Train and Deploy Machine Learning Model With Web Interface - Docker, PyTorch & Flask.
Running on Local/cloud machine
Clone the repo and build the docker image
sudo docker build -t flaskml .
Then after that you can run the container while specefying the absolute path to the app
sudo docker run -i -t --rm -p 8888:8888 -v **absolute path to app directory**:/app flaskml
This will run the application on localhost:8888
You can use serveo.net or Ngrok to port the application to the web.
Running on Jetson-Nano
On Jetson-nano, to avoid long running time to build the image, you can download it from Docker Hub.
We will also use a costumized Docker command https://gist.github.com/imadelh/cf7b12c9cc81c3cb95ad2c6bc747ccd0 to be able to access the GPU of the device on the container.
docker pull imadelh/jetson_pytorch_flask:arm_v1
Then on your device you can access the bash (this the default command on that image)
sudo ./mydocker.sh run -i -t --rm -v /home/imad:/home/root/ imadelh/jetson_pytorch_flask:arm_v1
and then simply get to the application directory and run it
cd app python3 app.py
- Training and saving the CNN model : https://gist.github.com/imadelh/b337c7b16899831d80d9221a9a60e09f
- Visualize the inference : https://colab.research.google.com/github/imadelh/ML-web-app/blob/master/Notebooks/emnist_inference_cnn-2.ipynb
This a generic web app for ML models. You can update your the network and weights by changing the following files.