImage string, :InstanceType [:one-of ["ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.8xlarge" "ml.r5.12xlarge" "ml.r5.16xlarge" "ml.r5.24xlarge"]], :VolumeSizeInGB integer, :RuleParameters [:map-of string string]}]}, :required [:TrainingJobName :AlgorithmSpecification :RoleArn :OutputDataConfig :ResourceConfig :StoppingCondition], :response {:TrainingJobArn string}}, :DescribeTrial {:documentation "
Provides a list of a trial's properties.
", :request {:TrialName string}, :required [:TrialName], :response {:ExperimentName string, :LastModifiedTime timestamp, :TrialName string, :TrialArn string, :DisplayName string, :CreatedBy {:UserProfileArn string, :UserProfileName string, :DomainId string}, :CreationTime timestamp, :LastModifiedBy {:UserProfileArn string, :UserProfileName string, :DomainId string}, :Source {:SourceArn string, :SourceType string}, :MetadataProperties {:CommitId string, :Repository string, :GeneratedBy string, :ProjectId string}}}, :CreatePresignedNotebookInstanceUrl {:documentation "
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
 
 The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
 
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
  
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
 ", :request {:NotebookInstanceName string, :SessionExpirationDurationInSeconds integer}, :required [:NotebookInstanceName], :response {:AuthorizedUrl string}}, :DescribeModelPackageGroup {:documentation "
Gets a description for the specified model group.
", :request {:ModelPackageGroupName string}, :required [:ModelPackageGroupName], :response {:ModelPackageGroupName string, :ModelPackageGroupArn string, :ModelPackageGroupDescription string, :CreationTime timestamp, :CreatedBy {:UserProfileArn string, :UserProfileName string, :DomainId string}, :ModelPackageGroupStatus [:one-of ["Pending" "InProgress" "Completed" "Failed" "Deleting" "DeleteFailed"]]}}, :ListTrials {:documentation "
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
", :request {:ExperimentName string, :TrialComponentName string, :CreatedAfter timestamp, :CreatedBefore timestamp, :SortBy [:one-of ["Name" "CreationTime"]], :SortOrder [:one-of ["Ascending" "Descending"]], :MaxResults integer, :NextToken string}, :required nil, :response {:TrialSummaries [:seq-of {:TrialArn string, :TrialName string, :DisplayName string, :TrialSource {:SourceArn string, :SourceType string}, :CreationTime timestamp, :LastModifiedTime timestamp}], :NextToken string}}, :UpdateArtifact {:documentation "
Updates an artifact.
", :request {:ArtifactArn string, :ArtifactName string, :Properties [:map-of string string], :PropertiesToRemove [:seq-of string]}, :required [:ArtifactArn], :response {:ArtifactArn string}}, :CreateProject {:documentation "
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
", :request {:ProjectName string, :ProjectDescription string, :ServiceCatalogProvisioningDetails {:ProductId string, :ProvisioningArtifactId string, :PathId string, :ProvisioningParameters [:seq-of {:Key string, :Value string}]}, :Tags [:seq-of {:Key string, :Value string}]}, :required [:ProjectName :ServiceCatalogProvisioningDetails], :response {:ProjectArn string, :ProjectId string}}, :StopTransformJob {:documentation "
Stops a transform job.
 
When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
", :request {:TransformJobName string}, :required [:TransformJobName], :response nil}, :DeleteTags {:documentation "
Deletes the specified tags from an Amazon SageMaker resource.
 
To list a resource's tags, use the ListTags API. 
  
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
 ", :request {:ResourceArn string, :TagKeys [:seq-of string]}, :required [:ResourceArn :TagKeys], :response {}}, :DescribePipeline {:documentation "
Describes the details of a pipeline.
", :request {:PipelineName string}, :required [:PipelineName], :response {:PipelineDisplayName string, :LastModifiedTime timestamp, :PipelineName string, :PipelineStatus [:one-of ["Active"]], :PipelineDescription string, :PipelineArn string, :RoleArn string, :CreatedBy {:UserProfileArn string, :UserProfileName string, :DomainId string}, :CreationTime timestamp, :PipelineDefinition string, :LastModifiedBy {:UserProfileArn string, :UserProfileName string, :DomainId string}, :LastRunTime timestamp}}, :CreateTrial {:documentation "
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
 
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
 
You can add tags to a trial and then use the Search API to search for the tags.
 
