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)
Topics
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)
Note
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
create_endpoint
If you did not train your model using SageMaker, specify the following environment variables:
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.