createNotebookInstance method

Future<CreateNotebookInstanceOutput> createNotebookInstance({ required InstanceType instanceType, required String notebookInstanceName, required String roleArn, List<NotebookInstanceAcceleratorType>? acceleratorTypes, List<String>? additionalCodeRepositories, String? defaultCodeRepository, DirectInternetAccess? directInternetAccess, String? kmsKeyId, String? lifecycleConfigName, RootAccess? rootAccess, List<String>? securityGroupIds, String? subnetId, List<Tag>? tags, int? volumeSizeInGB, })

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). You can't change the name of a notebook instance after you create it.

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.

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 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.

Parameter acceleratorTypes : A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

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 AWS 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 Amazon SageMaker 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 AWS 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 Amazon SageMaker Notebook Instances.

Parameter 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 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 kmsKeyId : The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker 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 AWS 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 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 AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS 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,
  String? kmsKeyId,
  String? lifecycleConfigName,
  RootAccess? rootAccess,
  List<String>? securityGroupIds,
  String? subnetId,
  List<Tag>? tags,
  int? volumeSizeInGB,
}) async {
  ArgumentError.checkNotNull(instanceType, 'instanceType');
  ArgumentError.checkNotNull(notebookInstanceName, 'notebookInstanceName');
  _s.validateStringLength(
    'notebookInstanceName',
    notebookInstanceName,
    0,
    63,
    isRequired: true,
  );
  ArgumentError.checkNotNull(roleArn, 'roleArn');
  _s.validateStringLength(
    'roleArn',
    roleArn,
    20,
    2048,
    isRequired: true,
  );
  _s.validateStringLength(
    'defaultCodeRepository',
    defaultCodeRepository,
    1,
    1024,
  );
  _s.validateStringLength(
    'kmsKeyId',
    kmsKeyId,
    0,
    2048,
  );
  _s.validateStringLength(
    'lifecycleConfigName',
    lifecycleConfigName,
    0,
    63,
  );
  _s.validateStringLength(
    'subnetId',
    subnetId,
    0,
    32,
  );
  _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.toValue(),
      'NotebookInstanceName': notebookInstanceName,
      'RoleArn': roleArn,
      if (acceleratorTypes != null)
        'AcceleratorTypes': acceleratorTypes.map((e) => e.toValue()).toList(),
      if (additionalCodeRepositories != null)
        'AdditionalCodeRepositories': additionalCodeRepositories,
      if (defaultCodeRepository != null)
        'DefaultCodeRepository': defaultCodeRepository,
      if (directInternetAccess != null)
        'DirectInternetAccess': directInternetAccess.toValue(),
      if (kmsKeyId != null) 'KmsKeyId': kmsKeyId,
      if (lifecycleConfigName != null)
        'LifecycleConfigName': lifecycleConfigName,
      if (rootAccess != null) 'RootAccess': rootAccess.toValue(),
      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);
}