Sagemaker

This page documents function available when using the Sagemaker module, created with @service Sagemaker.

Index

Documentation

Main.Sagemaker.add_associationMethod
add_association(destination_arn, source_arn)
add_association(destination_arn, source_arn, params::Dict{String,<:Any})

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Arguments

  • destination_arn: The Amazon Resource Name (ARN) of the destination.
  • source_arn: The ARN of the source.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AssociationType": The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use. ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job. AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment. DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs. Produced - The source generated the destination. For example, a training job produced a model artifact.
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Main.Sagemaker.add_tagsMethod
add_tags(resource_arn, tags)
add_tags(resource_arn, tags, params::Dict{String,<:Any})

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies. Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

Arguments

  • resource_arn: The Amazon Resource Name (ARN) of the resource that you want to tag.
  • tags: An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
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Main.Sagemaker.associate_trial_componentMethod
associate_trial_component(trial_component_name, trial_name)
associate_trial_component(trial_component_name, trial_name, params::Dict{String,<:Any})

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Arguments

  • trial_component_name: The name of the component to associated with the trial.
  • trial_name: The name of the trial to associate with.
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Main.Sagemaker.batch_describe_model_packageMethod
batch_describe_model_package(model_package_arn_list)
batch_describe_model_package(model_package_arn_list, params::Dict{String,<:Any})

This action batch describes a list of versioned model packages

Arguments

  • model_package_arn_list: The list of Amazon Resource Name (ARN) of the model package groups.
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Main.Sagemaker.create_actionMethod
create_action(action_name, action_type, source)
create_action(action_name, action_type, source, params::Dict{String,<:Any})

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Arguments

  • action_name: The name of the action. Must be unique to your account in an Amazon Web Services Region.
  • action_type: The action type.
  • source: The source type, ID, and URI.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The description of the action.
  • "MetadataProperties":
  • "Properties": A list of properties to add to the action.
  • "Status": The status of the action.
  • "Tags": A list of tags to apply to the action.
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Main.Sagemaker.create_algorithmMethod
create_algorithm(algorithm_name, training_specification)
create_algorithm(algorithm_name, training_specification, params::Dict{String,<:Any})

Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.

Arguments

  • algorithm_name: The name of the algorithm.
  • training_specification: Specifies details about training jobs run by this algorithm, including the following: The Amazon ECR path of the container and the version digest of the algorithm. The hyperparameters that the algorithm supports. The instance types that the algorithm supports for training. Whether the algorithm supports distributed training. The metrics that the algorithm emits to Amazon CloudWatch. Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs. The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AlgorithmDescription": A description of the algorithm.
  • "CertifyForMarketplace": Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
  • "InferenceSpecification": Specifies details about inference jobs that the algorithm runs, including the following: The Amazon ECR paths of containers that contain the inference code and model artifacts. The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference. The input and output content formats that the algorithm supports for inference.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  • "ValidationSpecification": Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
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Main.Sagemaker.create_appMethod
create_app(app_name, app_type, domain_id, user_profile_name)
create_app(app_name, app_type, domain_id, user_profile_name, params::Dict{String,<:Any})

Creates a running app for the specified UserProfile. Supported apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

Arguments

  • app_name: The name of the app.
  • app_type: The type of app. Supported apps are JupyterServer and KernelGateway. TensorBoard is not supported.
  • domain_id: The domain ID.
  • user_profile_name: The user profile name.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ResourceSpec": The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
  • "Tags": Each tag consists of a key and an optional value. Tag keys must be unique per resource.
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Main.Sagemaker.create_app_image_configMethod
create_app_image_config(app_image_config_name)
create_app_image_config(app_image_config_name, params::Dict{String,<:Any})

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

Arguments

  • app_image_config_name: The name of the AppImageConfig. Must be unique to your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "KernelGatewayImageConfig": The KernelGatewayImageConfig.
  • "Tags": A list of tags to apply to the AppImageConfig.
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Main.Sagemaker.create_artifactMethod
create_artifact(artifact_type, source)
create_artifact(artifact_type, source, params::Dict{String,<:Any})

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Arguments

  • artifact_type: The artifact type.
  • source: The ID, ID type, and URI of the source.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ArtifactName": The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
  • "MetadataProperties":
  • "Properties": A list of properties to add to the artifact.
  • "Tags": A list of tags to apply to the artifact.
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Main.Sagemaker.create_auto_mljobMethod
create_auto_mljob(auto_mljob_name, input_data_config, output_data_config, role_arn)
create_auto_mljob(auto_mljob_name, input_data_config, output_data_config, role_arn, params::Dict{String,<:Any})

Creates an Autopilot job. Find the best-performing model after you run an Autopilot job by calling . For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Arguments

  • auto_mljob_name: Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
  • input_data_config: An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.
  • output_data_config: Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
  • role_arn: The ARN of the role that is used to access the data.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AutoMLJobConfig": Contains CompletionCriteria and SecurityConfig settings for the AutoML job.
  • "AutoMLJobObjective": Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjectiveMetricName and Autopilot infers whether to minimize or maximize it.
  • "GenerateCandidateDefinitionsOnly": Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
  • "ModelDeployConfig": Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
  • "ProblemType": Defines the type of supervised learning available for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
  • "Tags": Each tag consists of a key and an optional value. Tag keys must be unique per resource.
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Main.Sagemaker.create_code_repositoryMethod
create_code_repository(code_repository_name, git_config)
create_code_repository(code_repository_name, git_config, params::Dict{String,<:Any})

Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.

Arguments

  • code_repository_name: The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
  • git_config: Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
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Main.Sagemaker.create_compilation_jobMethod
create_compilation_job(compilation_job_name, input_config, output_config, role_arn, stopping_condition)
create_compilation_job(compilation_job_name, input_config, output_config, role_arn, stopping_condition, params::Dict{String,<:Any})

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket 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 with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following: A name for the compilation job Information about the input model artifacts The output location for the compiled model and the device (target) that the model runs on The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job. You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Arguments

  • compilation_job_name: A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
  • input_config: Provides 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.
  • output_config: Provides information about the output location for the compiled model and the target device the model runs on.
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf. During model compilation, Amazon SageMaker needs your permission to: Read input data from an S3 bucket Write model artifacts to an S3 bucket Write logs to Amazon CloudWatch Logs Publish metrics to Amazon CloudWatch You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
  • stopping_condition: Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  • "VpcConfig": A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
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Main.Sagemaker.create_contextMethod
create_context(context_name, context_type, source)
create_context(context_name, context_type, source, params::Dict{String,<:Any})

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Arguments

  • context_name: The name of the context. Must be unique to your account in an Amazon Web Services Region.
  • context_type: The context type.
  • source: The source type, ID, and URI.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The description of the context.
  • "Properties": A list of properties to add to the context.
  • "Tags": A list of tags to apply to the context.
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Main.Sagemaker.create_data_quality_job_definitionMethod
create_data_quality_job_definition(data_quality_app_specification, data_quality_job_input, data_quality_job_output_config, job_definition_name, job_resources, role_arn)
create_data_quality_job_definition(data_quality_app_specification, data_quality_job_input, data_quality_job_output_config, job_definition_name, job_resources, role_arn, params::Dict{String,<:Any})

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Arguments

  • data_quality_app_specification: Specifies the container that runs the monitoring job.
  • data_quality_job_input: A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
  • data_quality_job_output_config:
  • job_definition_name: The name for the monitoring job definition.
  • job_resources:
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DataQualityBaselineConfig": Configures the constraints and baselines for the monitoring job.
  • "NetworkConfig": Specifies networking configuration for the monitoring job.
  • "StoppingCondition":
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_device_fleetMethod
create_device_fleet(device_fleet_name, output_config)
create_device_fleet(device_fleet_name, output_config, params::Dict{String,<:Any})

Creates a device fleet.

Arguments

  • device_fleet_name: The name of the fleet that the device belongs to.
  • output_config: The output configuration for storing sample data collected by the fleet.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": A description of the fleet.
  • "EnableIotRoleAlias": Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
  • "RoleArn": The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
  • "Tags": Creates tags for the specified fleet.
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Main.Sagemaker.create_domainMethod
create_domain(auth_mode, default_user_settings, domain_name, subnet_ids, vpc_id)
create_domain(auth_mode, default_user_settings, domain_name, subnet_ids, vpc_id, params::Dict{String,<:Any})

Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other. EFS storage When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption. VPC configuration All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available: PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value. 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. When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections. NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully. For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.

Arguments

  • auth_mode: The mode of authentication that members use to access the domain.
  • default_user_settings: The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API. SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.
  • domain_name: A name for the domain.
  • subnet_ids: The VPC subnets that Studio uses for communication.
  • vpc_id: The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AppNetworkAccessType": Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly. PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access VpcOnly - All Studio traffic is through the specified VPC and subnets
  • "AppSecurityGroupManagement": The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
  • "DomainSettings": A collection of Domain settings.
  • "HomeEfsFileSystemKmsKeyId": This member is deprecated and replaced with KmsKeyId.
  • "KmsKeyId": SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
  • "Tags": Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. Tags that you specify for the Domain are also added to all Apps that the Domain launches.
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Main.Sagemaker.create_edge_packaging_jobMethod
create_edge_packaging_job(compilation_job_name, edge_packaging_job_name, model_name, model_version, output_config, role_arn)
create_edge_packaging_job(compilation_job_name, edge_packaging_job_name, model_name, model_version, output_config, role_arn, params::Dict{String,<:Any})

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Arguments

  • compilation_job_name: The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
  • edge_packaging_job_name: The name of the edge packaging job.
  • model_name: The name of the model.
  • model_version: The version of the model.
  • output_config: Provides information about the output location for the packaged model.
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ResourceKey": The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
  • "Tags": Creates tags for the packaging job.
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Main.Sagemaker.create_endpointMethod
create_endpoint(endpoint_config_name, endpoint_name)
create_endpoint(endpoint_config_name, endpoint_name, params::Dict{String,<:Any})

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API. Use this API to deploy models using Amazon SageMaker hosting services. For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see the Create Endpoint example notebook. You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. When 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. When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide. To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role. Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy. Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role: "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"] "Resource": [ "arn:aws:sagemaker:region:account-id:endpoint/endpointName" "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName" ] For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Arguments

  • endpoint_config_name: The name of an endpoint configuration. For more information, see CreateEndpointConfig.
  • endpoint_name: The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in .

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DeploymentConfig":
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
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Main.Sagemaker.create_endpoint_configMethod
create_endpoint_config(endpoint_config_name, production_variants)
create_endpoint_config(endpoint_config_name, production_variants, params::Dict{String,<:Any})

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. 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.

Arguments

  • endpoint_config_name: The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
  • production_variants: An list of ProductionVariant objects, one for each model that you want to host at this endpoint.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AsyncInferenceConfig": Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync .
  • "DataCaptureConfig":
  • "KmsKeyId": The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKeyId can be any of the following formats: Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab Alias name: alias/ExampleAlias Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint, UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMS Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
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Main.Sagemaker.create_experimentMethod
create_experiment(experiment_name)
create_experiment(experiment_name, params::Dict{String,<:Any})

Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

Arguments

  • experiment_name: The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The description of the experiment.
  • "DisplayName": The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName, the value in ExperimentName is displayed.
  • "Tags": A list of tags to associate with the experiment. You can use Search API to search on the tags.
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Main.Sagemaker.create_feature_groupMethod
create_feature_group(event_time_feature_name, feature_definitions, feature_group_name, record_identifier_feature_name)
create_feature_group(event_time_feature_name, feature_definitions, feature_group_name, record_identifier_feature_name, params::Dict{String,<:Any})

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record. The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account. You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

Arguments

  • event_time_feature_name: The name of the feature that stores the EventTime of a Record in a FeatureGroup. An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime. An EventTime can be a String or Fractional. Fractional: EventTime feature values must be a Unix timestamp in seconds. String: EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy, MM, and dd represent the year, month, and day respectively and HH, mm, ss, and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.
  • feature_definitions: A list of Feature names and types. Name and Type is compulsory per Feature. Valid feature FeatureTypes are Integral, Fractional and String. FeatureNames cannot be any of the following: isdeleted, writetime, apiinvocationtime You can create up to 2,500 FeatureDefinitions per FeatureGroup.
  • feature_group_name: The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name: Must start and end with an alphanumeric character. Can only contain alphanumeric character and hyphens. Spaces are not allowed.
  • record_identifier_feature_name: The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore. Only the latest record per identifier value will be stored in the OnlineStore. RecordIdentifierFeatureName must be one of feature definitions' names. You use the RecordIdentifierFeatureName to access data in a FeatureStore. This name: Must start and end with an alphanumeric character. Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": A free-form description of a FeatureGroup.
  • "OfflineStoreConfig": Use this to configure an OfflineFeatureStore. This parameter allows you to specify: The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore. A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog. An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent. To learn more about this parameter, see OfflineStoreConfig.
  • "OnlineStoreConfig": You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig; the default value is False. You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the OnlineStore.
  • "RoleArn": The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
  • "Tags": Tags used to identify Features in each FeatureGroup.
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Main.Sagemaker.create_flow_definitionMethod
create_flow_definition(flow_definition_name, human_loop_config, output_config, role_arn)
create_flow_definition(flow_definition_name, human_loop_config, output_config, role_arn, params::Dict{String,<:Any})

Creates a flow definition.

