create_notebook_instance async ¶

async def create_notebook_instance(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    notebook_instance_name: Optional[
        "capo_sagemaker.types.notebook_instance_name.NotebookInstanceName"
    ] = None,
    instance_type: Optional[
        "capo_sagemaker.types.instance_type.InstanceType"
    ] = None,
    subnet_id: Optional["capo_sagemaker.types.subnet_id.SubnetId"] = None,
    security_group_ids: Optional[
        "capo_sagemaker.types.security_group_ids.SecurityGroupIds"
    ] = None,
    ip_address_type: Optional[
        "capo_sagemaker.types.ip_address_type.IPAddressType"
    ] = None,
    role_arn: Optional["capo_sagemaker.types.role_arn.RoleArn"] = None,
    kms_key_id: Optional["capo_sagemaker.types.kms_key_id.KmsKeyId"] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
    lifecycle_config_name: Optional[
        "capo_sagemaker.types.notebook_instance_lifecycle_config_name.NotebookInstanceLifecycleConfigName"
    ] = None,
    direct_internet_access: Optional[
        "capo_sagemaker.types.direct_internet_access.DirectInternetAccess"
    ] = None,
    volume_size_in_gb: Optional[
        "capo_sagemaker.types.notebook_instance_volume_size_in_gb.NotebookInstanceVolumeSizeInGB"
    ] = None,
    accelerator_types: Optional[
        "capo_sagemaker.types.notebook_instance_accelerator_types.NotebookInstanceAcceleratorTypes"
    ] = None,
    default_code_repository: Optional[
        "capo_sagemaker.types.code_repository_name_or_url.CodeRepositoryNameOrUrl"
    ] = None,
    additional_code_repositories: Optional[
        "capo_sagemaker.types.additional_code_repository_names_or_urls.AdditionalCodeRepositoryNamesOrUrls"
    ] = None,
    root_access: Optional["capo_sagemaker.types.root_access.RootAccess"] = None,
    platform_identifier: Optional[
        "capo_sagemaker.types.platform_identifier.PlatformIdentifier"
    ] = None,
    instance_metadata_service_configuration: Optional[
        "capo_sagemaker.types.instance_metadata_service_configuration.InstanceMetadataServiceConfiguration"
    ] = None,
) -> "capo_sagemaker.types.create_notebook_instance_output.CreateNotebookInstanceOutput":
    """<p>Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. </p> <p>In a <code>CreateNotebookInstance</code> 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. </p> <p>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. </p> <p>After receiving the request, SageMaker AI does the following:</p> <ol> <li> <p>Creates a network interface in the SageMaker AI VPC.</p> </li> <li> <p>(Option) If you specified <code>SubnetId</code>, 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.</p> </li> <li> <p>Launches an EC2 instance of the type specified in the request in the SageMaker AI VPC. If you specified <code>SubnetId</code> 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.</p> </li> </ol> <p>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.</p> <p>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. </p> <p>For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works.html">How It Works</a>. </p>

