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Installation
This document provides guides on how to install TorusML 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, TorusML 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, TorusML supports only
3.8,3.9,3.10,3.11and3.12versions ofpython. - Linux requirement: The TorusML Python package requires
glibc >= 2.28. On Linux, you can check yourglibcversion by runningldd --version. - Kaggle installation: TorusML 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 TorusML using PyPi requires a Linux-based OS or macOS (both x86 and Apple Silicon CPUs are supported).
{% hint style="info" %} If you need to install on Windows, use Docker or WSL. On WSL, TorusML will work as long as the package is not installed in the /mnt/c/ directory, which corresponds to the host OS filesystem. {% endhint %}
Installation
To install TorusML 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 TorusML using Docker by either pulling the latest image or a specific version:
docker pull luxfhe/concrete-ml:latest
# or
docker pull luxfhe/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 luxfhe/concrete-ml
# With local volume to save notebooks on host:
docker run --rm -it -p 8888:8888 -v /host/path:/data luxfhe/concrete-ml
This will launch a TorusML 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 luxfhe/concrete-ml /bin/bash