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Train and Deploy Machine Learning Model With Web Interface - Docker, PyTorch & Flask

Train and Deploy Machine Learning Model With Web Interface - Docker, PyTorch & Flask

ML-web-app

Train and Deploy Machine Learning Model With Web Interface - Docker, PyTorch & Flask.

Train-and-Deploy

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

Useful files

Info

This a generic web app for ML models. You can update your the network and weights by changing the following files.

app/ml_model/network.py
app/ml_model/trained_weights.pth

GitHub