Deployment

So far, we ran our MXNet code on Amazon EC2 instances, just like any other Python application. As you may know, there are alternative ways to run code on AWS and they can be obviously applied to MXNet.

#1 — Continuous Delivery of MXNet APIs using Code* and Amazon ECS

Using a CloudFormation template, this project will create an automated workflow that will provision, configure and orchestrate a pipeline triggering deployment of any changes to your MXNet model or application code. You will orchestrate all of the changes into a deployment pipeline to achieve continuous delivery using CodePipeline and CodeBuild. You can deploy new MXNet APIs and make those available to your users in just minutes, not days or weeks.

More information in the companion blog post:

#2 — Deploying MXNet in a Lambda function

This is a reference application that predicts labels along with their probabilities for an image using a pre-trained model with Apache MXNet deployed on AWS Lambda. A Serverless Application Model template (SAM) and instructions are provided to automate the creation of an API endpoint.

You can leverage this package and its precompiled libraries to build your prediction pipeline on AWS Lambda with MXNet. Additional models can be found in the Model Zoo

More information the companion blog post: