create-app-image-config¶
Description¶
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.
See also: AWS API Documentation
See ‘aws help’ for descriptions of global parameters.
Synopsis¶
create-app-image-config
--app-image-config-name <value>
[--tags <value>]
[--kernel-gateway-image-config <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
Options¶
--app-image-config-name (string)
The name of the AppImageConfig. Must be unique to your account.
--tags (list)
A list of tags to apply to the AppImageConfig.
(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"
}
...
]
--kernel-gateway-image-config (structure)
The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
KernelSpecs -> (list)
The specification of the Jupyter kernels in the image.
(structure)
The specification of a Jupyter kernel.
Name -> (string)
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName -> (string)
The display name of the kernel.
FileSystemConfig -> (structure)
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath -> (string)
The path within the image to mount the user’s EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid -> (integer)
The default POSIX user ID (UID). If not specified, defaults to
1000.DefaultGid -> (integer)
The default POSIX group ID (GID). If not specified, defaults to
100.
Shorthand Syntax:
KernelSpecs=[{Name=string,DisplayName=string},{Name=string,DisplayName=string}],FileSystemConfig={MountPath=string,DefaultUid=integer,DefaultGid=integer}
JSON Syntax:
{
"KernelSpecs": [
{
"Name": "string",
"DisplayName": "string"
}
...
],
"FileSystemConfig": {
"MountPath": "string",
"DefaultUid": integer,
"DefaultGid": integer
}
}
--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.