Arguments

  • flow_definition_name: The name of your flow definition.
  • human_loop_config: An object containing information about the tasks the human reviewers will perform.
  • output_config: An object containing information about where the human review results will be uploaded.
  • role_arn: The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "HumanLoopActivationConfig": An object containing information about the events that trigger a human workflow.
  • "HumanLoopRequestSource": Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
  • "Tags": An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
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Main.Sagemaker.create_human_task_uiMethod
create_human_task_ui(human_task_ui_name, ui_template)
create_human_task_ui(human_task_ui_name, ui_template, params::Dict{String,<:Any})

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

Arguments

  • human_task_ui_name: The name of the user interface you are creating.
  • ui_template:

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
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Main.Sagemaker.create_hyper_parameter_tuning_jobMethod
create_hyper_parameter_tuning_job(hyper_parameter_tuning_job_config, hyper_parameter_tuning_job_name)
create_hyper_parameter_tuning_job(hyper_parameter_tuning_job_config, hyper_parameter_tuning_job_name, params::Dict{String,<:Any})

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.

Arguments

  • hyper_parameter_tuning_job_config: The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
  • hyper_parameter_tuning_job_name: The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
  • "TrainingJobDefinition": The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
  • "TrainingJobDefinitions": A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
  • "WarmStartConfig": Specifies the configuration for starting the hyperparameter tuning job using one or more previous 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. If you specify IDENTICALDATAAND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job 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.
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Main.Sagemaker.create_imageMethod
create_image(image_name, role_arn)
create_image(image_name, role_arn, params::Dict{String,<:Any})

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.

Arguments

  • image_name: The name of the image. Must be unique to your account.
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The description of the image.
  • "DisplayName": The display name of the image. If not provided, ImageName is displayed.
  • "Tags": A list of tags to apply to the image.
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Main.Sagemaker.create_image_versionMethod
create_image_version(base_image, client_token, image_name)
create_image_version(base_image, client_token, image_name, params::Dict{String,<:Any})

Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.

Arguments

  • base_image: The registry path of the container image to use as the starting point for this version. The path is an Amazon Container Registry (ECR) URI in the following format: &lt;acct-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;repo-name[:tag] or [@digest]&gt;
  • client_token: A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
  • image_name: The ImageName of the Image to create a version of.
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Main.Sagemaker.create_labeling_jobMethod
create_labeling_job(human_task_config, input_config, label_attribute_name, labeling_job_name, output_config, role_arn)
create_labeling_job(human_task_config, input_config, label_attribute_name, labeling_job_name, output_config, role_arn, params::Dict{String,<:Any})

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Arguments

  • human_task_config: Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
  • input_config: Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects. You must specify at least one of the following: S3DataSource or SnsDataSource. Use SnsDataSource to 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 that stops after all data objects in the input manifest file have been labeled. Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job. If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.
  • label_attribute_name: The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements. The name can't end with "-metadata". If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref". Image semantic segmentation (SemanticSegmentation), and adjustment (AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation) labeling jobs for this task type. Video frame object detection (VideoObjectDetection), and adjustment and verification (AdjustmentVideoObjectDetection) labeling jobs for this task type. Video frame object tracking (VideoObjectTracking), and adjustment and verification (AdjustmentVideoObjectTracking) labeling jobs for this task type. 3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type. 3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling jobs for this task type. If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.
  • labeling_job_name: The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
  • output_config: The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
  • role_arn: The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "LabelCategoryConfigS3Uri": The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects. For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs. For named entity recognition jobs, in addition to "labels", you must provide worker instructions in the label category configuration file using the "instructions" parameter: "instructions": {"shortInstruction":"&lt;h1&gt;Add header&lt;/h1&gt;&lt;p&gt;Add Instructions&lt;/p&gt;", "fullInstruction":"&lt;p&gt;Add additional instructions.&lt;/p&gt;"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) . For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label1, label2,...,labeln with your label categories. { "document-version": "2018-11-28", "labels": [{"label": "label1"},{"label": "label2"},...{"label": "labeln"}] } Note the following about the label category configuration file: For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one. Each label category must be unique, you cannot specify duplicate label categories. If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the LabelAttributeName of the labeling job you want to adjust or verify annotations of.
  • "LabelingJobAlgorithmsConfig": Configures the information required to perform automated data labeling.
  • "StoppingConditions": A set of conditions for stopping the 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.
  • "Tags": An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_modelMethod
create_model(execution_role_arn, model_name)
create_model(execution_role_arn, model_name, params::Dict{String,<:Any})

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment. 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 (Amazon Web Services SDK for Python (Boto 3)). To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the CreateModel request, you must define a container with the PrimaryContainer parameter. In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

Arguments

  • execution_role_arn: The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles. To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
  • model_name: The name of the new model.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Containers": Specifies the containers in the inference pipeline.
  • "EnableNetworkIsolation": Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
  • "InferenceExecutionConfig": Specifies details of how containers in a multi-container endpoint are called.
  • "PrimaryContainer": The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  • "VpcConfig": A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
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Main.Sagemaker.create_model_bias_job_definitionMethod
create_model_bias_job_definition(job_definition_name, job_resources, model_bias_app_specification, model_bias_job_input, model_bias_job_output_config, role_arn)
create_model_bias_job_definition(job_definition_name, job_resources, model_bias_app_specification, model_bias_job_input, model_bias_job_output_config, role_arn, params::Dict{String,<:Any})

Creates the definition for a model bias job.

Arguments

  • job_definition_name: The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
  • job_resources:
  • model_bias_app_specification: Configures the model bias job to run a specified Docker container image.
  • model_bias_job_input: Inputs for the model bias job.
  • model_bias_job_output_config:
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ModelBiasBaselineConfig": The baseline configuration for a model bias job.
  • "NetworkConfig": Networking options for a model bias job.
  • "StoppingCondition":
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_model_explainability_job_definitionMethod
create_model_explainability_job_definition(job_definition_name, job_resources, model_explainability_app_specification, model_explainability_job_input, model_explainability_job_output_config, role_arn)
create_model_explainability_job_definition(job_definition_name, job_resources, model_explainability_app_specification, model_explainability_job_input, model_explainability_job_output_config, role_arn, params::Dict{String,<:Any})

Creates the definition for a model explainability job.

Arguments

  • job_definition_name: The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
  • job_resources:
  • model_explainability_app_specification: Configures the model explainability job to run a specified Docker container image.
  • model_explainability_job_input: Inputs for the model explainability job.
  • model_explainability_job_output_config:
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ModelExplainabilityBaselineConfig": The baseline configuration for a model explainability job.
  • "NetworkConfig": Networking options for a model explainability job.
  • "StoppingCondition":
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_model_packageMethod
create_model_package()
create_model_package(params::Dict{String,<:Any})

Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CertifyForMarketplace": Whether to certify the model package for listing on Amazon Web Services Marketplace. This parameter is optional for unversioned models, and does not apply to versioned models.
  • "ClientToken": A unique token that guarantees that the call to this API is idempotent.
  • "CustomerMetadataProperties": The metadata properties associated with the model package versions.
  • "InferenceSpecification": Specifies details about inference jobs that can be run with models based on this model package, including the following: The Amazon ECR paths of containers that contain the inference code and model artifacts. The instance types that the model package supports for transform jobs and real-time endpoints used for inference. The input and output content formats that the model package supports for inference.
  • "MetadataProperties":
  • "ModelApprovalStatus": Whether the model is approved for deployment. This parameter is optional for versioned models, and does not apply to unversioned models. For versioned models, the value of this parameter must be set to Approved to deploy the model.
  • "ModelMetrics": A structure that contains model metrics reports.
  • "ModelPackageDescription": A description of the model package.
  • "ModelPackageGroupName": The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models.
  • "ModelPackageName": The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models.
  • "SourceAlgorithmSpecification": Details about the algorithm that was used to create the model package.
  • "Tags": A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
  • "ValidationSpecification": Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
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Main.Sagemaker.create_model_package_groupMethod
create_model_package_group(model_package_group_name)
create_model_package_group(model_package_group_name, params::Dict{String,<:Any})

Creates a model group. A model group contains a group of model versions.

Arguments

  • model_package_group_name: The name of the model group.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ModelPackageGroupDescription": A description for the model group.
  • "Tags": A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
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Main.Sagemaker.create_model_quality_job_definitionMethod
create_model_quality_job_definition(job_definition_name, job_resources, model_quality_app_specification, model_quality_job_input, model_quality_job_output_config, role_arn)
create_model_quality_job_definition(job_definition_name, job_resources, model_quality_app_specification, model_quality_job_input, model_quality_job_output_config, role_arn, params::Dict{String,<:Any})

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

Arguments

  • job_definition_name: The name of the monitoring job definition.
  • job_resources:
  • model_quality_app_specification: The container that runs the monitoring job.
  • model_quality_job_input: A list of the inputs that are monitored. Currently endpoints are supported.
  • model_quality_job_output_config:
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ModelQualityBaselineConfig": Specifies the constraints and baselines for the monitoring job.
  • "NetworkConfig": Specifies the network configuration for the monitoring job.
  • "StoppingCondition":
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_monitoring_scheduleMethod
create_monitoring_schedule(monitoring_schedule_config, monitoring_schedule_name)
create_monitoring_schedule(monitoring_schedule_config, monitoring_schedule_name, params::Dict{String,<:Any})

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.

Arguments

  • monitoring_schedule_config: The configuration object that specifies the monitoring schedule and defines the monitoring job.
  • monitoring_schedule_name: The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_notebook_instanceMethod
create_notebook_instance(instance_type, notebook_instance_name, role_arn)
create_notebook_instance(instance_type, notebook_instance_name, role_arn, params::Dict{String,<:Any})

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework. After receiving the request, Amazon SageMaker does the following: Creates a network interface in the Amazon SageMaker VPC. (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it. After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models. For more information, see How It Works.

Arguments

  • instance_type: The type of ML compute instance to launch for the notebook instance.
  • notebook_instance_name: The name of the new notebook instance.
  • role_arn: When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles. To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AcceleratorTypes": A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
  • "AdditionalCodeRepositories": An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
  • "DefaultCodeRepository": A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
  • "DirectInternetAccess": Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC. For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
  • "KmsKeyId": The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
  • "LifecycleConfigName": The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
  • "PlatformIdentifier": The platform identifier of the notebook instance runtime environment.
  • "RootAccess": Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled. Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
  • "SecurityGroupIds": The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
  • "SubnetId": The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  • "VolumeSizeInGB": The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
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Main.Sagemaker.create_notebook_instance_lifecycle_configMethod
create_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name)
create_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name, params::Dict{String,<:Any})

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.

Arguments

  • notebook_instance_lifecycle_config_name: The name of the lifecycle configuration.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "OnCreate": A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
  • "OnStart": A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
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Main.Sagemaker.create_pipelineMethod
create_pipeline(client_request_token, pipeline_definition, pipeline_name, role_arn)
create_pipeline(client_request_token, pipeline_definition, pipeline_name, role_arn, params::Dict{String,<:Any})

Creates a pipeline using a JSON pipeline definition.

Arguments

  • client_request_token: A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
  • pipeline_definition: The JSON pipeline definition of the pipeline.
  • pipeline_name: The name of the pipeline.
  • role_arn: The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "PipelineDescription": A description of the pipeline.
  • "PipelineDisplayName": The display name of the pipeline.
  • "Tags": A list of tags to apply to the created pipeline.
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Main.Sagemaker.create_presigned_domain_urlMethod
create_presigned_domain_url(domain_id, user_profile_name)
create_presigned_domain_url(domain_id, user_profile_name, params::Dict{String,<:Any})

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM. The IAM role or user used to call this API defines the permissions to access the app. 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 app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint . The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

Arguments

  • domain_id: The domain ID.
  • user_profile_name: The name of the UserProfile to sign-in as.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ExpiresInSeconds": The number of seconds until the pre-signed URL expires. This value defaults to 300.
  • "SessionExpirationDurationInSeconds": The session expiration duration in seconds. This value defaults to 43200.
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Main.Sagemaker.create_presigned_notebook_instance_urlMethod
create_presigned_notebook_instance_url(notebook_instance_name)
create_presigned_notebook_instance_url(notebook_instance_name, params::Dict{String,<:Any})

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 Amazon Web Services console sign-in page.

Arguments

  • notebook_instance_name: The name of the notebook instance.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "SessionExpirationDurationInSeconds": The duration of the session, in seconds. The default is 12 hours.
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Main.Sagemaker.create_processing_jobMethod
create_processing_job(app_specification, processing_job_name, processing_resources, role_arn)
create_processing_job(app_specification, processing_job_name, processing_resources, role_arn, params::Dict{String,<:Any})

Creates a processing job.

Arguments

  • app_specification: Configures the processing job to run a specified Docker container image.
  • processing_job_name: The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
  • processing_resources: 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.
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Environment": The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
  • "ExperimentConfig":
  • "NetworkConfig": Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
  • "ProcessingInputs": An array of inputs configuring the data to download into the processing container.
  • "ProcessingOutputConfig": Output configuration for the processing job.
  • "StoppingCondition": The time limit for how long the processing job is allowed to run.
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_projectMethod
create_project(project_name, service_catalog_provisioning_details)
create_project(project_name, service_catalog_provisioning_details, params::Dict{String,<:Any})

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.

Arguments

  • project_name: The name of the project.
  • service_catalog_provisioning_details: The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ProjectDescription": A description for the project.
  • "Tags": An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
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Main.Sagemaker.create_studio_lifecycle_configMethod
create_studio_lifecycle_config(studio_lifecycle_config_app_type, studio_lifecycle_config_content, studio_lifecycle_config_name)
create_studio_lifecycle_config(studio_lifecycle_config_app_type, studio_lifecycle_config_content, studio_lifecycle_config_name, params::Dict{String,<:Any})

Creates a new Studio Lifecycle Configuration.

