Installation

This document provides guides on how to install Concrete ML using PyPi or Docker.

Prerequisite

Before you start, determine your environment:

• Hardware platform

• Operating System (OS) version

• Python version

OS/HW support

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

OS / HW	Available on Docker	Available on pip
Linux	Yes	Yes
Windows	Yes	No
Windows Subsystem for Linux	Yes	Yes
macOS 11+ (Intel)	Yes	Yes
macOS 11+ (Apple Silicon: M1, M2, etc.)	Coming soon	Yes

Python support

• Version: In the current release, Concrete ML supports only 3.8, 3.9 and 3.10 versions of python.

• Linux requirement: The Concrete ML Python package requires glibc >= 2.28. On Linux, you can check your glibc version by running ldd --version.

• Kaggle installation: Concrete ML can be installed on Kaggle (see question on community for more details) and on Google Colab.

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

Installation using PyPi

Requirements

Installing Concrete ML using PyPi requires a Linux-based OS or macOS (both x86 and Apple Silicon CPUs are supported).

If you need to install on Windows, use 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.

If you encounter any issue during installation on Apple Silicon mac, please visit this troubleshooting guide on community.

Installation using Docker

You can install Concrete ML 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

You can use the image with Docker volumes, see the Docker documentation here. Use 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, you can launch a shell in Docker, with or without volumes:

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

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