createNotebookInstance method

Future<CreateNotebookInstanceOutput> createNotebookInstance({
  1. required InstanceType instanceType,
  2. required String notebookInstanceName,
  3. required String roleArn,
  4. List<NotebookInstanceAcceleratorType>? acceleratorTypes,
  5. List<String>? additionalCodeRepositories,
  6. String? defaultCodeRepository,
  7. DirectInternetAccess? directInternetAccess,
  8. InstanceMetadataServiceConfiguration? instanceMetadataServiceConfiguration,
  9. IPAddressType? ipAddressType,
  10. String? kmsKeyId,
  11. String? lifecycleConfigName,
  12. String? platformIdentifier,
  13. RootAccess? rootAccess,
  14. List<String>? securityGroupIds,
  15. String? subnetId,
  16. List<Tag>? tags,
  17. int? volumeSizeInGB,
})

Creates an SageMaker AI 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. SageMaker AI 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.

SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework.

After receiving the request, SageMaker AI does the following:

  1. Creates a network interface in the SageMaker AI VPC.
  2. (Option) If you specified SubnetId, SageMaker AI 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, SageMaker AI 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 SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI 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, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

After SageMaker AI 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 SageMaker AI endpoints, and validate hosted models.

For more information, see How It Works.

May throw ResourceLimitExceeded.

Parameter instanceType : The type of ML compute instance to launch for the notebook instance.

Parameter notebookInstanceName : The name of the new notebook instance.

Parameter roleArn : When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker AI assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker AI can perform these tasks. The policy must allow the SageMaker AI service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker AI Roles.

Parameter acceleratorTypes : This parameter is no longer supported. Elastic Inference (EI) is no longer available.

This parameter was used to specify a list of EI instance types to associate with this notebook instance.

Parameter additionalCodeRepositories : An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

Parameter defaultCodeRepository : A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances.

Parameter directInternetAccess : Sets whether SageMaker AI provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker AI training and endpoint services unless you configure a NAT Gateway in your VPC.

For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

Parameter instanceMetadataServiceConfiguration : Information on the IMDS configuration of the notebook instance

Parameter ipAddressType : The IP address type for the notebook instance. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.

Parameter kmsKeyId : The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker AI uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.

Parameter lifecycleConfigName : The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Parameter platformIdentifier : The platform identifier of the notebook instance runtime environment. The default value is notebook-al2023-v1.

Parameter rootAccess : Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

Parameter securityGroupIds : The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

Parameter subnetId : The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

Parameter tags : An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Parameter volumeSizeInGB : The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

Implementation

Future<CreateNotebookInstanceOutput> createNotebookInstance({
  required InstanceType instanceType,
  required String notebookInstanceName,
  required String roleArn,
  List<NotebookInstanceAcceleratorType>? acceleratorTypes,
  List<String>? additionalCodeRepositories,
  String? defaultCodeRepository,
  DirectInternetAccess? directInternetAccess,
  InstanceMetadataServiceConfiguration? instanceMetadataServiceConfiguration,
  IPAddressType? ipAddressType,
  String? kmsKeyId,
  String? lifecycleConfigName,
  String? platformIdentifier,
  RootAccess? rootAccess,
  List<String>? securityGroupIds,
  String? subnetId,
  List<Tag>? tags,
  int? volumeSizeInGB,
}) async {
  _s.validateNumRange(
    'volumeSizeInGB',
    volumeSizeInGB,
    5,
    16384,
  );
  final headers = <String, String>{
    'Content-Type': 'application/x-amz-json-1.1',
    'X-Amz-Target': 'SageMaker.CreateNotebookInstance'
  };
  final jsonResponse = await _protocol.send(
    method: 'POST',
    requestUri: '/',
    exceptionFnMap: _exceptionFns,
    // TODO queryParams
    headers: headers,
    payload: {
      'InstanceType': instanceType.value,
      'NotebookInstanceName': notebookInstanceName,
      'RoleArn': roleArn,
      if (acceleratorTypes != null)
        'AcceleratorTypes': acceleratorTypes.map((e) => e.value).toList(),
      if (additionalCodeRepositories != null)
        'AdditionalCodeRepositories': additionalCodeRepositories,
      if (defaultCodeRepository != null)
        'DefaultCodeRepository': defaultCodeRepository,
      if (directInternetAccess != null)
        'DirectInternetAccess': directInternetAccess.value,
      if (instanceMetadataServiceConfiguration != null)
        'InstanceMetadataServiceConfiguration':
            instanceMetadataServiceConfiguration,
      if (ipAddressType != null) 'IpAddressType': ipAddressType.value,
      if (kmsKeyId != null) 'KmsKeyId': kmsKeyId,
      if (lifecycleConfigName != null)
        'LifecycleConfigName': lifecycleConfigName,
      if (platformIdentifier != null)
        'PlatformIdentifier': platformIdentifier,
      if (rootAccess != null) 'RootAccess': rootAccess.value,
      if (securityGroupIds != null) 'SecurityGroupIds': securityGroupIds,
      if (subnetId != null) 'SubnetId': subnetId,
      if (tags != null) 'Tags': tags,
      if (volumeSizeInGB != null) 'VolumeSizeInGB': volumeSizeInGB,
    },
  );

  return CreateNotebookInstanceOutput.fromJson(jsonResponse.body);
}