Arguments

  • studio_lifecycle_config_app_type: The App type that the Lifecycle Configuration is attached to.
  • studio_lifecycle_config_content: The content of your Studio Lifecycle Configuration script. This content must be base64 encoded.
  • studio_lifecycle_config_name: The name of the Studio Lifecycle Configuration to create.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
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Main.Sagemaker.create_training_jobMethod
create_training_job(algorithm_specification, output_data_config, resource_config, role_arn, stopping_condition, training_job_name)
create_training_job(algorithm_specification, output_data_config, resource_config, role_arn, stopping_condition, training_job_name, params::Dict{String,<:Any})

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 a managed spot training job has to complete. Environment - The environment variables to set in the Docker container. RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError. For more information about Amazon SageMaker, see How It Works.

Arguments

  • algorithm_specification: The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
  • output_data_config: Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
  • resource_config: The resources, including the ML compute instances and ML storage volumes, to use for model training. ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
  • role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles. To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
  • stopping_condition: Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, 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.
  • training_job_name: The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CheckpointConfig": Contains information about the output location for managed spot training checkpoint data.
  • "DebugHookConfig":
  • "DebugRuleConfigurations": Configuration information for Debugger rules for debugging output tensors.
  • "EnableInterContainerTrafficEncryption": To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
  • "EnableManagedSpotTraining": To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run. The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
  • "EnableNetworkIsolation": Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
  • "Environment": The environment variables to set in the Docker container.
  • "ExperimentConfig":
  • "HyperParameters": Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms. You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.
  • "InputDataConfig": An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location. Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, trainingdata and validationdata. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format. Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
  • "ProfilerConfig":
  • "ProfilerRuleConfigurations": Configuration information for Debugger rules for profiling system and framework metrics.
  • "RetryStrategy": The number of times to retry the job when the job fails due to an InternalServerError.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
  • "TensorBoardOutputConfig":
  • "VpcConfig": A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
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Main.Sagemaker.create_transform_jobMethod
create_transform_job(model_name, transform_input, transform_job_name, transform_output, transform_resources)
create_transform_job(model_name, transform_input, transform_job_name, transform_output, transform_resources, params::Dict{String,<:Any})

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances for the transform job. For more information about how batch transformation works, see Batch Transform.

Arguments

  • model_name: The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
  • transform_input: Describes the input source and the way the transform job consumes it.
  • transform_job_name: The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
  • transform_output: Describes the results of the transform job.
  • transform_resources: Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "BatchStrategy": Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord. To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line. To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.
  • "DataProcessing": 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.
  • "Environment": The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
  • "ExperimentConfig":
  • "MaxConcurrentTransforms": The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
  • "MaxPayloadInMB": The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
  • "ModelClientConfig": Configures the timeout and maximum number of retries for processing a transform job invocation.
  • "Tags": (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
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Main.Sagemaker.create_trialMethod
create_trial(experiment_name, trial_name)
create_trial(experiment_name, trial_name, params::Dict{String,<:Any})

Creates an 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 SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services 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.

Arguments

  • experiment_name: The name of the experiment to associate the trial with.
  • trial_name: The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DisplayName": The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
  • "MetadataProperties":
  • "Tags": A list of tags to associate with the trial. You can use Search API to search on the tags.
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Main.Sagemaker.create_trial_componentMethod
create_trial_component(trial_component_name)
create_trial_component(trial_component_name, params::Dict{String,<:Any})

Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.

Arguments

  • trial_component_name: The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DisplayName": The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
  • "EndTime": When the component ended.
  • "InputArtifacts": The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
  • "MetadataProperties":
  • "OutputArtifacts": The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
  • "Parameters": The hyperparameters for the component.
  • "StartTime": When the component started.
  • "Status": The status of the component. States include: InProgress Completed Failed
  • "Tags": A list of tags to associate with the component. You can use Search API to search on the tags.
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Main.Sagemaker.create_user_profileMethod
create_user_profile(domain_id, user_profile_name)
create_user_profile(domain_id, user_profile_name, params::Dict{String,<:Any})

Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.

Arguments

  • domain_id: The ID of the associated Domain.
  • user_profile_name: A name for the UserProfile. This value is not case sensitive.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "SingleSignOnUserIdentifier": A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is SSO, this field is required. If the Domain's AuthMode is not SSO, this field cannot be specified.
  • "SingleSignOnUserValue": The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not SSO, this field cannot be specified.
  • "Tags": Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
  • "UserSettings": A collection of settings.
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Main.Sagemaker.create_workforceMethod
create_workforce(workforce_name)
create_workforce(workforce_name, params::Dict{String,<:Any})

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

Arguments

  • workforce_name: The name of the private workforce.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CognitoConfig": Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool. Do not use OidcConfig if you specify values for CognitoConfig.
  • "OidcConfig": Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use CognitoConfig if you specify values for OidcConfig.
  • "SourceIpConfig":
  • "Tags": An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
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Main.Sagemaker.create_workteamMethod
create_workteam(description, member_definitions, workteam_name)
create_workteam(description, member_definitions, workteam_name, params::Dict{String,<:Any})

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.

Arguments

  • description: A description of the work team.
  • member_definitions: A list of MemberDefinition objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. Do not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups.
  • workteam_name: The name of the work team. Use this name to identify the work team.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "NotificationConfiguration": Configures notification of workers regarding available or expiring work items.
  • "Tags": An array of key-value pairs. For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
  • "WorkforceName": The name of the workforce.
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Main.Sagemaker.delete_actionMethod
delete_action(action_name)
delete_action(action_name, params::Dict{String,<:Any})

Deletes an action.

Arguments

  • action_name: The name of the action to delete.
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Main.Sagemaker.delete_algorithmMethod
delete_algorithm(algorithm_name)
delete_algorithm(algorithm_name, params::Dict{String,<:Any})

Removes the specified algorithm from your account.

Arguments

  • algorithm_name: The name of the algorithm to delete.
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Main.Sagemaker.delete_appMethod
delete_app(app_name, app_type, domain_id, user_profile_name)
delete_app(app_name, app_type, domain_id, user_profile_name, params::Dict{String,<:Any})

Used to stop and delete an app.

Arguments

  • app_name: The name of the app.
  • app_type: The type of app.
  • domain_id: The domain ID.
  • user_profile_name: The user profile name.
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Main.Sagemaker.delete_app_image_configMethod
delete_app_image_config(app_image_config_name)
delete_app_image_config(app_image_config_name, params::Dict{String,<:Any})

Deletes an AppImageConfig.

Arguments

  • app_image_config_name: The name of the AppImageConfig to delete.
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Main.Sagemaker.delete_artifactMethod
delete_artifact()
delete_artifact(params::Dict{String,<:Any})

Deletes an artifact. Either ArtifactArn or Source must be specified.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ArtifactArn": The Amazon Resource Name (ARN) of the artifact to delete.
  • "Source": The URI of the source.
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Main.Sagemaker.delete_associationMethod
delete_association(destination_arn, source_arn)
delete_association(destination_arn, source_arn, params::Dict{String,<:Any})

Deletes an association.

Arguments

  • destination_arn: The Amazon Resource Name (ARN) of the destination.
  • source_arn: The ARN of the source.
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Main.Sagemaker.delete_code_repositoryMethod
delete_code_repository(code_repository_name)
delete_code_repository(code_repository_name, params::Dict{String,<:Any})

Deletes the specified Git repository from your account.

Arguments

  • code_repository_name: The name of the Git repository to delete.
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Main.Sagemaker.delete_contextMethod
delete_context(context_name)
delete_context(context_name, params::Dict{String,<:Any})

Deletes an context.

Arguments

  • context_name: The name of the context to delete.
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Main.Sagemaker.delete_data_quality_job_definitionMethod
delete_data_quality_job_definition(job_definition_name)
delete_data_quality_job_definition(job_definition_name, params::Dict{String,<:Any})

Deletes a data quality monitoring job definition.

Arguments

  • job_definition_name: The name of the data quality monitoring job definition to delete.
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Main.Sagemaker.delete_device_fleetMethod
delete_device_fleet(device_fleet_name)
delete_device_fleet(device_fleet_name, params::Dict{String,<:Any})

Deletes a fleet.

Arguments

  • device_fleet_name: The name of the fleet to delete.
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Main.Sagemaker.delete_domainMethod
delete_domain(domain_id)
delete_domain(domain_id, params::Dict{String,<:Any})

Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.

Arguments

  • domain_id: The domain ID.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "RetentionPolicy": The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
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Main.Sagemaker.delete_endpointMethod
delete_endpoint(endpoint_name)
delete_endpoint(endpoint_name, params::Dict{String,<:Any})

Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created. Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.

Arguments

  • endpoint_name: The name of the endpoint that you want to delete.
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Main.Sagemaker.delete_endpoint_configMethod
delete_endpoint_config(endpoint_config_name)
delete_endpoint_config(endpoint_config_name, params::Dict{String,<:Any})

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Arguments

  • endpoint_config_name: The name of the endpoint configuration that you want to delete.
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Main.Sagemaker.delete_experimentMethod
delete_experiment(experiment_name)
delete_experiment(experiment_name, params::Dict{String,<:Any})

Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.

Arguments

  • experiment_name: The name of the experiment to delete.
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Main.Sagemaker.delete_feature_groupMethod
delete_feature_group(feature_group_name)
delete_feature_group(feature_group_name, params::Dict{String,<:Any})

Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called. Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.

Arguments

  • feature_group_name: The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
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Main.Sagemaker.delete_flow_definitionMethod
delete_flow_definition(flow_definition_name)
delete_flow_definition(flow_definition_name, params::Dict{String,<:Any})

Deletes the specified flow definition.

Arguments

  • flow_definition_name: The name of the flow definition you are deleting.
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Main.Sagemaker.delete_human_task_uiMethod
delete_human_task_ui(human_task_ui_name)
delete_human_task_ui(human_task_ui_name, params::Dict{String,<:Any})

Use this operation to delete a human task user interface (worker task template). To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.

Arguments

  • human_task_ui_name: The name of the human task user interface (work task template) you want to delete.
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Main.Sagemaker.delete_imageMethod
delete_image(image_name)
delete_image(image_name, params::Dict{String,<:Any})

Deletes a SageMaker image and all versions of the image. The container images aren't deleted.

Arguments

  • image_name: The name of the image to delete.
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Main.Sagemaker.delete_image_versionMethod
delete_image_version(image_name, version)
delete_image_version(image_name, version, params::Dict{String,<:Any})

Deletes a version of a SageMaker image. The container image the version represents isn't deleted.

Arguments

  • image_name: The name of the image.
  • version: The version to delete.
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Main.Sagemaker.delete_modelMethod
delete_model(model_name)
delete_model(model_name, params::Dict{String,<:Any})

Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

Arguments

  • model_name: The name of the model to delete.
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Main.Sagemaker.delete_model_bias_job_definitionMethod
delete_model_bias_job_definition(job_definition_name)
delete_model_bias_job_definition(job_definition_name, params::Dict{String,<:Any})

Deletes an Amazon SageMaker model bias job definition.

Arguments

  • job_definition_name: The name of the model bias job definition to delete.
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Main.Sagemaker.delete_model_explainability_job_definitionMethod
delete_model_explainability_job_definition(job_definition_name)
delete_model_explainability_job_definition(job_definition_name, params::Dict{String,<:Any})

Deletes an Amazon SageMaker model explainability job definition.

Arguments

  • job_definition_name: The name of the model explainability job definition to delete.
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Main.Sagemaker.delete_model_packageMethod
delete_model_package(model_package_name)
delete_model_package(model_package_name, params::Dict{String,<:Any})

Deletes a model package. A model package is used to create Amazon SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.

Arguments

  • model_package_name: The name or Amazon Resource Name (ARN) of the model package to delete. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
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Main.Sagemaker.delete_model_package_groupMethod
delete_model_package_group(model_package_group_name)
delete_model_package_group(model_package_group_name, params::Dict{String,<:Any})

Deletes the specified model group.

Arguments

  • model_package_group_name: The name of the model group to delete.
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Main.Sagemaker.delete_model_package_group_policyMethod
delete_model_package_group_policy(model_package_group_name)
delete_model_package_group_policy(model_package_group_name, params::Dict{String,<:Any})

Deletes a model group resource policy.

Arguments

  • model_package_group_name: The name of the model group for which to delete the policy.
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Main.Sagemaker.delete_model_quality_job_definitionMethod
delete_model_quality_job_definition(job_definition_name)
delete_model_quality_job_definition(job_definition_name, params::Dict{String,<:Any})

Deletes the secified model quality monitoring job definition.

Arguments

  • job_definition_name: The name of the model quality monitoring job definition to delete.
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Main.Sagemaker.delete_monitoring_scheduleMethod
delete_monitoring_schedule(monitoring_schedule_name)
delete_monitoring_schedule(monitoring_schedule_name, params::Dict{String,<:Any})

Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.

Arguments

  • monitoring_schedule_name: The name of the monitoring schedule to delete.
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Main.Sagemaker.delete_notebook_instanceMethod
delete_notebook_instance(notebook_instance_name)
delete_notebook_instance(notebook_instance_name, params::Dict{String,<:Any})

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API. When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Arguments

  • notebook_instance_name: The name of the Amazon SageMaker notebook instance to delete.
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Main.Sagemaker.delete_notebook_instance_lifecycle_configMethod
delete_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name)
delete_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name, params::Dict{String,<:Any})

Deletes a notebook instance lifecycle configuration.

Arguments

  • notebook_instance_lifecycle_config_name: The name of the lifecycle configuration to delete.
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Main.Sagemaker.delete_pipelineMethod
delete_pipeline(client_request_token, pipeline_name)
delete_pipeline(client_request_token, pipeline_name, params::Dict{String,<:Any})

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

Arguments

  • client_request_token: A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
  • pipeline_name: The name of the pipeline to delete.
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Main.Sagemaker.delete_projectMethod
delete_project(project_name)
delete_project(project_name, params::Dict{String,<:Any})

Delete the specified project.

