README.md

Deploy Deep Learning CNN on IoT Edge - Keras

In this tutorial, we introduce how to deploy an ML/DL (machine learning/deep learning) module through Azure IoT Edge.

Azure IoT Edge is an Internet of Things (IoT) service that builds on top of Azure IoT Hub. It is a hybrid solution combining the benefits of the two scenarios: IoT in the Cloud and IoT on the Edge. This service is meant for customers who want to analyze data on devices, a.k.a. "at the edge", instead of in the cloud. By moving parts of your workload to the edge, your devices can spend less time sending messages to the cloud and react more quickly to changes in status. On the other hand, Azure IoT Hub provides centralized way to manage Azure IoT Edge devices, and make it easy to train ML models in the Cloud and deploy the trained models on the Edge devices.

In this example, we deploy a trained Keras (Tensorflow) CNN model to the edge device. When the image data is generated from a process pipeline and fed into the edge device, the deployed model can make predictions right on the edge device without accessing to the cloud. Following diagram shows the major components of an Azure IoT edge device. Source code and full documentation are linked below.

We perform following steps for the deployment.

• Step 1: Build the trained ML/DL model into docker image. This image will be used to create a docker container running on the edge device.
• Step 2: Provision and Configure IoT Edge Device
• Step 3: Deploy ML/DL Module on IoT Edge Device
• Step 4: Test ML/DL Module

To get started with the tutorial, please proceed with following steps in sequential order.

• Prerequisites
• Steps
• Cleaning up

Prerequisites

1. Linux (x64) with GPU enabled.
2. Anaconda Python
3. Docker installed.
4. Azure account.

The tutorial was developed on an Azure Ubuntu DSVM, which addresses the first three prerequisites.

Steps

Please follow these steps to set up your environment and run notebooks. They setup the notebooks to use Docker and Azure seamlessly.

1. Add your user to the docker group:

   sudo usermod -aG docker $USER
   newgrp docker
   

   To verify whether you have correct configuration, try executing docker ps command. You should not get permission denied errors.

2. Navigate to the directory which is the framework you have chosen (e.g. Keras_Tensorflow).

3. Create the Python virtual environment using the environment.yml:

   conda env create -f environment.yml
   

4. Activate the virtual environment:

   source activate deployment_aml
   

5. Login to Azure:

   az login
   

6. If you have more than one Azure subscription, select it:

   az account set --subscription <Your Azure Subscription>
   

7. Start the Jupyter notebook server in the virtual environment:

   jupyter notebook
   

8. Select correct kernel: set the kernel to be Python [conda env: deployment_aml](or Python 3 if that option does not show).

9. After following the setup instructions above, run the Jupyter notebooks in order starting with the first notebook 00_AMLSetup.ipynb.

Cleaning up

To remove the conda environment created see here. The last Jupyter notebook also gives details on deleting Azure resources associated with this repository.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.