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
", :request {:TrialName string, :DisplayName string, :ExperimentName string, :MetadataProperties {:CommitId string, :Repository string, :GeneratedBy string, :ProjectId string}, :Tags [:seq-of {:Key string, :Value string}]}, :required [:TrialName :ExperimentName], :response {:TrialArn string}}, :DescribeSubscribedWorkteam {:documentation "
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
", :request {:WorkteamArn string}, :required [:WorkteamArn], :response {:SubscribedWorkteam {:WorkteamArn string, :MarketplaceTitle string, :SellerName string, :MarketplaceDescription string, :ListingId string}}}, :CreateFlowDefinition {:documentation "
Creates a flow definition.
", :request {:FlowDefinitionName string, :HumanLoopRequestSource {:AwsManagedHumanLoopRequestSource [:one-of ["AWS/Rekognition/DetectModerationLabels/Image/V3" "AWS/Textract/AnalyzeDocument/Forms/V1"]]}, :HumanLoopActivationConfig {:HumanLoopActivationConditionsConfig {:HumanLoopActivationConditions string}}, :HumanLoopConfig {:TaskTitle string, :TaskTimeLimitInSeconds integer, :WorkteamArn string, :TaskKeywords [:seq-of string], :HumanTaskUiArn string, :PublicWorkforceTaskPrice {:AmountInUsd {:Dollars integer, :Cents integer, :TenthFractionsOfACent integer}}, :TaskCount integer, :TaskDescription string, :TaskAvailabilityLifetimeInSeconds integer}, :OutputConfig {:S3OutputPath string, :KmsKeyId string}, :RoleArn string, :Tags [:seq-of {:Key string, :Value string}]}, :required [:FlowDefinitionName :HumanLoopConfig :OutputConfig :RoleArn], :response {:FlowDefinitionArn string}}, :DescribeImage {:documentation "
Describes a SageMaker image.
", :request {:ImageName string}, :required [:ImageName], :response {:LastModifiedTime timestamp, :ImageArn string, :ImageStatus [:one-of ["CREATING" "CREATED" "CREATE_FAILED" "UPDATING" "UPDATE_FAILED" "DELETING" "DELETE_FAILED"]], :DisplayName string, :RoleArn string, :CreationTime timestamp, :ImageName string, :Description string, :FailureReason string}}, :UpdateModelPackage {:documentation "
Updates a versioned model.
", :request {:ModelPackageArn string, :ModelApprovalStatus [:one-of ["Approved" "Rejected" "PendingManualApproval"]], :ApprovalDescription string}, :required [:ModelPackageArn :ModelApprovalStatus], :response {:ModelPackageArn string}}, :ListCandidatesForAutoMLJob {:documentation "
List the Candidates created for the job.
", :request {:AutoMLJobName string, :StatusEquals [:one-of ["Completed" "InProgress" "Failed" "Stopped" "Stopping"]], :CandidateNameEquals string, :SortOrder [:one-of ["Ascending" "Descending"]], :SortBy [:one-of ["CreationTime" "Status" "FinalObjectiveMetricValue"]], :MaxResults integer, :NextToken string}, :required [:AutoMLJobName], :response {:Candidates [:seq-of {:EndTime timestamp, :LastModifiedTime timestamp, :FinalAutoMLJobObjectiveMetric {:Type [:one-of ["Maximize" "Minimize"]], :MetricName [:one-of ["Accuracy" "MSE" "F1" "F1macro" "AUC"]], :Value "float"}, :InferenceContainers [:seq-of {:Image string, :ModelDataUrl string, :Environment [:map-of string string]}], :CandidateName string, :CandidateSteps [:seq-of {:CandidateStepType [:one-of ["AWS::SageMaker::TrainingJob" "AWS::SageMaker::TransformJob" "AWS::SageMaker::ProcessingJob"]], :CandidateStepArn string, :CandidateStepName string}], :CreationTime timestamp, :ObjectiveStatus [:one-of ["Succeeded" "Pending" "Failed"]], :FailureReason string, :CandidateStatus [:one-of ["Completed" "InProgress" "Failed" "Stopped" "Stopping"]]}], :NextToken string}}, :UpdateContext {:documentation "
