sagemaker

class pulumi_aws.sagemaker.Model(resource_name, opts=None, containers=None, enable_network_isolation=None, execution_role_arn=None, name=None, primary_container=None, tags=None, vpc_config=None, __name__=None, __opts__=None)

Provides a SageMaker model resource.

Parameters:
  • resource_name (str) – The name of the resource.
  • opts (pulumi.ResourceOptions) – Options for the resource.
  • containers (pulumi.Input[list]) – Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.
  • enable_network_isolation (pulumi.Input[bool]) – Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
  • execution_role_arn (pulumi.Input[str]) – A role that SageMaker can assume to access model artifacts and docker images for deployment.
  • name (pulumi.Input[str]) – The name of the model (must be unique). If omitted, Terraform will assign a random, unique name.
  • primary_container (pulumi.Input[dict]) – The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.
  • tags (pulumi.Input[dict]) – A mapping of tags to assign to the resource.
  • vpc_config (pulumi.Input[dict]) – Specifies the VPC that you want your model to connect to. VpcConfig is used in hosting services and in batch transform.
arn = None

The Amazon Resource Name (ARN) assigned by AWS to this model.

containers = None

Specifies containers in the inference pipeline. If not specified, the primary_container argument is required. Fields are documented below.

enable_network_isolation = None

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

execution_role_arn = None

A role that SageMaker can assume to access model artifacts and docker images for deployment.

name = None

The name of the model (must be unique). If omitted, Terraform will assign a random, unique name.

primary_container = None

The primary docker image containing inference code that is used when the model is deployed for predictions. If not specified, the container argument is required. Fields are documented below.

tags = None

A mapping of tags to assign to the resource.

vpc_config = None

Specifies the VPC that you want your model to connect to. VpcConfig is used in hosting services and in batch transform.

translate_output_property(prop)

Provides subclasses of Resource an opportunity to translate names of output properties into a format of their choosing before writing those properties to the resource object.

Parameters:prop (str) – A property name.
Returns:A potentially transformed property name.
Return type:str
translate_input_property(prop)

Provides subclasses of Resource an opportunity to translate names of input properties into a format of their choosing before sending those properties to the Pulumi engine.

Parameters:prop (str) – A property name.
Returns:A potentially transformed property name.
Return type:str
class pulumi_aws.sagemaker.NotebookInstance(resource_name, opts=None, instance_type=None, kms_key_id=None, name=None, role_arn=None, security_groups=None, subnet_id=None, tags=None, __name__=None, __opts__=None)

Provides a Sagemaker Notebook Instance resource.

Parameters:
  • resource_name (str) – The name of the resource.
  • opts (pulumi.ResourceOptions) – Options for the resource.
  • instance_type (pulumi.Input[str]) – The name of ML compute instance type.
  • kms_key_id (pulumi.Input[str]) – The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
  • name (pulumi.Input[str]) – The name of the notebook instance (must be unique).
  • role_arn (pulumi.Input[str]) – The ARN of the IAM role to be used by the notebook instance which allows SageMaker to call other services on your behalf.
  • security_groups (pulumi.Input[list]) – The associated security groups.
  • subnet_id (pulumi.Input[str]) – The VPC subnet ID.
  • tags (pulumi.Input[dict]) – A mapping of tags to assign to the resource.
arn = None

The Amazon Resource Name (ARN) assigned by AWS to this notebook instance.

instance_type = None

The name of ML compute instance type.

kms_key_id = None

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

name = None

The name of the notebook instance (must be unique).

role_arn = None

The ARN of the IAM role to be used by the notebook instance which allows SageMaker to call other services on your behalf.

security_groups = None

The associated security groups.

subnet_id = None

The VPC subnet ID.

tags = None

A mapping of tags to assign to the resource.

translate_output_property(prop)

Provides subclasses of Resource an opportunity to translate names of output properties into a format of their choosing before writing those properties to the resource object.

Parameters:prop (str) – A property name.
Returns:A potentially transformed property name.
Return type:str
translate_input_property(prop)

Provides subclasses of Resource an opportunity to translate names of input properties into a format of their choosing before sending those properties to the Pulumi engine.

Parameters:prop (str) – A property name.
Returns:A potentially transformed property name.
Return type:str