To run this example on AWS you will also need to have the AWS CLI properly setup on your system. To do so please refer to AWS documentation. One can also run this example locally using Docker, or just by running the scripts locally.

On the developer machine:

To train your model you can
- use train_with_docker.sh to use Docker (recommended way),
- or, only if you know what you're doing and will manage synchronisation between versions, use python train.py
This will train a model and serialize the FHE circuit in a new folder called ./dev.

On the server machine:

Copy the './dev' directory from the developer machine.
If you need to delete existing Dockers: docker rm -f $(docker ps -a -q)
Launch the server via:

python ../server/deploy_to_docker.py --path-to-model ./dev

You will finally see some

INFO: Uvicorn running on http://0.0.0.0:5000 (Press CTRL+C to quit)

which means the server is ready to server, on Port 5000.

On the client machine:

If you go for a Docker part on the client side:

Launch the build_docker_client_image.py to build a client Docker image.
Run the client with client.sh script. This will run the container in interactive mode.
Then, in this Docker, you can launch the client script to interact with the server:

URL="<my_url>" python client.py

where <my_url> is the content of the url.txt file (if you don't set URL, the default is 0.0.0.0; this defines the IP to use when running server in Docker on localhost).

If you go for client side done in Python:

Prepare the client side:

python3.8 -m venv .venvclient
source .venvclient/bin/activate
pip install -r client_requirements.txt

Run the client script:

URL="http://localhost:8888" python client.py

And here it is! Whether you use Docker or Python for the client side, you deployed a Torus ML model and ran an inference using Fully Homormophic Encryption. In particular, you can see that the FHE predictions are correct.