{% hint style="danger" %} Not all hardware/OS combinations are supported. Determine your platform, OS version, and Python version before referencing the table below. {% endhint %}
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 | Yes |
Only some versions of python are supported: In the current release, these are 3.8, 3.9 and 3.10. 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) 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.
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.
To install Concrete ML from PyPi, run the following:
pip install -U pip wheel setuptools
pip install concrete-mlThis will automatically install all dependencies, notably Concrete.
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:v0.4.0The 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-mlThis 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