README.md

Deployment

In this folder we show how to deploy a simple Concrete ML model that does breast cancer classification, either through Docker or Amazon Web Services.

Get started

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.

1. To train your model you can use train.py, or train_with_docker.sh to use Docker (recommended way). This will train a model and serialize the FHE circuit in a new folder called ./dev.
2. Once that's done you can use the script provided in Concrete ML in use_case_examples/deployment/server/, use deploy_to_docker.py.

• python use_case_examples/deployment/server/deploy_to_docker.py --path-to-model ./dev

3. Once that's done you can launch the build_docker_client_image.py script to build a client Docker image.
4. You can then run the client by using the client.sh script. This will run the container in interactive mode. To interact with the server you can launch the client.py script using URL="<my_url>" python client.py where <my_url> is the content of the url.txt file (default is 0.0.0.0, ip to use when running server in Docker on localhost).

And here it is you deployed a Concrete ML model and ran an inference using Fully Homormophic Encryption.