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StartNotebookInstanceCommand
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker AI sets the notebook instance status to InService
. A notebook instance's status must be InService
before you can connect to your Jupyter notebook.
Example Syntax
Use a bare-bones client and the command you need to make an API call.
import { SageMakerClient, StartNotebookInstanceCommand } from "@aws-sdk/client-sagemaker"; // ES Modules import
// const { SageMakerClient, StartNotebookInstanceCommand } = require("@aws-sdk/client-sagemaker"); // CommonJS import
const client = new SageMakerClient(config);
const input = { // StartNotebookInstanceInput
NotebookInstanceName: "STRING_VALUE", // required
};
const command = new StartNotebookInstanceCommand(input);
const response = await client.send(command);
// {};
StartNotebookInstanceCommand Input
See StartNotebookInstanceCommandInput for more details
Parameter | Type | Description |
---|
Parameter | Type | Description |
---|---|---|
NotebookInstanceName Required | string | undefined | The name of the notebook instance to start. |
StartNotebookInstanceCommand Output
See StartNotebookInstanceCommandOutput for details
Parameter | Type | Description |
---|
Parameter | Type | Description |
---|---|---|
$metadata Required | ResponseMetadata | Metadata pertaining to this request. |
Throws
Name | Fault | Details |
---|
Name | Fault | Details |
---|---|---|
ResourceLimitExceeded | client | You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. |
SageMakerServiceException | Base exception class for all service exceptions from SageMaker service. |