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This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker.
This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (build.py
). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with staging
and prod
stages.
Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging
and prod
. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. You will need to go to CodePipeline AWS Managed Console to complete this step.
You own this code and you can modify this template to change as you need it, add additional tests for your custom validation.
A description of some of the artifacts is provided below:
buildspec.yml
build.py
endpoint-config-template.yml
staging-config.json
staging
stage in the pipeline. You can configure the instance type, instance count here.prod-config.json
prod
stage in the pipeline. You can configure the instance type, instance count here.test\buildspec.yml
staging
stage to run the test code of the following python filetest\test.py
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?