Updates a context.
", :request {:ContextName string, :Description string, :Properties [:map-of string string], :PropertiesToRemove [:seq-of string]}, :required [:ContextName], :response {:ContextArn string}}, :DescribeApp {:documentation "
Describes the app.
", :request {:DomainId string, :UserProfileName string, :AppType [:one-of ["JupyterServer" "KernelGateway" "TensorBoard"]], :AppName string}, :required [:DomainId :UserProfileName :AppType :AppName], :response {:UserProfileName string, :LastHealthCheckTimestamp timestamp, :DomainId string, :LastUserActivityTimestamp timestamp, :AppType [:one-of ["JupyterServer" "KernelGateway" "TensorBoard"]], :ResourceSpec {:SageMakerImageArn string, :SageMakerImageVersionArn string, :InstanceType [:one-of ["system" "ml.t3.micro" "ml.t3.small" "ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.8xlarge" "ml.m5.12xlarge" "ml.m5.16xlarge" "ml.m5.24xlarge" "ml.c5.large" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.12xlarge" "ml.c5.18xlarge" "ml.c5.24xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.g4dn.xlarge" "ml.g4dn.2xlarge" "ml.g4dn.4xlarge" "ml.g4dn.8xlarge" "ml.g4dn.12xlarge" "ml.g4dn.16xlarge"]]}, :AppArn string, :CreationTime timestamp, :AppName string, :FailureReason string, :Status [:one-of ["Deleted" "Deleting" "Failed" "InService" "Pending"]]}}, :DescribeNotebookInstance {:documentation "
Returns information about a notebook instance.
", :request {:NotebookInstanceName string}, :required [:NotebookInstanceName], :response {:Url string, :NotebookInstanceArn string, :LastModifiedTime timestamp, :AdditionalCodeRepositories [:seq-of string], :NotebookInstanceLifecycleConfigName string, :InstanceType [:one-of ["ml.t2.medium" "ml.t2.large" "ml.t2.xlarge" "ml.t2.2xlarge" "ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.c5d.xlarge" "ml.c5d.2xlarge" "ml.c5d.4xlarge" "ml.c5d.9xlarge" "ml.c5d.18xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge"]], :AcceleratorTypes [:seq-of [:one-of ["ml.eia1.medium" "ml.eia1.large" "ml.eia1.xlarge" "ml.eia2.medium" "ml.eia2.large" "ml.eia2.xlarge"]]], :DirectInternetAccess [:one-of ["Enabled" "Disabled"]], :RoleArn string, :NotebookInstanceName string, :SubnetId string, :KmsKeyId string, :RootAccess [:one-of ["Enabled" "Disabled"]], :SecurityGroups [:seq-of string], :CreationTime timestamp, :DefaultCodeRepository string, :FailureReason string, :VolumeSizeInGB integer, :NotebookInstanceStatus [:one-of ["Pending" "InService" "Stopping" "Stopped" "Failed" "Deleting" "Updating"]], :NetworkInterfaceId string}}, :DescribeLabelingJob {:documentation "
Gets information about a labeling job.
