Installation

Please note that not all hardware/OS combinations are supported. Determine your platform, OS version, and Python version before referencing the table below.

Depending on your OS, Concrete-ML may be installed with Docker or with pip:

OS / HW	Available on Docker	Available on pip
Linux	Yes	Yes
Windows	Yes	Not currently
Windows Subsystem for Linux	Yes	Yes
macOS (Intel)	Yes	Yes
macOS (Apple Silicon, ie M1, M2 etc)	Yes	Not currently

Also, only some versions of python are supported: in the current release, these are 3.7 (Linux only), 3.8, and 3.9. Moreover, the Concrete-ML Python package requires glibc >= 2.28. On Linux, you can check your glibc version by running ldd --version.

Concrete-ML can be installed on Kaggle (see question on community for more details), but not on Google Colab (see question on community for more details).

Most of these limits are shared with the rest of the Concrete stack (namely Concrete-Numpy and Concrete-Compiler). Support for more platforms will be added in the future.

Using PyPi

Requirements

Installing Concrete-ML using PyPi requires a Linux-based OS or macOS running on an x86 CPU. For Apple Silicon, Docker is the only currently supported option (see below).

Installing on Windows can be done using Docker or WSL. On WSL, Concrete-ML will work as long as the package is not installed in the /mnt/c/ directory, which corresponds to the host OS filesystem.

Installation

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

pip install -U pip wheel setuptools
pip install concrete-ml

This will automatically install all dependencies, notably Concrete-Numpy.

Using Docker

Concrete-ML can be installed using Docker by either pulling the latest image or a specific version:

docker pull zamafhe/concrete-ml:latest
# or
docker pull zamafhe/concrete-ml:v0.4.0

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

The image can then be used via the following command:

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

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

This will launch a Concrete-ML enabled Jupyter server in Docker that can be accessed directly from a browser.

Alternatively, a shell can be lauched in Docker, with or without volumes:

docker run --rm -it zamafhe/concrete-ml /bin/bash