pipelines/components/aws/sagemaker/model

SageMaker Hosting Services - Create Model Kubeflow Pipeline component

Summary

Component to create a model in SageMaker from a Kubeflow Pipelines workflow.

Details

Deploying a model using Amazon SageMaker hosting services is a three-step process:

1. Create a model in Amazon SageMaker - Specify the S3 path where model artifacts are stored and Docker registry path for the image that contains the inference code
2. Create an endpoint configuration for an HTTPS endpoint - Specify the name of model in production variants and the type of instance that you want Amazon SageMaker to launch to host the model.
3. Create an HTTPS endpoint - Launch the ML compute instances and deploy the model as specified in the endpoint configuration

This component handles Step 1. Step 2 and 3 can be done using the deploy component for AWS SageMaker.

Intended Use

Create a model in Amazon SageMaker to be used for creating an endpoint in hosting services or run a batch transform job.

Runtime Arguments

Argument	Description	Optional (in pipeline definition)	Optional (in UI)	Data type	Accepted values	Default
region	The region where the model is created	No	No	String
endpoint_url	The endpoint URL for the private link VPC endpoint	Yes	String
assume_role	The ARN of an IAM role to assume when connecting to SageMaker	Yes	String
tags	Key-value pairs to tag the model created in AWS	Yes	Yes	Dict		{}
role	The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs	No	No	String
network_isolation	Isolates the model container. No inbound or outbound network calls can be made to or from the model container	Yes	Yes	Boolean		True
model_name	The name of the new model	No	No	String
vpc_subnets	The ID of the subnets in the VPC to which you want to connect your training job or model	No if vpc_security_group_ids is specified	No if vpc_security_group_ids is specified	Array of Strings
vpc_security_group_ids	The security groups for the VPC that is specified in the vpc_subnets field	No if vpc_subnets is specified	No if vpc_subnets is specified	Array of Strings

The docker image containing inference code, associated artifacts, and environment map that the inference code uses when the model is deployed for predictions make up the ContainerDefinition object in CreateModel API. The following parameters(except secondary_containers) describes the container, as part of model definition:

Argument	Description	Optional (in pipeline definition)	Optional (in UI)	Data type	Accepted values	Default
container_host_name	When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics	Yes	Yes	String	Length Constraints: Maximum length of 63. Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9])*
environment	The environment variables to set in the Docker container	Yes	Yes	Dict	Maximum length of 1024. Key Pattern: [a-zA-Z_][a-zA-Z0-9_]*. Value Pattern: [\S\s]*	{}
image	The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored	Yes	Yes	String
model_artifact_url	The S3 path where the model artifacts are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix)	Yes	Yes	String
model_package	The name or Amazon Resource Name (ARN) of the model package to use to create the model	Yes	Yes	String
secondary_containers	List of ContainerDefinition dictionaries in form of string (see Notes below)	Yes	Yes	String	Maximum number of 5 items

Notes:

• Please use the links in the Resources section for detailed information on each input parameter and SageMaker APIs used in this component
• If you don't specify a value for container_host_name parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
• Each key and value in the Environment parameter string to string map can have length of up to 1024. SageMaker supports up to 16 entries in the map.
• Input format to specify secondary_containers is:

[ 
      { 
         "ContainerHostname": "string",
         "Environment": { 
            "string" : "string" 
         },
         "Image": "string",
         "ModelDataUrl": "string",
         "ModelPackageName": "string"
      }
   ]

• Specify either an image, OR a model_package in the ContainerDefinition. model_artifact_url is optional if model artifact is already contained custom docker image.
• If you have a single container to define the model, use the parameters container_host_name, environment, image, model_artifact_url, model_package directly to define the primary container.

Outputs

Name	Description
model_name	The name of the model created in Amazon SageMaker

Requirements

• Kubeflow pipelines SDK
• Kubeflow set-up

Samples

Integrated into a pipeline

MNIST Classification pipeline: Pipeline | Steps

Resources

• Create Model API documentation
• Boto3 API reference