Arguments

  • project_name: The name of the project to delete.
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Main.Sagemaker.delete_studio_lifecycle_configMethod
delete_studio_lifecycle_config(studio_lifecycle_config_name)
delete_studio_lifecycle_config(studio_lifecycle_config_name, params::Dict{String,<:Any})

Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

Arguments

  • studio_lifecycle_config_name: The name of the Studio Lifecycle Configuration to delete.
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Main.Sagemaker.delete_tagsMethod
delete_tags(resource_arn, tag_keys)
delete_tags(resource_arn, tag_keys, params::Dict{String,<:Any})

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. When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.

Arguments

  • resource_arn: The Amazon Resource Name (ARN) of the resource whose tags you want to delete.
  • tag_keys: An array or one or more tag keys to delete.
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Main.Sagemaker.delete_trialMethod
delete_trial(trial_name)
delete_trial(trial_name, params::Dict{String,<:Any})

Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.

Arguments

  • trial_name: The name of the trial to delete.
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Main.Sagemaker.delete_trial_componentMethod
delete_trial_component(trial_component_name)
delete_trial_component(trial_component_name, params::Dict{String,<:Any})

Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Arguments

  • trial_component_name: The name of the component to delete.
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Main.Sagemaker.delete_user_profileMethod
delete_user_profile(domain_id, user_profile_name)
delete_user_profile(domain_id, user_profile_name, params::Dict{String,<:Any})

Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.

Arguments

  • domain_id: The domain ID.
  • user_profile_name: The user profile name.
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Main.Sagemaker.delete_workforceMethod
delete_workforce(workforce_name)
delete_workforce(workforce_name, params::Dict{String,<:Any})

Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce. If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.

Arguments

  • workforce_name: The name of the workforce.
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Main.Sagemaker.delete_workteamMethod
delete_workteam(workteam_name)
delete_workteam(workteam_name, params::Dict{String,<:Any})

Deletes an existing work team. This operation can't be undone.

Arguments

  • workteam_name: The name of the work team to delete.
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Main.Sagemaker.deregister_devicesMethod
deregister_devices(device_fleet_name, device_names)
deregister_devices(device_fleet_name, device_names, params::Dict{String,<:Any})

Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

Arguments

  • device_fleet_name: The name of the fleet the devices belong to.
  • device_names: The unique IDs of the devices.
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Main.Sagemaker.describe_actionMethod
describe_action(action_name)
describe_action(action_name, params::Dict{String,<:Any})

Describes an action.

Arguments

  • action_name: The name of the action to describe.
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Main.Sagemaker.describe_algorithmMethod
describe_algorithm(algorithm_name)
describe_algorithm(algorithm_name, params::Dict{String,<:Any})

Returns a description of the specified algorithm that is in your account.

Arguments

  • algorithm_name: The name of the algorithm to describe.
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Main.Sagemaker.describe_appMethod
describe_app(app_name, app_type, domain_id, user_profile_name)
describe_app(app_name, app_type, domain_id, user_profile_name, params::Dict{String,<:Any})

Describes the app.

Arguments

  • app_name: The name of the app.
  • app_type: The type of app.
  • domain_id: The domain ID.
  • user_profile_name: The user profile name.
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Main.Sagemaker.describe_app_image_configMethod
describe_app_image_config(app_image_config_name)
describe_app_image_config(app_image_config_name, params::Dict{String,<:Any})

Describes an AppImageConfig.

Arguments

  • app_image_config_name: The name of the AppImageConfig to describe.
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Main.Sagemaker.describe_artifactMethod
describe_artifact(artifact_arn)
describe_artifact(artifact_arn, params::Dict{String,<:Any})

Describes an artifact.

Arguments

  • artifact_arn: The Amazon Resource Name (ARN) of the artifact to describe.
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Main.Sagemaker.describe_auto_mljobMethod
describe_auto_mljob(auto_mljob_name)
describe_auto_mljob(auto_mljob_name, params::Dict{String,<:Any})

Returns information about an Amazon SageMaker AutoML job.

Arguments

  • auto_mljob_name: Requests information about an AutoML job using its unique name.
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Main.Sagemaker.describe_code_repositoryMethod
describe_code_repository(code_repository_name)
describe_code_repository(code_repository_name, params::Dict{String,<:Any})

Gets details about the specified Git repository.

Arguments

  • code_repository_name: The name of the Git repository to describe.
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Main.Sagemaker.describe_compilation_jobMethod
describe_compilation_job(compilation_job_name)
describe_compilation_job(compilation_job_name, params::Dict{String,<:Any})

Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Arguments

  • compilation_job_name: The name of the model compilation job that you want information about.
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Main.Sagemaker.describe_contextMethod
describe_context(context_name)
describe_context(context_name, params::Dict{String,<:Any})

Describes a context.

Arguments

  • context_name: The name of the context to describe.
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Main.Sagemaker.describe_data_quality_job_definitionMethod
describe_data_quality_job_definition(job_definition_name)
describe_data_quality_job_definition(job_definition_name, params::Dict{String,<:Any})

Gets the details of a data quality monitoring job definition.

Arguments

  • job_definition_name: The name of the data quality monitoring job definition to describe.
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Main.Sagemaker.describe_deviceMethod
describe_device(device_fleet_name, device_name)
describe_device(device_fleet_name, device_name, params::Dict{String,<:Any})

Describes the device.

Arguments

  • device_fleet_name: The name of the fleet the devices belong to.
  • device_name: The unique ID of the device.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "NextToken": Next token of device description.
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Main.Sagemaker.describe_device_fleetMethod
describe_device_fleet(device_fleet_name)
describe_device_fleet(device_fleet_name, params::Dict{String,<:Any})

A description of the fleet the device belongs to.

Arguments

  • device_fleet_name: The name of the fleet.
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Main.Sagemaker.describe_domainMethod
describe_domain(domain_id)
describe_domain(domain_id, params::Dict{String,<:Any})

The description of the domain.

Arguments

  • domain_id: The domain ID.
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Main.Sagemaker.describe_edge_packaging_jobMethod
describe_edge_packaging_job(edge_packaging_job_name)
describe_edge_packaging_job(edge_packaging_job_name, params::Dict{String,<:Any})

A description of edge packaging jobs.

Arguments

  • edge_packaging_job_name: The name of the edge packaging job.
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Main.Sagemaker.describe_endpointMethod
describe_endpoint(endpoint_name)
describe_endpoint(endpoint_name, params::Dict{String,<:Any})

Returns the description of an endpoint.

Arguments

  • endpoint_name: The name of the endpoint.
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Main.Sagemaker.describe_endpoint_configMethod
describe_endpoint_config(endpoint_config_name)
describe_endpoint_config(endpoint_config_name, params::Dict{String,<:Any})

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Arguments

  • endpoint_config_name: The name of the endpoint configuration.
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Main.Sagemaker.describe_experimentMethod
describe_experiment(experiment_name)
describe_experiment(experiment_name, params::Dict{String,<:Any})

Provides a list of an experiment's properties.

Arguments

  • experiment_name: The name of the experiment to describe.
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Main.Sagemaker.describe_feature_groupMethod
describe_feature_group(feature_group_name)
describe_feature_group(feature_group_name, params::Dict{String,<:Any})

Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

Arguments

  • feature_group_name: The name of the FeatureGroup you want described.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "NextToken": A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.
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Main.Sagemaker.describe_flow_definitionMethod
describe_flow_definition(flow_definition_name)
describe_flow_definition(flow_definition_name, params::Dict{String,<:Any})

Returns information about the specified flow definition.

Arguments

  • flow_definition_name: The name of the flow definition.
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Main.Sagemaker.describe_human_task_uiMethod
describe_human_task_ui(human_task_ui_name)
describe_human_task_ui(human_task_ui_name, params::Dict{String,<:Any})

Returns information about the requested human task user interface (worker task template).

Arguments

  • human_task_ui_name: The name of the human task user interface (worker task template) you want information about.
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Main.Sagemaker.describe_hyper_parameter_tuning_jobMethod
describe_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name)
describe_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name, params::Dict{String,<:Any})

Gets a description of a hyperparameter tuning job.

Arguments

  • hyper_parameter_tuning_job_name: The name of the tuning job.
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Main.Sagemaker.describe_imageMethod
describe_image(image_name)
describe_image(image_name, params::Dict{String,<:Any})

Describes a SageMaker image.

Arguments

  • image_name: The name of the image to describe.
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Main.Sagemaker.describe_image_versionMethod
describe_image_version(image_name)
describe_image_version(image_name, params::Dict{String,<:Any})

Describes a version of a SageMaker image.

Arguments

  • image_name: The name of the image.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Version": The version of the image. If not specified, the latest version is described.
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Main.Sagemaker.describe_labeling_jobMethod
describe_labeling_job(labeling_job_name)
describe_labeling_job(labeling_job_name, params::Dict{String,<:Any})

Gets information about a labeling job.

Arguments

  • labeling_job_name: The name of the labeling job to return information for.
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Main.Sagemaker.describe_modelMethod
describe_model(model_name)
describe_model(model_name, params::Dict{String,<:Any})

Describes a model that you created using the CreateModel API.

Arguments

  • model_name: The name of the model.
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Main.Sagemaker.describe_model_bias_job_definitionMethod
describe_model_bias_job_definition(job_definition_name)
describe_model_bias_job_definition(job_definition_name, params::Dict{String,<:Any})

Returns a description of a model bias job definition.

Arguments

  • job_definition_name: The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
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Main.Sagemaker.describe_model_explainability_job_definitionMethod
describe_model_explainability_job_definition(job_definition_name)
describe_model_explainability_job_definition(job_definition_name, params::Dict{String,<:Any})

Returns a description of a model explainability job definition.

Arguments

  • job_definition_name: The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
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Main.Sagemaker.describe_model_packageMethod
describe_model_package(model_package_name)
describe_model_package(model_package_name, params::Dict{String,<:Any})

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

Arguments

  • model_package_name: The name or Amazon Resource Name (ARN) of the model package to describe. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
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Main.Sagemaker.describe_model_package_groupMethod
describe_model_package_group(model_package_group_name)
describe_model_package_group(model_package_group_name, params::Dict{String,<:Any})

Gets a description for the specified model group.

Arguments

  • model_package_group_name: The name of the model group to describe.
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Main.Sagemaker.describe_model_quality_job_definitionMethod
describe_model_quality_job_definition(job_definition_name)
describe_model_quality_job_definition(job_definition_name, params::Dict{String,<:Any})

Returns a description of a model quality job definition.

Arguments

  • job_definition_name: The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
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Main.Sagemaker.describe_monitoring_scheduleMethod
describe_monitoring_schedule(monitoring_schedule_name)
describe_monitoring_schedule(monitoring_schedule_name, params::Dict{String,<:Any})

Describes the schedule for a monitoring job.

Arguments

  • monitoring_schedule_name: Name of a previously created monitoring schedule.
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Main.Sagemaker.describe_notebook_instanceMethod
describe_notebook_instance(notebook_instance_name)
describe_notebook_instance(notebook_instance_name, params::Dict{String,<:Any})

Returns information about a notebook instance.

Arguments

  • notebook_instance_name: The name of the notebook instance that you want information about.
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Main.Sagemaker.describe_notebook_instance_lifecycle_configMethod
describe_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name)
describe_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name, params::Dict{String,<:Any})

Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Arguments

  • notebook_instance_lifecycle_config_name: The name of the lifecycle configuration to describe.
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Main.Sagemaker.describe_pipelineMethod
describe_pipeline(pipeline_name)
describe_pipeline(pipeline_name, params::Dict{String,<:Any})

Describes the details of a pipeline.

Arguments

  • pipeline_name: The name of the pipeline to describe.
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Main.Sagemaker.describe_pipeline_definition_for_executionMethod
describe_pipeline_definition_for_execution(pipeline_execution_arn)
describe_pipeline_definition_for_execution(pipeline_execution_arn, params::Dict{String,<:Any})

Describes the details of an execution's pipeline definition.

Arguments

  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.
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Main.Sagemaker.describe_pipeline_executionMethod
describe_pipeline_execution(pipeline_execution_arn)
describe_pipeline_execution(pipeline_execution_arn, params::Dict{String,<:Any})

Describes the details of a pipeline execution.

Arguments

  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.
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Main.Sagemaker.describe_processing_jobMethod
describe_processing_job(processing_job_name)
describe_processing_job(processing_job_name, params::Dict{String,<:Any})

Returns a description of a processing job.

Arguments

  • processing_job_name: The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
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Main.Sagemaker.describe_projectMethod
describe_project(project_name)
describe_project(project_name, params::Dict{String,<:Any})

Describes the details of a project.

Arguments

  • project_name: The name of the project to describe.
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Main.Sagemaker.describe_studio_lifecycle_configMethod
describe_studio_lifecycle_config(studio_lifecycle_config_name)
describe_studio_lifecycle_config(studio_lifecycle_config_name, params::Dict{String,<:Any})

Describes the Studio Lifecycle Configuration.

Arguments

  • studio_lifecycle_config_name: The name of the Studio Lifecycle Configuration to describe.
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Main.Sagemaker.describe_subscribed_workteamMethod
describe_subscribed_workteam(workteam_arn)
describe_subscribed_workteam(workteam_arn, params::Dict{String,<:Any})

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Arguments

  • workteam_arn: The Amazon Resource Name (ARN) of the subscribed work team to describe.
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Main.Sagemaker.describe_training_jobMethod
describe_training_job(training_job_name)
describe_training_job(training_job_name, params::Dict{String,<:Any})

Returns information about a training job. Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

Arguments

  • training_job_name: The name of the training job.
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Main.Sagemaker.describe_transform_jobMethod
describe_transform_job(transform_job_name)
describe_transform_job(transform_job_name, params::Dict{String,<:Any})

Returns information about a transform job.

