This document provides guides on how to install Concrete ML using PyPi or Docker.
Before you start, determine your environment:
Hardware platform
Operating System (OS) version
Python version
Depending on your OS/HW, Concrete ML may be installed with Docker or with pip:
OS / HW | Available on Docker | Available on pip |
---|---|---|
Version: In the current release, Concrete ML supports only 3.8
, 3.9
, 3.10
and 3.11
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.
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.
To install Concrete ML from PyPi, run the following:
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.
You can install Concrete ML using Docker by either pulling the latest image or a specific version:
You can use the image with Docker volumes, see the Docker documentation here. Use the following command:
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:
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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
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