Colossal-AI

Colossal-AI is an open-source distributed training framework designed to help researchers and engineers train large-scale neural networks with unmatched speed and efficiency. It provides a rich set of parallelism strategies—including tensor, pipeline, and data parallelism—that can be combined to maximize GPU utilization across clusters. The framework is developed by HPC-AI Technology and is actively maintained with a growing community of contributors and users.

• Distributed Training — supports data, tensor, and pipeline parallelism out of the box, enabling efficient scaling across multiple GPUs and nodes.
• Hybrid Parallelism — combine multiple parallelism paradigms (e.g., train GPT with hybrid parallelism) to achieve optimal throughput for your specific model architecture.
• Gemini Heterogeneous Memory Manager — intelligently manages CPU and GPU memory to reduce out-of-memory errors and allow training of larger models on limited hardware.
• Command Line Interface (CLI) — a unified CLI tool to launch distributed jobs, run tensor parallel micro-benchmarks, and manage Colossal-AI projects.
• Flexible Configuration — define project configurations declaratively, specifying features, parallelism strategies, and global hyper-parameters in a single config file.
• Quick Start & Examples — get started quickly with installation guides, quick demos, and a rich library of usage examples covering common large model training scenarios.
• Active Community — engage with other users and contributors via GitHub Discussions, Slack, and the project forum; submit your own Colossal-AI projects to the showcase.
• Open Source — the full source code is publicly available on GitHub under an open-source license, making it freely usable and extensible for research and production.