", :request {:LabelingJobName string}, :required [:LabelingJobName], :response {:LastModifiedTime timestamp, :LabelingJobOutput {:OutputDatasetS3Uri string, :FinalActiveLearningModelArn string}, :Tags [:seq-of {:Key string, :Value string}], :HumanTaskConfig {:TaskTitle string, :TaskTimeLimitInSeconds integer, :MaxConcurrentTaskCount integer, :WorkteamArn string, :NumberOfHumanWorkersPerDataObject integer, :TaskKeywords [:seq-of string], :PublicWorkforceTaskPrice {:AmountInUsd {:Dollars integer, :Cents integer, :TenthFractionsOfACent integer}}, :TaskDescription string, :AnnotationConsolidationConfig {:AnnotationConsolidationLambdaArn string}, :UiConfig {:UiTemplateS3Uri string, :HumanTaskUiArn string}, :TaskAvailabilityLifetimeInSeconds integer, :PreHumanTaskLambdaArn string}, :LabelingJobAlgorithmsConfig {:LabelingJobAlgorithmSpecificationArn string, :InitialActiveLearningModelArn string, :LabelingJobResourceConfig {:VolumeKmsKeyId string}}, :LabelAttributeName string, :LabelCounters {:TotalLabeled integer, :HumanLabeled integer, :MachineLabeled integer, :FailedNonRetryableError integer, :Unlabeled integer}, :OutputConfig {:S3OutputPath string, :KmsKeyId string, :SnsTopicArn string}, :RoleArn string, :JobReferenceCode string, :CreationTime timestamp, :LabelCategoryConfigS3Uri string, :LabelingJobName string, :LabelingJobArn string, :FailureReason string, :InputConfig {:DataSource {:S3DataSource {:ManifestS3Uri string}, :SnsDataSource {:SnsTopicArn string}}, :DataAttributes {:ContentClassifiers [:seq-of [:one-of ["FreeOfPersonallyIdentifiableInformation" "FreeOfAdultContent"]]]}}, :LabelingJobStatus [:one-of ["Initializing" "InProgress" "Completed" "Failed" "Stopping" "Stopped"]], :StoppingConditions {:MaxHumanLabeledObjectCount integer, :MaxPercentageOfInputDatasetLabeled integer}}}, :CreateDataQualityJobDefinition {:documentation "
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
", :request {:DataQualityBaselineConfig {:BaseliningJobName string, :ConstraintsResource {:S3Uri string}, :StatisticsResource {:S3Uri string}}, :JobDefinitionName string, :JobResources {:ClusterConfig {:InstanceCount integer, :InstanceType [:one-of ["ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.8xlarge" "ml.r5.12xlarge" "ml.r5.16xlarge" "ml.r5.24xlarge"]], :VolumeSizeInGB integer, :VolumeKmsKeyId string}}, :Tags [:seq-of {:Key string, :Value string}], :DataQualityAppSpecification {:ImageUri string, :ContainerEntrypoint [:seq-of string], :ContainerArguments [:seq-of string], :RecordPreprocessorSourceUri string, :PostAnalyticsProcessorSourceUri string, :Environment [:map-of string string]}, :NetworkConfig {:EnableInterContainerTrafficEncryption boolean, :EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}}, :StoppingCondition {:MaxRuntimeInSeconds integer}, :DataQualityJobInput {:EndpointInput {:InferenceAttribute string, :EndpointName string, :ProbabilityThresholdAttribute double, :EndTimeOffset string, :FeaturesAttribute string, :ProbabilityAttribute string, :S3InputMode [:one-of ["Pipe" "File"]], :LocalPath string, :StartTimeOffset string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]]}}, :RoleArn string, :DataQualityJobOutputConfig {:MonitoringOutputs [:seq-of {:S3Output {:S3Uri string, :LocalPath string, :S3UploadMode [:one-of ["Continuous" "EndOfJob"]]}}], :KmsKeyId string}}, :required [:JobDefinitionName :DataQualityAppSpecification :DataQualityJobInput :DataQualityJobOutputConfig :JobResources :RoleArn], :response {:JobDefinitionArn string}}, :CreateHyperParameterTuningJob {:documentation "
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