    Args:
        notebook_instance_name: <p>The name of the new notebook instance.</p>
        instance_type: <p>The type of ML compute instance to launch for the notebook instance.</p>
        subnet_id: <p>The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance. </p>
        security_group_ids: <p>The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet. </p>
        ip_address_type: <p>The IP address type for the notebook instance. Specify <code>ipv4</code> for IPv4-only connectivity or <code>dualstack</code> for both IPv4 and IPv6 connectivity. When you specify <code>dualstack</code>, the subnet must support IPv6 CIDR blocks. If not specified, defaults to <code>ipv4</code>.</p>
        role_arn: <p> 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html">SageMaker AI Roles</a>. </p> <note> <p>To be able to pass this role to SageMaker AI, the caller of this API must have the <code>iam:PassRole</code> permission.</p> </note>
        kms_key_id: <p>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 <a href="https://docs.aws.amazon.com/kms/latest/developerguide/enabling-keys.html">Enabling and Disabling Keys</a> in the <i>Amazon Web Services Key Management Service Developer Guide</i>.</p>
        tags: <p>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 <a href="https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html">Tagging Amazon Web Services Resources</a>.</p>
        lifecycle_config_name: <p>The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html">Step 2.1: (Optional) Customize a Notebook Instance</a>.</p>
        direct_internet_access: <p>Sets whether SageMaker AI provides internet access to the notebook instance. If you set this to <code>Disabled</code> 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.</p> <p>For more information, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/appendix-additional-considerations.html#appendix-notebook-and-internet-access">Notebook Instances Are Internet-Enabled by Default</a>. You can set the value of this parameter to <code>Disabled</code> only if you set a value for the <code>SubnetId</code> parameter.</p>
        volume_size_in_gb: <p>The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.</p>
        accelerator_types: <p>This parameter is no longer supported. Elastic Inference (EI) is no longer available.</p> <p>This parameter was used to specify a list of EI instance types to associate with this notebook instance.</p>
        default_code_repository: <p>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 <a href="https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html">Amazon Web Services CodeCommit</a> 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html">Associating Git Repositories with SageMaker AI Notebook Instances</a>.</p>
        additional_code_repositories: <p>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 <a href="https://docs.aws.amazon.com/codecommit/latest/userguide/welcome.html">Amazon Web Services CodeCommit</a> 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-git-repo.html">Associating Git Repositories with SageMaker AI Notebook Instances</a>.</p>
        root_access: <p>Whether root access is enabled or disabled for users of the notebook instance. The default value is <code>Enabled</code>.</p> <note> <p>Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.</p> </note>
        platform_identifier: <p>The platform identifier of the notebook instance runtime environment. The default value is <code>notebook-al2023-v1</code>.</p>
        instance_metadata_service_configuration: <p>Information on the IMDS configuration of the notebook instance</p>

    Raises:
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.create_notebook_instance_input.CreateNotebookInstanceInput]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.create_notebook_instance_output.CreateNotebookInstanceOutput"
    ]:
        import capo_sagemaker._operations.sage_maker.create_notebook_instance

        (
            output,
            http_response,
        ) = await capo_sagemaker._operations.sage_maker.create_notebook_instance.async_create_notebook_instance(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateNotebookInstance")
    )
    input_: capo_sagemaker.types.create_notebook_instance_input.CreateNotebookInstanceInput = {}
    if notebook_instance_name is not None:
        input_["notebook_instance_name"] = notebook_instance_name
    if instance_type is not None:
        input_["instance_type"] = instance_type
    if subnet_id is not None:
        input_["subnet_id"] = subnet_id
    if security_group_ids is not None:
        input_["security_group_ids"] = security_group_ids
    if ip_address_type is not None:
        input_["ip_address_type"] = ip_address_type
    if role_arn is not None:
        input_["role_arn"] = role_arn
    if kms_key_id is not None:
        input_["kms_key_id"] = kms_key_id
    if tags is not None:
        input_["tags"] = tags
    if lifecycle_config_name is not None:
        input_["lifecycle_config_name"] = lifecycle_config_name
    if direct_internet_access is not None:
        input_["direct_internet_access"] = direct_internet_access
    if volume_size_in_gb is not None:
        input_["volume_size_in_gb"] = volume_size_in_gb
    if accelerator_types is not None:
        input_["accelerator_types"] = accelerator_types
    if default_code_repository is not None:
        input_["default_code_repository"] = default_code_repository
    if additional_code_repositories is not None:
        input_["additional_code_repositories"] = additional_code_repositories
    if root_access is not None:
        input_["root_access"] = root_access
    if platform_identifier is not None:
        input_["platform_identifier"] = platform_identifier
    if instance_metadata_service_configuration is not None:
        input_["instance_metadata_service_configuration"] = (
            instance_metadata_service_configuration
        )

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output