ColossalAI boosts your model training with parallelization plugins that slash costs & time. Mix data, tensor & pipeline parallel - write one, run distributed. Tools auto-shard across GPUs. Train fast on one node or huge models like 175B OPT across clusters.
ColossalAI lets engineers train even larger models faster using standard parallelization techniques, requiring minimal code changes—toggle settings like Lego blocks. ColossalAI handles the complexity behind the scenes.
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🔹 Mix and match data, pipeline, and tensor (1D to 3D) parallelism to fit your model—no need to rewrite code for distributed training. The same code runs on 1 GPU or cluster. The software shards and glues things under the hood.
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🔹 Unique memory managers like Gemini give 2-5x memory savings, letting you train models too big for your GPUs. PatrickStar uses chunking to 2x distributed efficiency. No more OOM errors!
Integrations allow 1-line training for 175B models like OPT or fine-tuning BLOOM. Reduce costs by 5-50x on clusters.
These advancements unlock huge AI that was out of reach for small teams and academics. It has a dashboard to monitor in real-time, too.
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Enables training massive AI models with minimal code changes. Just toggle parallelization strategies like data, tensor, and pipeline parallelism that work seamlessly in the background. It makes distributed training over clusters accessible to non-experts. 💪
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Unique memory optimizers like Gemini 💎 and PatrickStar ⭐ give 2-5x memory savings, letting you train models too big to fit on your GPUs without getting OOM errors. Push hardware limits. 📈
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Reduces cost 💰 and time ⏱️ of training huge 175B+ parameter models by 5-50x through optimizations and integrations. It unlocks cutting-edge AI for small teams. Accelerate model R&D and productization. 🚀
⛓️ Simplifies distributed training - Mix and match data, tensor, and pipeline parallel with no code changes. It just works out of the box like Lego blocks. 👷♂️
💰 Cuts hardware costs 5-50x - Optimizes memory and computations to slash spending on clusters needed for huge models. It unlocks cutting-edge AI affordably. 💸
⚡️ Speeds up experiments - Train models 5-10x faster. Accelerate research prototyping and product build cycle. 🏃♂️
🧠 Trains bigger models - Unique memory optimizers fit larger models on your existing GPUs. Get 2-5x more capacity to push performance. 📈
🎛️ AutoComplexity coming soon - No need to hand-tune 100 params. AutoML automates finding optimal parallel config. 🤖
- 👷🏽♀️ Builders: Shenggui Li, Jiarui Fang, Hongxin Liu
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/shenggui-li/, https://www.linkedin.com/in/fangjiarui/
- 👩🏽🏭 Builders on X: https://twitter.com/frankkklee, https://github.com/feifeibear
- 💾 Used in 275 repositories
- 👩🏽💻 Contributors: 161
- 💫 GitHub Stars: 36.2k
- 🍴 Forks: 4.1k
- 👁️ Watch: 373
- 🪪 License: Apache-2.0
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/hpcaitech/ColossalAI
- Official Website: https://colossalai.org/
- LinkedIn Page: https://www.linkedin.com/company/hpc-ai-technology/
- Slack Community: https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-2404o93sy-Y3~br1qkIeEcMOVSfJ8YYg
- X Page: https://twitter.com/HPCAITech
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/colossalai/README.md
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