", :request {:HyperParameterTuningJobName string, :HyperParameterTuningJobConfig {:Strategy [:one-of ["Bayesian" "Random"]], :HyperParameterTuningJobObjective {:Type [:one-of ["Maximize" "Minimize"]], :MetricName string}, :ResourceLimits {:MaxNumberOfTrainingJobs integer, :MaxParallelTrainingJobs integer}, :ParameterRanges {:IntegerParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :ContinuousParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :CategoricalParameterRanges [:seq-of {:Name string, :Values [:seq-of string]}]}, :TrainingJobEarlyStoppingType [:one-of ["Off" "Auto"]], :TuningJobCompletionCriteria {:TargetObjectiveMetricValue "float"}}, :TrainingJobDefinition {:EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}, :ResourceConfig {:InstanceType [:one-of ["ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.g4dn.xlarge" "ml.g4dn.2xlarge" "ml.g4dn.4xlarge" "ml.g4dn.8xlarge" "ml.g4dn.12xlarge" "ml.g4dn.16xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.p3dn.24xlarge" "ml.p4d.24xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.c5n.xlarge" "ml.c5n.2xlarge" "ml.c5n.4xlarge" "ml.c5n.9xlarge" "ml.c5n.18xlarge"]], :InstanceCount integer, :VolumeSizeInGB integer, :VolumeKmsKeyId string}, :StaticHyperParameters [:map-of string string], :HyperParameterRanges {:IntegerParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :ContinuousParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :CategoricalParameterRanges [:seq-of {:Name string, :Values [:seq-of string]}]}, :TuningObjective {:Type [:one-of ["Maximize" "Minimize"]], :MetricName string}, :EnableInterContainerTrafficEncryption boolean, :AlgorithmSpecification {:TrainingImage string, :TrainingInputMode [:one-of ["Pipe" "File"]], :AlgorithmName string, :MetricDefinitions [:seq-of {:Name string, :Regex string}]}, :OutputDataConfig {:KmsKeyId string, :S3OutputPath string}, :CheckpointConfig {:S3Uri string, :LocalPath string}, :StoppingCondition {:MaxRuntimeInSeconds integer, :MaxWaitTimeInSeconds integer}, :RoleArn string, :EnableManagedSpotTraining boolean, :DefinitionName string, :InputDataConfig [:seq-of {:ChannelName string, :DataSource {:S3DataSource {:S3DataType [:one-of ["ManifestFile" "S3Prefix" "AugmentedManifestFile"]], :S3Uri string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]], :AttributeNames [:seq-of string]}, :FileSystemDataSource {:FileSystemId string, :FileSystemAccessMode [:one-of ["rw" "ro"]], :FileSystemType [:one-of ["EFS" "FSxLustre"]], :DirectoryPath string}}, :ContentType string, :CompressionType [:one-of ["None" "Gzip"]], :RecordWrapperType [:one-of ["None" "RecordIO"]], :InputMode [:one-of ["Pipe" "File"]], :ShuffleConfig {:Seed long}}]}, :TrainingJobDefinitions [:seq-of {:EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}, :ResourceConfig {:InstanceType [:one-of ["ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.g4dn.xlarge" "ml.g4dn.2xlarge" "ml.g4dn.4xlarge" "ml.g4dn.8xlarge" "ml.g4dn.12xlarge" "ml.g4dn.16xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.p3dn.24xlarge" "ml.p4d.24xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.c5n.xlarge" "ml.c5n.2xlarge" "ml.c5n.4xlarge" "ml.c5n.9xlarge" "ml.c5n.18xlarge"]], :InstanceCount integer, :VolumeSizeInGB integer, :VolumeKmsKeyId string}, :StaticHyperParameters [:map-of string string], :HyperParameterRanges {:IntegerParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :ContinuousParameterRanges [:seq-of {:Name string, :MinValue string, :MaxValue string, :ScalingType [:one-of ["Auto" "Linear" "Logarithmic" "ReverseLogarithmic"]]}], :CategoricalParameterRanges [:seq-of {:Name string, :Values [:seq-of string]}]}, :TuningObjective {:Type [:one-of ["Maximize" "Minimize"]], :MetricName string}, :EnableInterContainerTrafficEncryption boolean, :AlgorithmSpecification {:TrainingImage string, :TrainingInputMode [:one-of ["Pipe" "File"]], :AlgorithmName string, :MetricDefinitions [:seq-of {:Name string, :Regex string}]}, :OutputDataConfig {:KmsKeyId string, :S3OutputPath string}, :CheckpointConfig {:S3Uri string, :LocalPath string}, :StoppingCondition {:MaxRuntimeInSeconds integer, :MaxWaitTimeInSeconds integer}, :RoleArn string, :EnableManagedSpotTraining boolean, :DefinitionName string, :InputDataConfig [:seq-of {:ChannelName string, :DataSource {:S3DataSource {:S3DataType [:one-of ["ManifestFile" "S3Prefix" "AugmentedManifestFile"]], :S3Uri string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]], :AttributeNames [:seq-of string]}, :FileSystemDataSource {:FileSystemId string, :FileSystemAccessMode [:one-of ["rw" "ro"]], :FileSystemType [:one-of ["EFS" "FSxLustre"]], :DirectoryPath string}}, :ContentType string, :CompressionType [:one-of ["None" "Gzip"]], :RecordWrapperType [:one-of ["None" "RecordIO"]], :InputMode [:one-of ["Pipe" "File"]], :ShuffleConfig {:Seed long}}]}], :WarmStartConfig {:ParentHyperParameterTuningJobs [:seq-of {:HyperParameterTuningJobName string}], :WarmStartType [:one-of ["IdenticalDataAndAlgorithm" "TransferLearning"]]}, :Tags [:seq-of {:Key string, :Value string}]}, :required [:HyperParameterTuningJobName :HyperParameterTuningJobConfig], :response {:HyperParameterTuningJobArn string}}, :CreateModelExplainabilityJobDefinition {:documentation "
Creates the definition for a model explainability job.
", :request {:JobDefinitionName string, :JobResources {:ClusterConfig {:InstanceCount integer, :InstanceType [:one-of ["ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.8xlarge" "ml.r5.12xlarge" "ml.r5.16xlarge" "ml.r5.24xlarge"]], :VolumeSizeInGB integer, :VolumeKmsKeyId string}}, :Tags [:seq-of {:Key string, :Value string}], :ModelExplainabilityJobOutputConfig {:MonitoringOutputs [:seq-of {:S3Output {:S3Uri string, :LocalPath string, :S3UploadMode [:one-of ["Continuous" "EndOfJob"]]}}], :KmsKeyId string}, :NetworkConfig {:EnableInterContainerTrafficEncryption boolean, :EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}}, :StoppingCondition {:MaxRuntimeInSeconds integer}, :RoleArn string, :ModelExplainabilityAppSpecification {:ImageUri string, :ConfigUri string, :Environment [:map-of string string]}, :ModelExplainabilityBaselineConfig {:BaseliningJobName string, :ConstraintsResource {:S3Uri string}}, :ModelExplainabilityJobInput {:EndpointInput {:InferenceAttribute string, :EndpointName string, :ProbabilityThresholdAttribute double, :EndTimeOffset string, :FeaturesAttribute string, :ProbabilityAttribute string, :S3InputMode [:one-of ["Pipe" "File"]], :LocalPath string, :StartTimeOffset string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]]}}}, :required [:JobDefinitionName :ModelExplainabilityAppSpecification :ModelExplainabilityJobInput :ModelExplainabilityJobOutputConfig :JobResources :RoleArn], :response {:JobDefinitionArn string}}, :DisassociateTrialComponent {:documentation "
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
 
