bvallelunga/containerized-app

A containerized demo of the 'similar-words' example app.

Example Containerized App

Prerequisites

1. Install and open the docker app on your machine.
2. Install git lfs and clone this repo.
3. Install the docker python sdk:

pip3 install docker

4. cd into the repo.

Getting Started

First, build the docker image and create a docker container from that image:

python build.py

Once we have a container, we can start it up with:

python start.py

We can then send data to the model, running inside the container, through POST requests:

sh post.sh

When we are done with the container, we can stop it with:

python stop.py

Production Setting

In a production setting, the flow would be as follows:

1. An app is approved for the marketplace
2. A docker image is automatically created for the app and uploaded to our image registry (public or private)
3. A provider machine would then enroll in the app * i.e. the dvm-cli will create a docker container locally using the docker image found in our registry
4. The provider would then start providing * i.e. the docker container will start allowing it to process prediction requests