Deploying ML Model from Azure ACR Docker Images to Raspberry Pi

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Introduction

This guide outlines deploying a machine learning model from a Docker image stored in Azure Container Registry (ACR) to a Raspberry Pi. By containerizing your ML model, you can ensure consistent deployment across different environments, including edge devices like the Raspberry Pi.

Prerequisites

Before you begin, ensure the following:

  • An Azure Container Registry (ACR) with your ML model Docker image.
  • Docker installed on your local development machine.
  • Raspberry Pi set up with Raspbian OS and Docker installed.

Build and Push Docker Image

  • Build Docker Image:
  • On your development machine, navigate to the directory containing your ML model code and Dockerfile.
  • Build the Docker image:

docker build -t <acr-login-server>/<image-name>:<tag> .

  • Push Docker Image to ACR:
  • Push the Docker image to your Azure Container Registry:

docker push <acr-login-server>/<image-name>:<tag>

Setting up Raspberry Pi

  • Install Docker on Raspberry Pi:
  • Connect to your Raspberry Pi and install Docker. Refer to the Docker documentation for Raspberry Pi for detailed instructions.
  • Pull Docker Image on Raspberry Pi:
  • On your Raspberry Pi, open a terminal and pull the Docker image from ACR:

docker pull <acr-login-server>/<image-name>:<tag>

Run Docker Container

  • Run Docker Container on Raspberry Pi:
  • Start a Docker container on your Raspberry Pi:

docker run -d --name ml-model-container <acr-login-server>/<image-name>:<tag>

Testing the ML Model

  • Access the Running Container:
  • Open a terminal on your Raspberry Pi and access the running container:

docker exec -it ml-model-container /bin/bash

  • Test the ML Model:
  • Once inside the container, run tests or inference scripts to verify the functionality of your ML model.

Monitoring and Logging

Integrate monitoring and logging solutions to track the performance and behaviour of your ML model on the Raspberry Pi.

Troubleshooting

If you encounter issues during deployment, check Docker logs on the Raspberry Pi, inspect container status, and review any error messages. Additionally, ensure that your Raspberry Pi is correctly configured, and the Docker image is compatible with the ARM architecture.