[ aws . sagemaker ]

create-notebook-instance

Description

Creates an 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. 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.

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

After receiving the request, SageMaker does the following:

• Creates a network interface in the SageMaker VPC.

• (Option) If you specified SubnetId , 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, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

• Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, 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, SageMaker returns its Amazon Resource Name (ARN). You can’t change the name of a notebook instance after you create it.

After 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 SageMaker endpoints, and validate hosted models.

For more information, see How It Works .

See also: AWS API Documentation

Synopsis

  create-notebook-instance
--notebook-instance-name <value>
--instance-type <value>
[--subnet-id <value>]
[--security-group-ids <value>]
--role-arn <value>
[--kms-key-id <value>]
[--tags <value>]
[--lifecycle-config-name <value>]
[--direct-internet-access <value>]
[--volume-size-in-gb <value>]
[--accelerator-types <value>]
[--default-code-repository <value>]
[--additional-code-repositories <value>]
[--root-access <value>]
[--platform-identifier <value>]
[--instance-metadata-service-configuration <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--notebook-instance-name (string)

The name of the new notebook instance.

--instance-type (string)

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

Possible values:

• ml.t2.medium

• ml.t2.large

• ml.t2.xlarge

• ml.t2.2xlarge

• ml.t3.medium

• ml.t3.large

• ml.t3.xlarge

• ml.t3.2xlarge

• ml.m4.xlarge

• ml.m4.2xlarge

• ml.m4.4xlarge

• ml.m4.10xlarge

• ml.m4.16xlarge

• ml.m5.xlarge

• ml.m5.2xlarge

• ml.m5.4xlarge

• ml.m5.12xlarge

• ml.m5.24xlarge

• ml.m5d.large

• ml.m5d.xlarge

• ml.m5d.2xlarge

• ml.m5d.4xlarge

• ml.m5d.8xlarge

• ml.m5d.12xlarge

• ml.m5d.16xlarge

• ml.m5d.24xlarge

• ml.c4.xlarge

• ml.c4.2xlarge

• ml.c4.4xlarge

• ml.c4.8xlarge

• ml.c5.xlarge

• ml.c5.2xlarge

• ml.c5.4xlarge

• ml.c5.9xlarge

• ml.c5.18xlarge

• ml.c5d.xlarge

• ml.c5d.2xlarge

• ml.c5d.4xlarge

• ml.c5d.9xlarge

• ml.c5d.18xlarge

• ml.p2.xlarge

• ml.p2.8xlarge

• ml.p2.16xlarge

• ml.p3.2xlarge

• ml.p3.8xlarge

• ml.p3.16xlarge

• ml.p3dn.24xlarge

• ml.g4dn.xlarge

• ml.g4dn.2xlarge

• ml.g4dn.4xlarge

• ml.g4dn.8xlarge

• ml.g4dn.12xlarge

• ml.g4dn.16xlarge

• ml.r5.large

• ml.r5.xlarge

• ml.r5.2xlarge

• ml.r5.4xlarge

• ml.r5.8xlarge

• ml.r5.12xlarge

• ml.r5.16xlarge

• ml.r5.24xlarge

• ml.g5.xlarge

• ml.g5.2xlarge

• ml.g5.4xlarge

• ml.g5.8xlarge

• ml.g5.16xlarge

• ml.g5.12xlarge

• ml.g5.24xlarge

• ml.g5.48xlarge

--subnet-id (string)

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

--security-group-ids (list)

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

(string)

Syntax:

"string" "string" ...

--role-arn (string)

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

Note

To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

--kms-key-id (string)

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that 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 Amazon Web Services Key Management Service Developer Guide .

--tags (list)

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 .

(structure)

A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .

Key -> (string)

The tag key. Tag keys must be unique per resource.

Value -> (string)

The tag value.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--lifecycle-config-name (string)

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 .

--direct-internet-access (string)

Sets whether SageMaker 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 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.

Possible values:

• Enabled

• Disabled

--volume-size-in-gb (integer)

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

--accelerator-types (list)

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 .

(string)

Syntax:

"string" "string" ...

Where valid values are:
  ml.eia1.medium
  ml.eia1.large
  ml.eia1.xlarge
  ml.eia2.medium
  ml.eia2.large
  ml.eia2.xlarge

--default-code-repository (string)

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 Notebook Instances .

--additional-code-repositories (list)

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 Notebook Instances .

(string)

Syntax:

"string" "string" ...

--root-access (string)

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

Note

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.

Possible values:

• Enabled

• Disabled

--platform-identifier (string)

The platform identifier of the notebook instance runtime environment.

--instance-metadata-service-configuration (structure)

Information on the IMDS configuration of the notebook instance

MinimumInstanceMetadataServiceVersion -> (string)

Indicates the minimum IMDS version that the notebook instance supports. When passed as part of CreateNotebookInstance , if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part of UpdateNotebookInstance , there is no default.

Shorthand Syntax:

MinimumInstanceMetadataServiceVersion=string

JSON Syntax:

{
  "MinimumInstanceMetadataServiceVersion": "string"
}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination.

--output (string)

The formatting style for command output.

• json

• text

• table

• yaml

• yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

• on

• off

• auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

• base64

• raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

NotebookInstanceArn -> (string)

The Amazon Resource Name (ARN) of the notebook instance.