Arguments

  • transform_job_name: The name of the transform job that you want to view details of.
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Main.Sagemaker.describe_trialMethod
describe_trial(trial_name)
describe_trial(trial_name, params::Dict{String,<:Any})

Provides a list of a trial's properties.

Arguments

  • trial_name: The name of the trial to describe.
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Main.Sagemaker.describe_trial_componentMethod
describe_trial_component(trial_component_name)
describe_trial_component(trial_component_name, params::Dict{String,<:Any})

Provides a list of a trials component's properties.

Arguments

  • trial_component_name: The name of the trial component to describe.
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Main.Sagemaker.describe_user_profileMethod
describe_user_profile(domain_id, user_profile_name)
describe_user_profile(domain_id, user_profile_name, params::Dict{String,<:Any})

Describes a user profile. For more information, see CreateUserProfile.

Arguments

  • domain_id: The domain ID.
  • user_profile_name: The user profile name. This value is not case sensitive.
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Main.Sagemaker.describe_workforceMethod
describe_workforce(workforce_name)
describe_workforce(workforce_name, params::Dict{String,<:Any})

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks. This operation applies only to private workforces.

Arguments

  • workforce_name: The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.
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Main.Sagemaker.describe_workteamMethod
describe_workteam(workteam_name)
describe_workteam(workteam_name, params::Dict{String,<:Any})

Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).

Arguments

  • workteam_name: The name of the work team to return a description of.
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Main.Sagemaker.disassociate_trial_componentMethod
disassociate_trial_component(trial_component_name, trial_name)
disassociate_trial_component(trial_component_name, trial_name, params::Dict{String,<:Any})

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.

Arguments

  • trial_component_name: The name of the component to disassociate from the trial.
  • trial_name: The name of the trial to disassociate from.
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Main.Sagemaker.get_device_fleet_reportMethod
get_device_fleet_report(device_fleet_name)
get_device_fleet_report(device_fleet_name, params::Dict{String,<:Any})

Describes a fleet.

Arguments

  • device_fleet_name: The name of the fleet.
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Main.Sagemaker.get_model_package_group_policyMethod
get_model_package_group_policy(model_package_group_name)
get_model_package_group_policy(model_package_group_name, params::Dict{String,<:Any})

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Arguments

  • model_package_group_name: The name of the model group for which to get the resource policy.
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Main.Sagemaker.get_search_suggestionsMethod
get_search_suggestions(resource)
get_search_suggestions(resource, params::Dict{String,<:Any})

An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

Arguments

  • resource: The name of the Amazon SageMaker resource to search for.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "SuggestionQuery": Limits the property names that are included in the response.
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Main.Sagemaker.list_actionsMethod
list_actions()
list_actions(params::Dict{String,<:Any})

Lists the actions in your account and their properties.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ActionType": A filter that returns only actions of the specified type.
  • "CreatedAfter": A filter that returns only actions created on or after the specified time.
  • "CreatedBefore": A filter that returns only actions created on or before the specified time.
  • "MaxResults": The maximum number of actions to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListActions didn't return the full set of actions, the call returns a token for getting the next set of actions.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "SourceUri": A filter that returns only actions with the specified source URI.
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Main.Sagemaker.list_algorithmsMethod
list_algorithms()
list_algorithms(params::Dict{String,<:Any})

Lists the machine learning algorithms that have been created.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only algorithms created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only algorithms created before the specified time (timestamp).
  • "MaxResults": The maximum number of algorithms to return in the response.
  • "NameContains": A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
  • "NextToken": If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.
  • "SortBy": The parameter by which to sort the results. The default is CreationTime.
  • "SortOrder": The sort order for the results. The default is Ascending.
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Main.Sagemaker.list_app_image_configsMethod
list_app_image_configs()
list_app_image_configs(params::Dict{String,<:Any})

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only AppImageConfigs created on or after the specified time.
  • "CreationTimeBefore": A filter that returns only AppImageConfigs created on or before the specified time.
  • "MaxResults": The maximum number of AppImageConfigs to return in the response. The default value is 10.
  • "ModifiedTimeAfter": A filter that returns only AppImageConfigs modified on or after the specified time.
  • "ModifiedTimeBefore": A filter that returns only AppImageConfigs modified on or before the specified time.
  • "NameContains": A filter that returns only AppImageConfigs whose name contains the specified string.
  • "NextToken": If the previous call to ListImages didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
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Main.Sagemaker.list_appsMethod
list_apps()
list_apps(params::Dict{String,<:Any})

Lists apps.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DomainIdEquals": A parameter to search for the domain ID.
  • "MaxResults": Returns a list up to a specified limit.
  • "NextToken": If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
  • "SortBy": The parameter by which to sort the results. The default is CreationTime.
  • "SortOrder": The sort order for the results. The default is Ascending.
  • "UserProfileNameEquals": A parameter to search by user profile name.
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Main.Sagemaker.list_artifactsMethod
list_artifacts()
list_artifacts(params::Dict{String,<:Any})

Lists the artifacts in your account and their properties.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ArtifactType": A filter that returns only artifacts of the specified type.
  • "CreatedAfter": A filter that returns only artifacts created on or after the specified time.
  • "CreatedBefore": A filter that returns only artifacts created on or before the specified time.
  • "MaxResults": The maximum number of artifacts to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListArtifacts didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "SourceUri": A filter that returns only artifacts with the specified source URI.
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Main.Sagemaker.list_associationsMethod
list_associations()
list_associations(params::Dict{String,<:Any})

Lists the associations in your account and their properties.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AssociationType": A filter that returns only associations of the specified type.
  • "CreatedAfter": A filter that returns only associations created on or after the specified time.
  • "CreatedBefore": A filter that returns only associations created on or before the specified time.
  • "DestinationArn": A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
  • "DestinationType": A filter that returns only associations with the specified destination type.
  • "MaxResults": The maximum number of associations to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListAssociations didn't return the full set of associations, the call returns a token for getting the next set of associations.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "SourceArn": A filter that returns only associations with the specified source ARN.
  • "SourceType": A filter that returns only associations with the specified source type.
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Main.Sagemaker.list_auto_mljobsMethod
list_auto_mljobs()
list_auto_mljobs(params::Dict{String,<:Any})

Request a list of jobs.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": Request a list of jobs, using a filter for time.
  • "CreationTimeBefore": Request a list of jobs, using a filter for time.
  • "LastModifiedTimeAfter": Request a list of jobs, using a filter for time.
  • "LastModifiedTimeBefore": Request a list of jobs, using a filter for time.
  • "MaxResults": Request a list of jobs up to a specified limit.
  • "NameContains": Request a list of jobs, using a search filter for name.
  • "NextToken": If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
  • "SortBy": The parameter by which to sort the results. The default is Name.
  • "SortOrder": The sort order for the results. The default is Descending.
  • "StatusEquals": Request a list of jobs, using a filter for status.
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Main.Sagemaker.list_candidates_for_auto_mljobMethod
list_candidates_for_auto_mljob(auto_mljob_name)
list_candidates_for_auto_mljob(auto_mljob_name, params::Dict{String,<:Any})

List the candidates created for the job.

Arguments

  • auto_mljob_name: List the candidates created for the job by providing the job's name.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CandidateNameEquals": List the candidates for the job and filter by candidate name.
  • "MaxResults": List the job's candidates up to a specified limit.
  • "NextToken": If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
  • "SortBy": The parameter by which to sort the results. The default is Descending.
  • "SortOrder": The sort order for the results. The default is Ascending.
  • "StatusEquals": List the candidates for the job and filter by status.
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Main.Sagemaker.list_code_repositoriesMethod
list_code_repositories()
list_code_repositories(params::Dict{String,<:Any})

Gets a list of the Git repositories in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only Git repositories that were created after the specified time.
  • "CreationTimeBefore": A filter that returns only Git repositories that were created before the specified time.
  • "LastModifiedTimeAfter": A filter that returns only Git repositories that were last modified after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only Git repositories that were last modified before the specified time.
  • "MaxResults": The maximum number of Git repositories to return in the response.
  • "NameContains": A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
  • "NextToken": If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.
  • "SortBy": The field to sort results by. The default is Name.
  • "SortOrder": The sort order for results. The default is Ascending.
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Main.Sagemaker.list_compilation_jobsMethod
list_compilation_jobs()
list_compilation_jobs(params::Dict{String,<:Any})

Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns the model compilation jobs that were created after a specified time.
  • "CreationTimeBefore": A filter that returns the model compilation jobs that were created before a specified time.
  • "LastModifiedTimeAfter": A filter that returns the model compilation jobs that were modified after a specified time.
  • "LastModifiedTimeBefore": A filter that returns the model compilation jobs that were modified before a specified time.
  • "MaxResults": The maximum number of model compilation jobs to return in the response.
  • "NameContains": A filter that returns the model compilation jobs whose name contains a specified string.
  • "NextToken": If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
  • "SortBy": The field by which to sort results. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponseCompilationJobStatus status.
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Main.Sagemaker.list_contextsMethod
list_contexts()
list_contexts(params::Dict{String,<:Any})

Lists the contexts in your account and their properties.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ContextType": A filter that returns only contexts of the specified type.
  • "CreatedAfter": A filter that returns only contexts created on or after the specified time.
  • "CreatedBefore": A filter that returns only contexts created on or before the specified time.
  • "MaxResults": The maximum number of contexts to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListContexts didn't return the full set of contexts, the call returns a token for getting the next set of contexts.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "SourceUri": A filter that returns only contexts with the specified source URI.
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Main.Sagemaker.list_data_quality_job_definitionsMethod
list_data_quality_job_definitions()
list_data_quality_job_definitions(params::Dict{String,<:Any})

Lists the data quality job definitions in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only data quality monitoring job definitions created after the specified time.
  • "CreationTimeBefore": A filter that returns only data quality monitoring job definitions created before the specified time.
  • "EndpointName": A filter that lists the data quality job definitions associated with the specified endpoint.
  • "MaxResults": The maximum number of data quality monitoring job definitions to return in the response.
  • "NameContains": A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
  • "NextToken": If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.&gt;
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
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Main.Sagemaker.list_device_fleetsMethod
list_device_fleets()
list_device_fleets(params::Dict{String,<:Any})

Returns a list of devices in the fleet.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": Filter fleets where packaging job was created after specified time.
  • "CreationTimeBefore": Filter fleets where the edge packaging job was created before specified time.
  • "LastModifiedTimeAfter": Select fleets where the job was updated after X
  • "LastModifiedTimeBefore": Select fleets where the job was updated before X
  • "MaxResults": The maximum number of results to select.
  • "NameContains": Filter for fleets containing this name in their fleet device name.
  • "NextToken": The response from the last list when returning a list large enough to need tokening.
  • "SortBy": The column to sort by.
  • "SortOrder": What direction to sort in.
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Main.Sagemaker.list_devicesMethod
list_devices()
list_devices(params::Dict{String,<:Any})

A list of devices.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DeviceFleetName": Filter for fleets containing this name in their device fleet name.
  • "LatestHeartbeatAfter": Select fleets where the job was updated after X
  • "MaxResults": Maximum number of results to select.
  • "ModelName": A filter that searches devices that contains this name in any of their models.
  • "NextToken": The response from the last list when returning a list large enough to need tokening.
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Main.Sagemaker.list_domainsMethod
list_domains()
list_domains(params::Dict{String,<:Any})

Lists the domains.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": Returns a list up to a specified limit.
  • "NextToken": If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
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Main.Sagemaker.list_edge_packaging_jobsMethod
list_edge_packaging_jobs()
list_edge_packaging_jobs(params::Dict{String,<:Any})

Returns a list of edge packaging jobs.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": Select jobs where the job was created after specified time.
  • "CreationTimeBefore": Select jobs where the job was created before specified time.
  • "LastModifiedTimeAfter": Select jobs where the job was updated after specified time.
  • "LastModifiedTimeBefore": Select jobs where the job was updated before specified time.
  • "MaxResults": Maximum number of results to select.
  • "ModelNameContains": Filter for jobs where the model name contains this string.
  • "NameContains": Filter for jobs containing this name in their packaging job name.
  • "NextToken": The response from the last list when returning a list large enough to need tokening.
  • "SortBy": Use to specify what column to sort by.
  • "SortOrder": What direction to sort by.
  • "StatusEquals": The job status to filter for.
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Main.Sagemaker.list_endpoint_configsMethod
list_endpoint_configs()
list_endpoint_configs(params::Dict{String,<:Any})

Lists endpoint configurations.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only endpoint configurations created before the specified time (timestamp).
  • "MaxResults": The maximum number of training jobs to return in the response.
  • "NameContains": A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.
  • "NextToken": If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
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Main.Sagemaker.list_endpointsMethod
list_endpoints()
list_endpoints(params::Dict{String,<:Any})

Lists endpoints.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only endpoints that were created before the specified time (timestamp).
  • "LastModifiedTimeAfter": A filter that returns only endpoints that were modified after the specified timestamp.
  • "LastModifiedTimeBefore": A filter that returns only endpoints that were modified before the specified timestamp.
  • "MaxResults": The maximum number of endpoints to return in the response. This value defaults to 10.
  • "NameContains": A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
  • "NextToken": If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.
  • "SortBy": Sorts the list of results. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
  • "StatusEquals": A filter that returns only endpoints with the specified status.
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Main.Sagemaker.list_experimentsMethod
list_experiments()
list_experiments(params::Dict{String,<:Any})

Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreatedAfter": A filter that returns only experiments created after the specified time.
  • "CreatedBefore": A filter that returns only experiments created before the specified time.
  • "MaxResults": The maximum number of experiments to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListExperiments didn't return the full set of experiments, the call returns a token for getting the next set of experiments.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
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Main.Sagemaker.list_feature_groupsMethod
list_feature_groups()
list_feature_groups(params::Dict{String,<:Any})

List FeatureGroups based on given filter and order.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": Use this parameter to search for FeatureGroupss created after a specific date and time.
  • "CreationTimeBefore": Use this parameter to search for FeatureGroupss created before a specific date and time.
  • "FeatureGroupStatusEquals": A FeatureGroup status. Filters by FeatureGroup status.
  • "MaxResults": The maximum number of results returned by ListFeatureGroups.
  • "NameContains": A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name.
  • "NextToken": A token to resume pagination of ListFeatureGroups results.
  • "OfflineStoreStatusEquals": An OfflineStore status. Filters by OfflineStore status.
  • "SortBy": The value on which the feature group list is sorted.
  • "SortOrder": The order in which feature groups are listed.
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Main.Sagemaker.list_flow_definitionsMethod
list_flow_definitions()
list_flow_definitions(params::Dict{String,<:Any})

Returns information about the flow definitions in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
  • "CreationTimeBefore": A filter that returns only flow definitions that were created before the specified timestamp.
  • "MaxResults": The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
  • "NextToken": A token to resume pagination.
  • "SortOrder": An optional value that specifies whether you want the results sorted in Ascending or Descending order.
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Main.Sagemaker.list_human_task_uisMethod
list_human_task_uis()
list_human_task_uis(params::Dict{String,<:Any})

Returns information about the human task user interfaces in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
  • "CreationTimeBefore": A filter that returns only human task user interfaces that were created before the specified timestamp.
  • "MaxResults": The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
  • "NextToken": A token to resume pagination.
  • "SortOrder": An optional value that specifies whether you want the results sorted in Ascending or Descending order.
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Main.Sagemaker.list_hyper_parameter_tuning_jobsMethod
list_hyper_parameter_tuning_jobs()
list_hyper_parameter_tuning_jobs(params::Dict{String,<:Any})

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only tuning jobs that were created after the specified time.
  • "CreationTimeBefore": A filter that returns only tuning jobs that were created before the specified time.
  • "LastModifiedTimeAfter": A filter that returns only tuning jobs that were modified after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only tuning jobs that were modified before the specified time.
  • "MaxResults": The maximum number of tuning jobs to return. The default value is 10.
  • "NameContains": A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
  • "NextToken": If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is Name.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that returns only tuning jobs with the specified status.
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Main.Sagemaker.list_image_versionsMethod
list_image_versions(image_name)
list_image_versions(image_name, params::Dict{String,<:Any})

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

Arguments

  • image_name: The name of the image to list the versions of.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only versions created on or after the specified time.
  • "CreationTimeBefore": A filter that returns only versions created on or before the specified time.
  • "LastModifiedTimeAfter": A filter that returns only versions modified on or after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only versions modified on or before the specified time.
  • "MaxResults": The maximum number of versions to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListImageVersions didn't return the full set of versions, the call returns a token for getting the next set of versions.
  • "SortBy": The property used to sort results. The default value is CREATION_TIME.
  • "SortOrder": The sort order. The default value is DESCENDING.
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Main.Sagemaker.list_imagesMethod
list_images()
list_images(params::Dict{String,<:Any})

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only images created on or after the specified time.
  • "CreationTimeBefore": A filter that returns only images created on or before the specified time.
  • "LastModifiedTimeAfter": A filter that returns only images modified on or after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only images modified on or before the specified time.
  • "MaxResults": The maximum number of images to return in the response. The default value is 10.
  • "NameContains": A filter that returns only images whose name contains the specified string.
  • "NextToken": If the previous call to ListImages didn't return the full set of images, the call returns a token for getting the next set of images.
  • "SortBy": The property used to sort results. The default value is CREATION_TIME.
  • "SortOrder": The sort order. The default value is DESCENDING.
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Main.Sagemaker.list_labeling_jobsMethod
list_labeling_jobs()
list_labeling_jobs(params::Dict{String,<:Any})

Gets a list of labeling jobs.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only labeling jobs created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only labeling jobs created before the specified time (timestamp).
  • "LastModifiedTimeAfter": A filter that returns only labeling jobs modified after the specified time (timestamp).
  • "LastModifiedTimeBefore": A filter that returns only labeling jobs modified before the specified time (timestamp).
  • "MaxResults": The maximum number of labeling jobs to return in each page of the response.
  • "NameContains": A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
  • "NextToken": If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that retrieves only labeling jobs with a specific status.
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Main.Sagemaker.list_labeling_jobs_for_workteamMethod
list_labeling_jobs_for_workteam(workteam_arn)
list_labeling_jobs_for_workteam(workteam_arn, params::Dict{String,<:Any})

Gets a list of labeling jobs assigned to a specified work team.

Arguments

  • workteam_arn: The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only labeling jobs created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only labeling jobs created before the specified time (timestamp).
  • "JobReferenceCodeContains": A filter the limits jobs to only the ones whose job reference code contains the specified string.
  • "MaxResults": The maximum number of labeling jobs to return in each page of the response.
  • "NextToken": If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
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Main.Sagemaker.list_model_bias_job_definitionsMethod
list_model_bias_job_definitions()
list_model_bias_job_definitions(params::Dict{String,<:Any})

Lists model bias jobs definitions that satisfy various filters.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only model bias jobs created after a specified time.
  • "CreationTimeBefore": A filter that returns only model bias jobs created before a specified time.
  • "EndpointName": Name of the endpoint to monitor for model bias.
  • "MaxResults": The maximum number of model bias jobs to return in the response. The default value is 10.
  • "NameContains": Filter for model bias jobs whose name contains a specified string.
  • "NextToken": The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
  • "SortBy": Whether to sort results by the Name or CreationTime field. The default is CreationTime.
  • "SortOrder": Whether to sort the results in Ascending or Descending order. The default is Descending.
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Main.Sagemaker.list_model_explainability_job_definitionsMethod
list_model_explainability_job_definitions()
list_model_explainability_job_definitions(params::Dict{String,<:Any})

Lists model explainability job definitions that satisfy various filters.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only model explainability jobs created after a specified time.
  • "CreationTimeBefore": A filter that returns only model explainability jobs created before a specified time.
  • "EndpointName": Name of the endpoint to monitor for model explainability.
  • "MaxResults": The maximum number of jobs to return in the response. The default value is 10.
  • "NameContains": Filter for model explainability jobs whose name contains a specified string.
  • "NextToken": The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
  • "SortBy": Whether to sort results by the Name or CreationTime field. The default is CreationTime.
  • "SortOrder": Whether to sort the results in Ascending or Descending order. The default is Descending.
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Main.Sagemaker.list_model_package_groupsMethod
list_model_package_groups()
list_model_package_groups(params::Dict{String,<:Any})

Gets a list of the model groups in your Amazon Web Services account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only model groups created after the specified time.
  • "CreationTimeBefore": A filter that returns only model groups created before the specified time.
  • "MaxResults": The maximum number of results to return in the response.
  • "NameContains": A string in the model group name. This filter returns only model groups whose name contains the specified string.
  • "NextToken": If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
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Main.Sagemaker.list_model_packagesMethod
list_model_packages()
list_model_packages(params::Dict{String,<:Any})

Lists the model packages that have been created.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only model packages created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only model packages created before the specified time (timestamp).
  • "MaxResults": The maximum number of model packages to return in the response.
  • "ModelApprovalStatus": A filter that returns only the model packages with the specified approval status.
  • "ModelPackageGroupName": A filter that returns only model versions that belong to the specified model group.
  • "ModelPackageType": A filter that returns onlyl the model packages of the specified type. This can be one of the following values. VERSIONED - List only versioned models. UNVERSIONED - List only unversioined models. BOTH - List both versioned and unversioned models.
  • "NameContains": A string in the model package name. This filter returns only model packages whose name contains the specified string.
  • "NextToken": If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.
  • "SortBy": The parameter by which to sort the results. The default is CreationTime.
  • "SortOrder": The sort order for the results. The default is Ascending.
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Main.Sagemaker.list_model_quality_job_definitionsMethod
list_model_quality_job_definitions()
list_model_quality_job_definitions(params::Dict{String,<:Any})

Gets a list of model quality monitoring job definitions in your account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only model quality monitoring job definitions created after the specified time.
  • "CreationTimeBefore": A filter that returns only model quality monitoring job definitions created before the specified time.
  • "EndpointName": A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
  • "MaxResults": The maximum number of results to return in a call to ListModelQualityJobDefinitions.
  • "NameContains": A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
  • "NextToken": If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
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Main.Sagemaker.list_modelsMethod
list_models()
list_models(params::Dict{String,<:Any})

Lists models created with the CreateModel API.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only models created before the specified time (timestamp).
  • "MaxResults": The maximum number of models to return in the response.
  • "NameContains": A string in the model name. This filter returns only models whose name contains the specified string.
  • "NextToken": If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.
  • "SortBy": Sorts the list of results. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
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Main.Sagemaker.list_monitoring_executionsMethod
list_monitoring_executions()
list_monitoring_executions(params::Dict{String,<:Any})

Returns list of all monitoring job executions.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only jobs created after a specified time.
  • "CreationTimeBefore": A filter that returns only jobs created before a specified time.
  • "EndpointName": Name of a specific endpoint to fetch jobs for.
  • "LastModifiedTimeAfter": A filter that returns only jobs modified before a specified time.
  • "LastModifiedTimeBefore": A filter that returns only jobs modified after a specified time.
  • "MaxResults": The maximum number of jobs to return in the response. The default value is 10.
  • "MonitoringJobDefinitionName": Gets a list of the monitoring job runs of the specified monitoring job definitions.
  • "MonitoringScheduleName": Name of a specific schedule to fetch jobs for.
  • "MonitoringTypeEquals": A filter that returns only the monitoring job runs of the specified monitoring type.
  • "NextToken": The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
  • "ScheduledTimeAfter": Filter for jobs scheduled after a specified time.
  • "ScheduledTimeBefore": Filter for jobs scheduled before a specified time.
  • "SortBy": Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.
  • "SortOrder": Whether to sort the results in Ascending or Descending order. The default is Descending.
  • "StatusEquals": A filter that retrieves only jobs with a specific status.
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Main.Sagemaker.list_monitoring_schedulesMethod
list_monitoring_schedules()
list_monitoring_schedules(params::Dict{String,<:Any})

Returns list of all monitoring schedules.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only monitoring schedules created after a specified time.
  • "CreationTimeBefore": A filter that returns only monitoring schedules created before a specified time.
  • "EndpointName": Name of a specific endpoint to fetch schedules for.
  • "LastModifiedTimeAfter": A filter that returns only monitoring schedules modified after a specified time.
  • "LastModifiedTimeBefore": A filter that returns only monitoring schedules modified before a specified time.
  • "MaxResults": The maximum number of jobs to return in the response. The default value is 10.
  • "MonitoringJobDefinitionName": Gets a list of the monitoring schedules for the specified monitoring job definition.
  • "MonitoringTypeEquals": A filter that returns only the monitoring schedules for the specified monitoring type.
  • "NameContains": Filter for monitoring schedules whose name contains a specified string.
  • "NextToken": The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
  • "SortBy": Whether to sort results by Status, CreationTime, ScheduledTime field. The default is CreationTime.
  • "SortOrder": Whether to sort the results in Ascending or Descending order. The default is Descending.
  • "StatusEquals": A filter that returns only monitoring schedules modified before a specified time.
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Main.Sagemaker.list_notebook_instance_lifecycle_configsMethod
list_notebook_instance_lifecycle_configs()
list_notebook_instance_lifecycle_configs(params::Dict{String,<:Any})

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
  • "LastModifiedTimeAfter": A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
  • "LastModifiedTimeBefore": A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
  • "MaxResults": The maximum number of lifecycle configurations to return in the response.
  • "NameContains": A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
  • "NextToken": If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.
  • "SortBy": Sorts the list of results. The default is CreationTime.
  • "SortOrder": The sort order for results.
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Main.Sagemaker.list_notebook_instancesMethod
list_notebook_instances()
list_notebook_instances(params::Dict{String,<:Any})

Returns a list of the Amazon SageMaker notebook instances in the requester's account in an Amazon Web Services Region.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AdditionalCodeRepositoryEquals": A filter that returns only notebook instances with associated with the specified git repository.
  • "CreationTimeAfter": A filter that returns only notebook instances that were created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only notebook instances that were created before the specified time (timestamp).
  • "DefaultCodeRepositoryContains": A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
  • "LastModifiedTimeAfter": A filter that returns only notebook instances that were modified after the specified time (timestamp).
  • "LastModifiedTimeBefore": A filter that returns only notebook instances that were modified before the specified time (timestamp).
  • "MaxResults": The maximum number of notebook instances to return.
  • "NameContains": A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
  • "NextToken": If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances. You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.
  • "NotebookInstanceLifecycleConfigNameContains": A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
  • "SortBy": The field to sort results by. The default is Name.
  • "SortOrder": The sort order for results.
  • "StatusEquals": A filter that returns only notebook instances with the specified status.
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Main.Sagemaker.list_pipeline_execution_stepsMethod
list_pipeline_execution_steps()
list_pipeline_execution_steps(params::Dict{String,<:Any})

Gets a list of PipeLineExecutionStep objects.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of pipeline execution steps to return in the response.
  • "NextToken": If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.
  • "PipelineExecutionArn": The Amazon Resource Name (ARN) of the pipeline execution.
  • "SortOrder": The field by which to sort results. The default is CreatedTime.
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Main.Sagemaker.list_pipeline_executionsMethod
list_pipeline_executions(pipeline_name)
list_pipeline_executions(pipeline_name, params::Dict{String,<:Any})

Gets a list of the pipeline executions.

