Installing

Concrete ML can be run on Linux based OSes as well as macOS on x86 CPUs. These hardware requirements are dictated by Concrete-Lib.

Do note that since WSL on Windows is a Linux based OS, Concrete ML will work as long as the package is not mandated in the /mnt/c/ directory, which corresponds to the host OS filesystem.

Python package

To install Concrete-ML from PyPi, run the following:

pip install concrete-ml

Note that concrete-ml installs concrete-numpy with all extras, including pygraphviz to draw graphs.

pygraphviz requires graphviz packages being installed on your OS, see https://pygraphviz.github.io/documentation/stable/install.html

graphviz packages are binary packages that won't automatically be installed by pip. Do check https://pygraphviz.github.io/documentation/stable/install.html for instructions on how to install graphviz for pygraphviz.

Docker image

You can also get the concrete-ml Docker image by either pulling the latest Docker image or a specific version:

docker pull zamafhe/concrete-ml:latest
# or
docker pull zamafhe/concrete-ml:v0.1.0

The image can be used with Docker volumes, see the Docker documentation here.

You can then use this image with the following command:

# Without local volume:
docker run --rm -it -p 8888:8888 zamafhe/concrete-ml:v0.1.0

# With local volume to save notebooks on host:
docker run --rm -it -p 8888:8888 -v /host/path:/data zamafhe/concrete-ml:v0.1.0

This will launch a Concrete-ML enabled Jupyter server in Docker, that you can access from your browser.

Alternatively, you can just open a shell in Docker with or without volumes:

docker run --rm -it zamafhe/concrete-ml:v0.2.0 /bin/bash