Colossal-AI is an open-source deep learning system designed for large-scale AI model training. It allows users to train and evaluate massive AI models efficiently across multiple GPUs or nodes, providing a flexible and modular framework for experimenting with distributed training strategies. Colossal-AI leverages the PyTorch framework and advanced parallelism techniques to deliver scalable and memory-efficient training, with a focus on usability, modularity, and open-source accessibility.

Features of Colossal-AI:

• Supports a wide range of training strategies, including data, model, and pipeline parallelism, using a unified interface.
• Optimized for large-scale deep learning pipelines, including memory-efficient computation, optimizer sharding, and gradient accumulation.
• Provides both Python API and CLI options for launching experiments and distributed training programs.
• Optionally supports multi-node and multi-GPU setups for training massive AI models efficiently.
• Open-source and actively maintained, widely used in AI research, model development, and educational projects.
• Cross-platform support for Linux, Windows, and macOS (depending on PyTorch and CUDA installation).

To check the installed Colossal-AI version on Ubuntu 24.04, you can check the package version in the virtual environment, for example:

 $ sudo su
 $sudo apt update
 $ source /opt/colossalai/venv/bin/activate 
$python -c "import colossalai; print(colossalai.__version__)"
# Displays installed version of Colossal-AI 

Disclaimer: Colossal-AI is open-source and maintained by the community. Users are responsible for ensuring correct setup and usage in their projects. Always refer to the official Colossal-AI documentation for the most accurate and up-to-date information.