Arguments

  • pipeline_name: The name of the pipeline.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreatedAfter": A filter that returns the pipeline executions that were created after a specified time.
  • "CreatedBefore": A filter that returns the pipeline executions that were created before a specified time.
  • "MaxResults": The maximum number of pipeline executions to return in the response.
  • "NextToken": If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
  • "SortBy": The field by which to sort results. The default is CreatedTime.
  • "SortOrder": The sort order for results.
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Main.Sagemaker.list_pipeline_parameters_for_executionMethod
list_pipeline_parameters_for_execution(pipeline_execution_arn)
list_pipeline_parameters_for_execution(pipeline_execution_arn, params::Dict{String,<:Any})

Gets a list of parameters for a pipeline execution.

Arguments

  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of parameters to return in the response.
  • "NextToken": If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
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Main.Sagemaker.list_pipelinesMethod
list_pipelines()
list_pipelines(params::Dict{String,<:Any})

Gets a list of pipelines.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreatedAfter": A filter that returns the pipelines that were created after a specified time.
  • "CreatedBefore": A filter that returns the pipelines that were created before a specified time.
  • "MaxResults": The maximum number of pipelines to return in the response.
  • "NextToken": If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.
  • "PipelineNamePrefix": The prefix of the pipeline name.
  • "SortBy": The field by which to sort results. The default is CreatedTime.
  • "SortOrder": The sort order for results.
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Main.Sagemaker.list_processing_jobsMethod
list_processing_jobs()
list_processing_jobs(params::Dict{String,<:Any})

Lists processing jobs that satisfy various filters.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only processing jobs created after the specified time.
  • "CreationTimeBefore": A filter that returns only processing jobs created after the specified time.
  • "LastModifiedTimeAfter": A filter that returns only processing jobs modified after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only processing jobs modified before the specified time.
  • "MaxResults": The maximum number of processing jobs to return in the response.
  • "NameContains": A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
  • "NextToken": If the result of the previous ListProcessingJobs request was truncated, the response includes a NextToken. To retrieve the next set of processing jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that retrieves only processing jobs with a specific status.
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Main.Sagemaker.list_projectsMethod
list_projects()
list_projects(params::Dict{String,<:Any})

Gets a list of the projects in an Amazon Web Services account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns the projects that were created after a specified time.
  • "CreationTimeBefore": A filter that returns the projects that were created before a specified time.
  • "MaxResults": The maximum number of projects to return in the response.
  • "NameContains": A filter that returns the projects whose name contains a specified string.
  • "NextToken": If the result of the previous ListProjects request was truncated, the response includes a NextToken. To retrieve the next set of projects, use the token in the next request.
  • "SortBy": The field by which to sort results. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
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Main.Sagemaker.list_studio_lifecycle_configsMethod
list_studio_lifecycle_configs()
list_studio_lifecycle_configs(params::Dict{String,<:Any})

Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AppTypeEquals": A parameter to search for the App Type to which the Lifecycle Configuration is attached.
  • "CreationTimeAfter": A filter that returns only Lifecycle Configurations created on or after the specified time.
  • "CreationTimeBefore": A filter that returns only Lifecycle Configurations created on or before the specified time.
  • "MaxResults": The maximum number of Studio Lifecycle Configurations to return in the response. The default value is 10.
  • "ModifiedTimeAfter": A filter that returns only Lifecycle Configurations modified after the specified time.
  • "ModifiedTimeBefore": A filter that returns only Lifecycle Configurations modified before the specified time.
  • "NameContains": A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
  • "NextToken": If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
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Main.Sagemaker.list_subscribed_workteamsMethod
list_subscribed_workteams()
list_subscribed_workteams(params::Dict{String,<:Any})

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of work teams to return in each page of the response.
  • "NameContains": A string in the work team name. This filter returns only work teams whose name contains the specified string.
  • "NextToken": If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
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Main.Sagemaker.list_tagsMethod
list_tags(resource_arn)
list_tags(resource_arn, params::Dict{String,<:Any})

Returns the tags for the specified Amazon SageMaker resource.

Arguments

  • resource_arn: The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": Maximum number of tags to return.
  • "NextToken": If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.
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Main.Sagemaker.list_training_jobsMethod
list_training_jobs()
list_training_jobs(params::Dict{String,<:Any})

Lists training jobs. When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters: { ... MaxResults: 100, StatusEquals: InProgress ... } First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code. aws sagemaker list-training-jobs –max-results 100 –status-equals InProgress

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only training jobs created after the specified time (timestamp).
  • "CreationTimeBefore": A filter that returns only training jobs created before the specified time (timestamp).
  • "LastModifiedTimeAfter": A filter that returns only training jobs modified after the specified time (timestamp).
  • "LastModifiedTimeBefore": A filter that returns only training jobs modified before the specified time (timestamp).
  • "MaxResults": The maximum number of training jobs to return in the response.
  • "NameContains": A string in the training job name. This filter returns only training jobs whose name contains the specified string.
  • "NextToken": If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that retrieves only training jobs with a specific status.
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Main.Sagemaker.list_training_jobs_for_hyper_parameter_tuning_jobMethod
list_training_jobs_for_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name)
list_training_jobs_for_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name, params::Dict{String,<:Any})

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Arguments

  • hyper_parameter_tuning_job_name: The name of the tuning job whose training jobs you want to list.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of training jobs to return. The default value is 10.
  • "NextToken": If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is Name. If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.
  • "SortOrder": The sort order for results. The default is Ascending.
  • "StatusEquals": A filter that returns only training jobs with the specified status.
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Main.Sagemaker.list_transform_jobsMethod
list_transform_jobs()
list_transform_jobs(params::Dict{String,<:Any})

Lists transform jobs.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreationTimeAfter": A filter that returns only transform jobs created after the specified time.
  • "CreationTimeBefore": A filter that returns only transform jobs created before the specified time.
  • "LastModifiedTimeAfter": A filter that returns only transform jobs modified after the specified time.
  • "LastModifiedTimeBefore": A filter that returns only transform jobs modified before the specified time.
  • "MaxResults": The maximum number of transform jobs to return in the response. The default value is 10.
  • "NameContains": A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
  • "NextToken": If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Descending.
  • "StatusEquals": A filter that retrieves only transform jobs with a specific status.
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Main.Sagemaker.list_trial_componentsMethod
list_trial_components()
list_trial_components(params::Dict{String,<:Any})

Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following: ExperimentName SourceArn TrialName

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreatedAfter": A filter that returns only components created after the specified time.
  • "CreatedBefore": A filter that returns only components created before the specified time.
  • "ExperimentName": A filter that returns only components that are part of the specified experiment. If you specify ExperimentName, you can't filter by SourceArn or TrialName.
  • "MaxResults": The maximum number of components to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListTrialComponents didn't return the full set of components, the call returns a token for getting the next set of components.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "SourceArn": A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify SourceArn, you can't filter by ExperimentName or TrialName.
  • "TrialName": A filter that returns only components that are part of the specified trial. If you specify TrialName, you can't filter by ExperimentName or SourceArn.
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Main.Sagemaker.list_trialsMethod
list_trials()
list_trials(params::Dict{String,<:Any})

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.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "CreatedAfter": A filter that returns only trials created after the specified time.
  • "CreatedBefore": A filter that returns only trials created before the specified time.
  • "ExperimentName": A filter that returns only trials that are part of the specified experiment.
  • "MaxResults": The maximum number of trials to return in the response. The default value is 10.
  • "NextToken": If the previous call to ListTrials didn't return the full set of trials, the call returns a token for getting the next set of trials.
  • "SortBy": The property used to sort results. The default value is CreationTime.
  • "SortOrder": The sort order. The default value is Descending.
  • "TrialComponentName": A filter that returns only trials that are associated with the specified trial component.
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Main.Sagemaker.list_user_profilesMethod
list_user_profiles()
list_user_profiles(params::Dict{String,<:Any})

Lists user profiles.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DomainIdEquals": A parameter by which to filter the results.
  • "MaxResults": Returns a list up to a specified limit.
  • "NextToken": If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
  • "SortBy": The parameter by which to sort the results. The default is CreationTime.
  • "SortOrder": The sort order for the results. The default is Ascending.
  • "UserProfileNameContains": A parameter by which to filter the results.
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Main.Sagemaker.list_workforcesMethod
list_workforces()
list_workforces(params::Dict{String,<:Any})

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of workforces returned in the response.
  • "NameContains": A filter you can use to search for workforces using part of the workforce name.
  • "NextToken": A token to resume pagination.
  • "SortBy": Sort workforces using the workforce name or creation date.
  • "SortOrder": Sort workforces in ascending or descending order.
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Main.Sagemaker.list_workteamsMethod
list_workteams()
list_workteams(params::Dict{String,<:Any})

Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of work teams to return in each page of the response.
  • "NameContains": A string in the work team's name. This filter returns only work teams whose name contains the specified string.
  • "NextToken": If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
  • "SortBy": The field to sort results by. The default is CreationTime.
  • "SortOrder": The sort order for results. The default is Ascending.
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Main.Sagemaker.put_model_package_group_policyMethod
put_model_package_group_policy(model_package_group_name, resource_policy)
put_model_package_group_policy(model_package_group_name, resource_policy, params::Dict{String,<:Any})

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Arguments

  • model_package_group_name: The name of the model group to add a resource policy to.
  • resource_policy: The resource policy for the model group.
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Main.Sagemaker.register_devicesMethod
register_devices(device_fleet_name, devices)
register_devices(device_fleet_name, devices, params::Dict{String,<:Any})

Register devices.

Arguments

  • device_fleet_name: The name of the fleet.
  • devices: A list of devices to register with SageMaker Edge Manager.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Tags": The tags associated with devices.
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Main.Sagemaker.render_ui_templateMethod
render_ui_template(role_arn, task)
render_ui_template(role_arn, task, params::Dict{String,<:Any})

Renders the UI template so that you can preview the worker's experience.

Arguments

  • role_arn: The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
  • task: A RenderableTask object containing a representative task to render.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "HumanTaskUiArn": The HumanTaskUiArn of the worker UI that you want to render. Do not provide a HumanTaskUiArn if you use the UiTemplate parameter. See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
  • "UiTemplate": A Template object containing the worker UI template to render.
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Main.Sagemaker.retry_pipeline_executionMethod
retry_pipeline_execution(client_request_token, pipeline_execution_arn)
retry_pipeline_execution(client_request_token, pipeline_execution_arn, params::Dict{String,<:Any})

Retry the execution of the pipeline.

Arguments

  • client_request_token: A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.
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Main.Sagemaker.searchMethod
search(resource)
search(resource, params::Dict{String,<:Any})

Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp.

Arguments

  • resource: The name of the Amazon SageMaker resource to search for.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "MaxResults": The maximum number of results to return.
  • "NextToken": If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.
  • "SearchExpression": A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.
  • "SortBy": The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.
  • "SortOrder": How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.
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Main.Sagemaker.send_pipeline_execution_step_failureMethod
send_pipeline_execution_step_failure(callback_token)
send_pipeline_execution_step_failure(callback_token, params::Dict{String,<:Any})

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Arguments

  • callback_token: The pipeline generated token from the Amazon SQS queue.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ClientRequestToken": A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
  • "FailureReason": A message describing why the step failed.
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Main.Sagemaker.send_pipeline_execution_step_successMethod
send_pipeline_execution_step_success(callback_token)
send_pipeline_execution_step_success(callback_token, params::Dict{String,<:Any})

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Arguments

  • callback_token: The pipeline generated token from the Amazon SQS queue.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ClientRequestToken": A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
  • "OutputParameters": A list of the output parameters of the callback step.
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Main.Sagemaker.start_monitoring_scheduleMethod
start_monitoring_schedule(monitoring_schedule_name)
start_monitoring_schedule(monitoring_schedule_name, params::Dict{String,<:Any})

Starts a previously stopped monitoring schedule. By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.

Arguments

  • monitoring_schedule_name: The name of the schedule to start.
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Main.Sagemaker.start_notebook_instanceMethod
start_notebook_instance(notebook_instance_name)
start_notebook_instance(notebook_instance_name, params::Dict{String,<:Any})

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

Arguments

  • notebook_instance_name: The name of the notebook instance to start.
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Main.Sagemaker.start_pipeline_executionMethod
start_pipeline_execution(client_request_token, pipeline_name)
start_pipeline_execution(client_request_token, pipeline_name, params::Dict{String,<:Any})

Starts a pipeline execution.

Arguments

  • client_request_token: A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
  • pipeline_name: The name of the pipeline.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "PipelineExecutionDescription": The description of the pipeline execution.
  • "PipelineExecutionDisplayName": The display name of the pipeline execution.
  • "PipelineParameters": Contains a list of pipeline parameters. This list can be empty.
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Main.Sagemaker.stop_auto_mljobMethod
stop_auto_mljob(auto_mljob_name)
stop_auto_mljob(auto_mljob_name, params::Dict{String,<:Any})

A method for forcing the termination of a running job.

Arguments

  • auto_mljob_name: The name of the object you are requesting.
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Main.Sagemaker.stop_compilation_jobMethod
stop_compilation_job(compilation_job_name)
stop_compilation_job(compilation_job_name, params::Dict{String,<:Any})

Stops a model compilation job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal. When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummaryCompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummaryCompilationJobStatus to Stopped.

