AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon SageMaker Service CreateNotebookInstance API operation.

Syntax

New-SMNotebookInstance
-NotebookInstanceName <String>
-DirectInternetAccess <DirectInternetAccess>
-InstanceType <InstanceType>
-KmsKeyId <String>
-LifecycleConfigName <String>
-RoleArn <String>
-SecurityGroupId <String[]>
-SubnetId <String>
-Tag <Tag[]>
-Force <SwitchParameter>

Description

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:
  1. Creates a network interface in the Amazon SageMaker VPC.
  2. (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.
  3. 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). 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.

Parameters

-DirectInternetAccess <DirectInternetAccess>
Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.For more information, see appendix-notebook-and-internet-access. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
Required?False
Position?Named
Accept pipeline input?False
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?False
-InstanceType <InstanceType>
The type of ML compute instance to launch for the notebook instance.
Required?False
Position?Named
Accept pipeline input?False
-KmsKeyId <String>
If you provide a AWS KMS key ID, Amazon SageMaker uses it to encrypt data at rest on the ML storage volume that is attached to your notebook instance.
Required?False
Position?Named
Accept pipeline input?False
-LifecycleConfigName <String>
The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see notebook-lifecycle-config.
Required?False
Position?Named
Accept pipeline input?False
-NotebookInstanceName <String>
The name of the new notebook instance.
Required?False
Position?1
Accept pipeline input?True (ByValue, )
-RoleArn <String>
When you send any requests to AWS 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.
Required?False
Position?Named
Accept pipeline input?False
-SecurityGroupId <String[]>
The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
Required?False
Position?Named
Accept pipeline input?False
-SubnetId <String>
The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.
Required?False
Position?Named
Accept pipeline input?False
-Tag <Tag[]>
A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.
Required?False
Position?Named
Accept pipeline input?False

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-ProfileLocation <String>

Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)

If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials. Note that the encrypted credential file is not supported on all platforms. It will be skipped when searching for profiles on Windows Nano Server, Mac, and Linux platforms.

If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.

As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.

Required? False
Position? Named
Accept pipeline input? False
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required? False
Position? Named
Accept pipeline input? False
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required? False
Position? Named
Accept pipeline input? False
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-Region <String>
The system name of the AWS region in which the operation should be invoked. For example, us-east-1, eu-west-1 etc.
Required? False
Position? Named
Accept pipeline input? False
-EndpointUrl <String>

The endpoint to make the call against.

Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.

Required? False
Position? Named
Accept pipeline input? False

Inputs

You can pipe a String object to this cmdlet for the NotebookInstanceName parameter.

Outputs

This cmdlet returns a String object. The service call response (type Amazon.SageMaker.Model.CreateNotebookInstanceResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.

Supported Version

AWS Tools for PowerShell: 2.x.y.z