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Class: Aws::SageMaker::Types::CreateNotebookInstanceInput
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::CreateNotebookInstanceInput
- Defined in:
- (unknown)
Overview
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, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
default_code_repository: "CodeRepositoryNameOrUrl",
additional_code_repositories: ["CodeRepositoryNameOrUrl"],
root_access: "Enabled", # accepts Enabled, Disabled
}
Instance Attribute Summary collapse
-
#accelerator_types ⇒ Array<String>
A list of Elastic Inference (EI) instance types to associate with this notebook instance.
-
#additional_code_repositories ⇒ Array<String>
An array of up to three Git repositories to associate with the notebook instance.
-
#default_code_repository ⇒ String
A Git repository to associate with the notebook instance as its default code repository.
-
#direct_internet_access ⇒ String
Sets whether Amazon SageMaker provides internet access to the notebook instance.
-
#instance_type ⇒ String
The type of ML compute instance to launch for the notebook instance.
-
#kms_key_id ⇒ 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_name ⇒ String
The name of a lifecycle configuration to associate with the notebook instance.
-
#notebook_instance_name ⇒ String
The name of the new notebook instance.
-
#role_arn ⇒ 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_access ⇒ String
Whether root access is enabled or disabled for users of the notebook instance.
-
#security_group_ids ⇒ Array<String>
The VPC security group IDs, in the form sg-xxxxxxxx.
-
#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.
-
#tags ⇒ Array<Types::Tag>
A list of tags to associate with the notebook instance.
-
#volume_size_in_gb ⇒ Integer
The size, in GB, of the ML storage volume to attach to the notebook instance.
Instance Attribute Details
#accelerator_types ⇒ Array<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.
#additional_code_repositories ⇒ Array<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.
#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 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.
#direct_internet_access ⇒ String
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.
#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.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
#kms_key_id ⇒ 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. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.
#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.
#notebook_instance_name ⇒ String
The name of the new notebook instance.
#role_arn ⇒ String
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.
iam:PassRole
permission.
#root_access ⇒ String
Whether root access is enabled or disabled for users of the notebook
instance. The default value is Enabled
.
Possible values:
- Enabled
- Disabled
#security_group_ids ⇒ Array<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.
#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.
#tags ⇒ Array<Types::Tag>
A list of tags to associate with the notebook instance. You can add tags
later by using the CreateTags
API.
#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.