Arguments

  • compilation_job_name: The name of the model compilation job to stop.
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Main.Sagemaker.stop_edge_packaging_jobMethod
stop_edge_packaging_job(edge_packaging_job_name)
stop_edge_packaging_job(edge_packaging_job_name, params::Dict{String,<:Any})

Request to stop an edge packaging job.

Arguments

  • edge_packaging_job_name: The name of the edge packaging job.
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Main.Sagemaker.stop_hyper_parameter_tuning_jobMethod
stop_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name)
stop_hyper_parameter_tuning_job(hyper_parameter_tuning_job_name, params::Dict{String,<:Any})

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.

Arguments

  • hyper_parameter_tuning_job_name: The name of the tuning job to stop.
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Main.Sagemaker.stop_labeling_jobMethod
stop_labeling_job(labeling_job_name)
stop_labeling_job(labeling_job_name, params::Dict{String,<:Any})

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

Arguments

  • labeling_job_name: The name of the labeling job to stop.
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Main.Sagemaker.stop_monitoring_scheduleMethod
stop_monitoring_schedule(monitoring_schedule_name)
stop_monitoring_schedule(monitoring_schedule_name, params::Dict{String,<:Any})

Stops a previously started monitoring schedule.

Arguments

  • monitoring_schedule_name: The name of the schedule to stop.
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Main.Sagemaker.stop_notebook_instanceMethod
stop_notebook_instance(notebook_instance_name)
stop_notebook_instance(notebook_instance_name, params::Dict{String,<:Any})

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.

Arguments

  • notebook_instance_name: The name of the notebook instance to terminate.
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Main.Sagemaker.stop_pipeline_executionMethod
stop_pipeline_execution(client_request_token, pipeline_execution_arn)
stop_pipeline_execution(client_request_token, pipeline_execution_arn, params::Dict{String,<:Any})

Stops a pipeline execution. Callback Step A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. Lambda Step A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

Arguments

  • client_request_token: A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.
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Main.Sagemaker.stop_processing_jobMethod
stop_processing_job(processing_job_name)
stop_processing_job(processing_job_name, params::Dict{String,<:Any})

Stops a processing job.

Arguments

  • processing_job_name: The name of the processing job to stop.
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Main.Sagemaker.stop_training_jobMethod
stop_training_job(training_job_name)
stop_training_job(training_job_name, params::Dict{String,<:Any})

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost. When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.

Arguments

  • training_job_name: The name of the training job to stop.
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Main.Sagemaker.stop_transform_jobMethod
stop_transform_job(transform_job_name)
stop_transform_job(transform_job_name, params::Dict{String,<:Any})

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.

Arguments

  • transform_job_name: The name of the transform job to stop.
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Main.Sagemaker.update_actionMethod
update_action(action_name)
update_action(action_name, params::Dict{String,<:Any})

Updates an action.

Arguments

  • action_name: The name of the action to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The new description for the action.
  • "Properties": The new list of properties. Overwrites the current property list.
  • "PropertiesToRemove": A list of properties to remove.
  • "Status": The new status for the action.
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Main.Sagemaker.update_app_image_configMethod
update_app_image_config(app_image_config_name)
update_app_image_config(app_image_config_name, params::Dict{String,<:Any})

Updates the properties of an AppImageConfig.

Arguments

  • app_image_config_name: The name of the AppImageConfig to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "KernelGatewayImageConfig": The new KernelGateway app to run on the image.
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Main.Sagemaker.update_artifactMethod
update_artifact(artifact_arn)
update_artifact(artifact_arn, params::Dict{String,<:Any})

Updates an artifact.

Arguments

  • artifact_arn: The Amazon Resource Name (ARN) of the artifact to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ArtifactName": The new name for the artifact.
  • "Properties": The new list of properties. Overwrites the current property list.
  • "PropertiesToRemove": A list of properties to remove.
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Main.Sagemaker.update_code_repositoryMethod
update_code_repository(code_repository_name)
update_code_repository(code_repository_name, params::Dict{String,<:Any})

Updates the specified Git repository with the specified values.

Arguments

  • code_repository_name: The name of the Git repository to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "GitConfig": The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format: {"username": UserName, "password": Password}
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Main.Sagemaker.update_contextMethod
update_context(context_name)
update_context(context_name, params::Dict{String,<:Any})

Updates a context.

Arguments

  • context_name: The name of the context to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The new description for the context.
  • "Properties": The new list of properties. Overwrites the current property list.
  • "PropertiesToRemove": A list of properties to remove.
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Main.Sagemaker.update_device_fleetMethod
update_device_fleet(device_fleet_name, output_config)
update_device_fleet(device_fleet_name, output_config, params::Dict{String,<:Any})

Updates a fleet of devices.

Arguments

  • device_fleet_name: The name of the fleet.
  • output_config: Output configuration for storing sample data collected by the fleet.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": Description of the fleet.
  • "EnableIotRoleAlias": Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
  • "RoleArn": The Amazon Resource Name (ARN) of the device.
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Main.Sagemaker.update_devicesMethod
update_devices(device_fleet_name, devices)
update_devices(device_fleet_name, devices, params::Dict{String,<:Any})

Updates one or more devices in a fleet.

Arguments

  • device_fleet_name: The name of the fleet the devices belong to.
  • devices: List of devices to register with Edge Manager agent.
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Main.Sagemaker.update_domainMethod
update_domain(domain_id)
update_domain(domain_id, params::Dict{String,<:Any})

Updates the default settings for new user profiles in the domain.

Arguments

  • domain_id: The ID of the domain to be updated.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DefaultUserSettings": A collection of settings.
  • "DomainSettingsForUpdate": A collection of DomainSettings configuration values to update.
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Main.Sagemaker.update_endpointMethod
update_endpoint(endpoint_config_name, endpoint_name)
update_endpoint(endpoint_config_name, endpoint_name, params::Dict{String,<:Any})

Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss). When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Arguments

  • endpoint_config_name: The name of the new endpoint configuration.
  • endpoint_name: The name of the endpoint whose configuration you want to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DeploymentConfig": The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
  • "ExcludeRetainedVariantProperties": When you are updating endpoint resources with UpdateEndpointInputRetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don't specify a value for ExcludeAllVariantProperties, no variant properties are overridden.
  • "RetainAllVariantProperties": When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true. To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false. The default is false.
  • "RetainDeploymentConfig": Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).
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Main.Sagemaker.update_endpoint_weights_and_capacitiesMethod
update_endpoint_weights_and_capacities(desired_weights_and_capacities, endpoint_name)
update_endpoint_weights_and_capacities(desired_weights_and_capacities, endpoint_name, params::Dict{String,<:Any})

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

Arguments

  • desired_weights_and_capacities: An object that provides new capacity and weight values for a variant.
  • endpoint_name: The name of an existing Amazon SageMaker endpoint.
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Main.Sagemaker.update_experimentMethod
update_experiment(experiment_name)
update_experiment(experiment_name, params::Dict{String,<:Any})

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Arguments

  • experiment_name: The name of the experiment to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": The description of the experiment.
  • "DisplayName": The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't specified, ExperimentName is displayed.
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Main.Sagemaker.update_imageMethod
update_image(image_name)
update_image(image_name, params::Dict{String,<:Any})

Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.

Arguments

  • image_name: The name of the image to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DeleteProperties": A list of properties to delete. Only the Description and DisplayName properties can be deleted.
  • "Description": The new description for the image.
  • "DisplayName": The new display name for the image.
  • "RoleArn": The new Amazon Resource Name (ARN) for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
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Main.Sagemaker.update_model_packageMethod
update_model_package(model_package_arn)
update_model_package(model_package_arn, params::Dict{String,<:Any})

Updates a versioned model.

Arguments

  • model_package_arn: The Amazon Resource Name (ARN) of the model package.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ApprovalDescription": A description for the approval status of the model.
  • "CustomerMetadataProperties": The metadata properties associated with the model package versions.
  • "CustomerMetadataPropertiesToRemove": The metadata properties associated with the model package versions to remove.
  • "ModelApprovalStatus": The approval status of the model.
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Main.Sagemaker.update_monitoring_scheduleMethod
update_monitoring_schedule(monitoring_schedule_config, monitoring_schedule_name)
update_monitoring_schedule(monitoring_schedule_config, monitoring_schedule_name, params::Dict{String,<:Any})

Updates a previously created schedule.

Arguments

  • monitoring_schedule_config: The configuration object that specifies the monitoring schedule and defines the monitoring job.
  • monitoring_schedule_name: The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
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Main.Sagemaker.update_notebook_instanceMethod
update_notebook_instance(notebook_instance_name)
update_notebook_instance(notebook_instance_name, params::Dict{String,<:Any})

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.

Arguments

  • notebook_instance_name: The name of the notebook instance to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "AcceleratorTypes": A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
  • "AdditionalCodeRepositories": An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
  • "DefaultCodeRepository": The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.
  • "DisassociateAcceleratorTypes": A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
  • "DisassociateAdditionalCodeRepositories": A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
  • "DisassociateDefaultCodeRepository": The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
  • "DisassociateLifecycleConfig": Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.
  • "InstanceType": The Amazon ML compute instance type.
  • "LifecycleConfigName": The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
  • "RoleArn": The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles. To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
  • "RootAccess": Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled. If you set this to Disabled, users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.
  • "VolumeSizeInGB": The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
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Main.Sagemaker.update_notebook_instance_lifecycle_configMethod
update_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name)
update_notebook_instance_lifecycle_config(notebook_instance_lifecycle_config_name, params::Dict{String,<:Any})

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Arguments

  • notebook_instance_lifecycle_config_name: The name of the lifecycle configuration.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "OnCreate": The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
  • "OnStart": The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
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Main.Sagemaker.update_pipelineMethod
update_pipeline(pipeline_name)
update_pipeline(pipeline_name, params::Dict{String,<:Any})

Updates a pipeline.

Arguments

  • pipeline_name: The name of the pipeline to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "PipelineDefinition": The JSON pipeline definition.
  • "PipelineDescription": The description of the pipeline.
  • "PipelineDisplayName": The display name of the pipeline.
  • "RoleArn": The Amazon Resource Name (ARN) that the pipeline uses to execute.
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Main.Sagemaker.update_pipeline_executionMethod
update_pipeline_execution(pipeline_execution_arn)
update_pipeline_execution(pipeline_execution_arn, params::Dict{String,<:Any})

Updates a pipeline execution.

Arguments

  • pipeline_execution_arn: The Amazon Resource Name (ARN) of the pipeline execution.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "PipelineExecutionDescription": The description of the pipeline execution.
  • "PipelineExecutionDisplayName": The display name of the pipeline execution.
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Main.Sagemaker.update_projectMethod
update_project(project_name)
update_project(project_name, params::Dict{String,<:Any})

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model. You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Arguments

  • project_name: The name of the project.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ProjectDescription": The description for the project.
  • "ServiceCatalogProvisioningUpdateDetails": The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
  • "Tags": An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
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Main.Sagemaker.update_training_jobMethod
update_training_job(training_job_name)
update_training_job(training_job_name, params::Dict{String,<:Any})

Update a model training job to request a new Debugger profiling configuration.

Arguments

  • training_job_name: The name of a training job to update the Debugger profiling configuration.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "ProfilerConfig": Configuration information for Debugger system monitoring, framework profiling, and storage paths.
  • "ProfilerRuleConfigurations": Configuration information for Debugger rules for profiling system and framework metrics.
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Main.Sagemaker.update_trialMethod
update_trial(trial_name)
update_trial(trial_name, params::Dict{String,<:Any})

Updates the display name of a trial.

Arguments

  • trial_name: The name of the trial to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DisplayName": The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
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Main.Sagemaker.update_trial_componentMethod
update_trial_component(trial_component_name)
update_trial_component(trial_component_name, params::Dict{String,<:Any})

Updates one or more properties of a trial component.

Arguments

  • trial_component_name: The name of the component to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "DisplayName": The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
  • "EndTime": When the component ended.
  • "InputArtifacts": Replaces all of the component's input artifacts with the specified artifacts.
  • "InputArtifactsToRemove": The input artifacts to remove from the component.
  • "OutputArtifacts": Replaces all of the component's output artifacts with the specified artifacts.
  • "OutputArtifactsToRemove": The output artifacts to remove from the component.
  • "Parameters": Replaces all of the component's hyperparameters with the specified hyperparameters.
  • "ParametersToRemove": The hyperparameters to remove from the component.
  • "StartTime": When the component started.
  • "Status": The new status of the component.
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Main.Sagemaker.update_user_profileMethod
update_user_profile(domain_id, user_profile_name)
update_user_profile(domain_id, user_profile_name, params::Dict{String,<:Any})

Updates a user profile.

Arguments

  • domain_id: The domain ID.
  • user_profile_name: The user profile name.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "UserSettings": A collection of settings.
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Main.Sagemaker.update_workforceMethod
update_workforce(workforce_name)
update_workforce(workforce_name, params::Dict{String,<:Any})

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.

Arguments

  • workforce_name: The name of the private workforce that you want to update. You can find your workforce name by using the operation.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "OidcConfig": Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
  • "SourceIpConfig": A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce. Maximum: Ten CIDR values
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Main.Sagemaker.update_workteamMethod
update_workteam(workteam_name)
update_workteam(workteam_name, params::Dict{String,<:Any})

Updates an existing work team with new member definitions or description.

Arguments

  • workteam_name: The name of the work team to update.

Optional Parameters

Optional parameters can be passed as a params::Dict{String,<:Any}. Valid keys are:

  • "Description": An updated description for the work team.
  • "MemberDefinitions": A list of MemberDefinition objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. You should not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.
  • "NotificationConfiguration": Configures SNS topic notifications for available or expiring work items
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