To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
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Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
  
 Use this API if you want to use Amazon SageMaker hosting services to deploy models into production. 
  
In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy. 
 
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. 
 
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)). 
  
When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
 ", :request {:EndpointConfigName string, :ProductionVariants [:seq-of {:VariantName string, :ModelName string, :InitialInstanceCount integer, :InstanceType [:one-of ["ml.t2.medium" "ml.t2.large" "ml.t2.xlarge" "ml.t2.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.m5d.large" "ml.m5d.xlarge" "ml.m5d.2xlarge" "ml.m5d.4xlarge" "ml.m5d.12xlarge" "ml.m5d.24xlarge" "ml.c4.large" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.large" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.c5d.large" "ml.c5d.xlarge" "ml.c5d.2xlarge" "ml.c5d.4xlarge" "ml.c5d.9xlarge" "ml.c5d.18xlarge" "ml.g4dn.xlarge" "ml.g4dn.2xlarge" "ml.g4dn.4xlarge" "ml.g4dn.8xlarge" "ml.g4dn.12xlarge" "ml.g4dn.16xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.12xlarge" "ml.r5.24xlarge" "ml.r5d.large" "ml.r5d.xlarge" "ml.r5d.2xlarge" "ml.r5d.4xlarge" "ml.r5d.12xlarge" "ml.r5d.24xlarge" "ml.inf1.xlarge" "ml.inf1.2xlarge" "ml.inf1.6xlarge" "ml.inf1.24xlarge"]], :InitialVariantWeight "float", :AcceleratorType [:one-of ["ml.eia1.medium" "ml.eia1.large" "ml.eia1.xlarge" "ml.eia2.medium" "ml.eia2.large" "ml.eia2.xlarge"]]}], :DataCaptureConfig {:EnableCapture boolean, :InitialSamplingPercentage integer, :DestinationS3Uri string, :KmsKeyId string, :CaptureOptions [:seq-of {:CaptureMode [:one-of ["Input" "Output"]]}], :CaptureContentTypeHeader {:CsvContentTypes [:seq-of string], :JsonContentTypes [:seq-of string]}}, :Tags [:seq-of {:Key string, :Value string}], :KmsKeyId string}, :required [:EndpointConfigName :ProductionVariants], :response {:EndpointConfigArn string}}, :DeleteDataQualityJobDefinition {:documentation "
Deletes a data quality monitoring job definition.
", :request {:JobDefinitionName string}, :required [:JobDefinitionName], :response nil}, :DescribeDeviceFleet {:documentation "
A description of the fleet the device belongs to.
", :request {:DeviceFleetName string}, :required [:DeviceFleetName], :response {:DeviceFleetName string, :DeviceFleetArn string, :OutputConfig {:S3OutputLocation string, :KmsKeyId string}, :Description string, :CreationTime timestamp, :LastModifiedTime timestamp, :RoleArn string, :IotRoleAlias string}}, :ListDeviceFleets {:documentation "
Returns a list of devices in the fleet.
", :request {:CreationTimeBefore timestamp, :NameContains string, :NextToken string, :SortBy [:one-of ["NAME" "CREATION_TIME" "LAST_MODIFIED_TIME"]], :SortOrder [:one-of ["Ascending" "Descending"]], :CreationTimeAfter timestamp, :MaxResults integer, :LastModifiedTimeBefore timestamp, :LastModifiedTimeAfter timestamp}, :required nil, :response {:DeviceFleetSummaries [:seq-of {:DeviceFleetArn string, :DeviceFleetName string, :CreationTime timestamp, :LastModifiedTime timestamp}], :NextToken string}}, :CreateMonitoringSchedule {:documentation "
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
