Deploy a Compiled Model Using Boto3 - Amazon SageMaker

Deploy a Compiled Model Using Boto3

You must satisfy the prerequisites section if the model was compiled using AWS SDK for Python (Boto3), AWS CLI, or the Amazon SageMaker console. Follow the steps below to create and deploy a SageMaker Neo-compiled model using Amazon Web Services SDK for Python (Boto3).

Deploy the Model

After you have satisfied the prerequisites, use the create_model, create_enpoint_config, and create_endpoint APIs.

The following example shows how to use these APIs to deploy a model compiled with Neo:

import boto3 client = boto3.client('sagemaker') # create sagemaker model create_model_api_response = client.create_model( ModelName='my-sagemaker-model', PrimaryContainer={ 'Image': <insert the ECR Image URI>, 'ModelDataUrl': 's3://path/to/model/artifact/model.tar.gz', 'Environment': {} }, ExecutionRoleArn='ARN for AmazonSageMaker-ExecutionRole' ) print ("create_model API response", create_model_api_response) # create sagemaker endpoint config create_endpoint_config_api_response = client.create_endpoint_config( EndpointConfigName='sagemaker-neomxnet-endpoint-configuration', ProductionVariants=[ { 'VariantName': <provide your variant name>, 'ModelName': 'my-sagemaker-model', 'InitialInstanceCount': 1, 'InstanceType': <provide your instance type here> }, ] ) print ("create_endpoint_config API response", create_endpoint_config_api_response) # create sagemaker endpoint create_endpoint_api_response = client.create_endpoint( EndpointName='provide your endpoint name', EndpointConfigName=<insert your endpoint config name>, ) print ("create_endpoint API response", create_endpoint_api_response)

The AmazonSageMakerFullAccess and AmazonS3ReadOnlyAccess policies must be attached to the AmazonSageMaker-ExecutionRole IAM role.

For full syntax of create_model, create_endpoint_config, and create_endpoint APIs, see create_model, create_endpoint_config, and create_endpoint, respectively.

If you did not train your model using SageMaker, specify the following environment variables:

MXNet and PyTorch
"Environment": { "SAGEMAKER_PROGRAM": "", "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code", "SAGEMAKER_CONTAINER_LOG_LEVEL": "20", "SAGEMAKER_REGION": "insert your region", "MMS_DEFAULT_RESPONSE_TIMEOUT": "500" }
"Environment": { "SAGEMAKER_PROGRAM": "", "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code", "SAGEMAKER_CONTAINER_LOG_LEVEL": "20", "SAGEMAKER_REGION": "insert your region" }

If you trained your model using SageMaker, specify the environment variable SAGEMAKER_SUBMIT_DIRECTORY as the full Amazon S3 bucket URI that contains the training script.