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
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
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