start-notebook-instance¶
Description¶
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.
See also: AWS API Documentation
See ‘aws help’ for descriptions of global parameters.
Synopsis¶
start-notebook-instance
--notebook-instance-name <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
Options¶
--notebook-instance-name (string)
The name of the notebook instance to start.
--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.
See ‘aws help’ for descriptions of global parameters.
Output¶
None