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Class: Aws::SageMaker::Types::CreateNotebookInstanceInput

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreateNotebookInstanceInput as input to an Aws::Client method, you can use a vanilla Hash:

{
  notebook_instance_name: "NotebookInstanceName", # required
  instance_type: "ml.t2.medium", # required, accepts 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.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
  subnet_id: "SubnetId",
  security_group_ids: ["SecurityGroupId"],
  role_arn: "RoleArn", # required
  kms_key_id: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  lifecycle_config_name: "NotebookInstanceLifecycleConfigName",
  direct_internet_access: "Enabled", # accepts Enabled, Disabled
  volume_size_in_gb: 1,
  accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge
  default_code_repository: "CodeRepositoryNameOrUrl",
  additional_code_repositories: ["CodeRepositoryNameOrUrl"],
  root_access: "Enabled", # accepts Enabled, Disabled
}

Instance Attribute Summary collapse

Instance Attribute Details

#accelerator_typesArray<String>

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.

Returns:

  • (Array<String>)

    A list of Elastic Inference (EI) instance types to associate with this notebook instance.

#additional_code_repositoriesArray<String>

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.

Returns:

  • (Array<String>)

    An array of up to three Git repositories to associate with the notebook instance.

#default_code_repositoryString

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.

Returns:

  • (String)

    A Git repository to associate with the notebook instance as its default code repository.

#direct_internet_accessString

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.

Returns:

  • (String)

    Sets whether Amazon SageMaker provides internet access to the notebook instance.

#instance_typeString

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.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

Returns:

  • (String)

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

#kms_key_idString

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.

Returns:

  • (String)

    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.

#lifecycle_config_nameString

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.

Returns:

  • (String)

    The name of a lifecycle configuration to associate with the notebook instance.

#notebook_instance_nameString

The name of the new notebook instance.

Returns:

  • (String)

    The name of the new notebook instance.

#role_arnString

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.

Returns:

  • (String)

    When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf.

#root_accessString

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

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

Returns:

  • (String)

    Whether root access is enabled or disabled for users of the notebook instance.

#security_group_idsArray<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.

Returns:

  • (Array<String>)

    The VPC security group IDs, in the form sg-xxxxxxxx.

#subnet_idString

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

Returns:

  • (String)

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

#tagsArray<Types::Tag>

A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.

Returns:

  • (Array<Types::Tag>)

    A list of tags to associate with the notebook instance.

#volume_size_in_gbInteger

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

Returns:

  • (Integer)

    The size, in GB, of the ML storage volume to attach to the notebook instance.