[ aws . sagemaker ]

create-app

Description

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.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  create-app
--domain-id <value>
--user-profile-name <value>
--app-type <value>
--app-name <value>
[--tags <value>]
[--resource-spec <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--domain-id (string)

The domain ID.

--user-profile-name (string)

The user profile name.

--app-type (string)

The type of app. Supported apps are JupyterServer and KernelGateway . TensorBoard is not supported.

Possible values:

• JupyterServer

• KernelGateway

• TensorBoard

• RStudioServerPro

• RSessionGateway

--app-name (string)

The name of the app.

--tags (list)

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

(structure)

A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

Key -> (string)

The tag key. Tag keys must be unique per resource.

Value -> (string)

The tag value.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--resource-spec (structure)

The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

Note

The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

SageMakerImageArn -> (string)

The ARN of the SageMaker image that the image version belongs to.

SageMakerImageVersionArn -> (string)

The ARN of the image version created on the instance.

InstanceType -> (string)

The instance type that the image version runs on.

Note

JupyterServer apps only support the system value.

For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

LifecycleConfigArn -> (string)

The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

Shorthand Syntax:

SageMakerImageArn=string,SageMakerImageVersionArn=string,InstanceType=string,LifecycleConfigArn=string

JSON Syntax:

{
  "SageMakerImageArn": "string",
  "SageMakerImageVersionArn": "string",
  "InstanceType": "system"|"ml.t3.micro"|"ml.t3.small"|"ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.8xlarge"|"ml.m5.12xlarge"|"ml.m5.16xlarge"|"ml.m5.24xlarge"|"ml.m5d.large"|"ml.m5d.xlarge"|"ml.m5d.2xlarge"|"ml.m5d.4xlarge"|"ml.m5d.8xlarge"|"ml.m5d.12xlarge"|"ml.m5d.16xlarge"|"ml.m5d.24xlarge"|"ml.c5.large"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.12xlarge"|"ml.c5.18xlarge"|"ml.c5.24xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.p3dn.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge",
  "LifecycleConfigArn": "string"
}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

See ‘aws help’ for descriptions of global parameters.

Output

AppArn -> (string)

The Amazon Resource Name (ARN) of the app.