", :request {:MonitoringScheduleName string, :MonitoringScheduleConfig {:ScheduleConfig {:ScheduleExpression string}, :MonitoringJobDefinition {:BaselineConfig {:BaseliningJobName string, :ConstraintsResource {:S3Uri string}, :StatisticsResource {:S3Uri string}}, :Environment [:map-of string string], :MonitoringInputs [:seq-of {:EndpointInput {:InferenceAttribute string, :EndpointName string, :ProbabilityThresholdAttribute double, :EndTimeOffset string, :FeaturesAttribute string, :ProbabilityAttribute string, :S3InputMode [:one-of ["Pipe" "File"]], :LocalPath string, :StartTimeOffset string, :S3DataDistributionType [:one-of ["FullyReplicated" "ShardedByS3Key"]]}}], :MonitoringResources {:ClusterConfig {:InstanceCount integer, :InstanceType [:one-of ["ml.t3.medium" "ml.t3.large" "ml.t3.xlarge" "ml.t3.2xlarge" "ml.m4.xlarge" "ml.m4.2xlarge" "ml.m4.4xlarge" "ml.m4.10xlarge" "ml.m4.16xlarge" "ml.c4.xlarge" "ml.c4.2xlarge" "ml.c4.4xlarge" "ml.c4.8xlarge" "ml.p2.xlarge" "ml.p2.8xlarge" "ml.p2.16xlarge" "ml.p3.2xlarge" "ml.p3.8xlarge" "ml.p3.16xlarge" "ml.c5.xlarge" "ml.c5.2xlarge" "ml.c5.4xlarge" "ml.c5.9xlarge" "ml.c5.18xlarge" "ml.m5.large" "ml.m5.xlarge" "ml.m5.2xlarge" "ml.m5.4xlarge" "ml.m5.12xlarge" "ml.m5.24xlarge" "ml.r5.large" "ml.r5.xlarge" "ml.r5.2xlarge" "ml.r5.4xlarge" "ml.r5.8xlarge" "ml.r5.12xlarge" "ml.r5.16xlarge" "ml.r5.24xlarge"]], :VolumeSizeInGB integer, :VolumeKmsKeyId string}}, :NetworkConfig {:EnableInterContainerTrafficEncryption boolean, :EnableNetworkIsolation boolean, :VpcConfig {:SecurityGroupIds [:seq-of string], :Subnets [:seq-of string]}}, :StoppingCondition {:MaxRuntimeInSeconds integer}, :MonitoringAppSpecification {:ImageUri string, :ContainerEntrypoint [:seq-of string], :ContainerArguments [:seq-of string], :RecordPreprocessorSourceUri string, :PostAnalyticsProcessorSourceUri string}, :RoleArn string, :MonitoringOutputConfig {:MonitoringOutputs [:seq-of {:S3Output {:S3Uri string, :LocalPath string, :S3UploadMode [:one-of ["Continuous" "EndOfJob"]]}}], :KmsKeyId string}}, :MonitoringJobDefinitionName string, :MonitoringType [:one-of ["DataQuality" "ModelQuality" "ModelBias" "ModelExplainability"]]}, :Tags [:seq-of {:Key string, :Value string}]}, :required [:MonitoringScheduleName :MonitoringScheduleConfig], :response {:MonitoringScheduleArn string}}, :DeleteModelBiasJobDefinition {:documentation "
Deletes an Amazon SageMaker model bias job definition.
", :request {:JobDefinitionName string}, :required [:JobDefinitionName], :response nil}, :StopNotebookInstance {:documentation "
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.
 
To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work. 
", :request {:NotebookInstanceName string}, :required [:NotebookInstanceName], :response nil}, :UpdateWorkteam {:documentation "
Updates an existing work team with new member definitions or description.
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For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)). 
  
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The endpoint name must be unique within an AWS Region in your AWS account. 
 
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If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
  
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Option 1: For a full Amazon SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
 
 
 
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 \"Action\": [\"sagemaker:CreateEndpoint\", \"sagemaker:CreateEndpointConfig\"] 
 
 \"Resource\": [ 
 
 \"arn:aws:sagemaker:region:account-id:endpoint/endpointName\" 
 
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 ] 
 
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Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
 
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 VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.
 
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For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
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Stops a pipeline execution.
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Lists the contexts in your account and their properties.
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Gets a list of parameters for a pipeline execution.
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Updates the display name of a trial.
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