fairscale
PyTorch extensions for high performance and large scale training. (by facebookresearch)
ColossalAI
Making large AI models cheaper, faster and more accessible (by hpcaitech)
fairscale | ColossalAI | |
---|---|---|
6 | 42 | |
2,907 | 37,911 | |
2.4% | 1.2% | |
4.5 | 9.7 | |
5 days ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fairscale
Posts with mentions or reviews of fairscale.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-27.
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[R] TorchScale: Transformers at Scale - Microsoft 2022 Shuming Ma et al - Improves modeling generality and capability, as well as training stability and efficiency.
I skimmed through the README and paper. What does this library have that that hasn't been included in xformers or fairscale?
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[D] DeepSpeed vs PyTorch native API
Things are slowly moving into PyTorch upstream such as the ZeRO redundancy optimizer but from my experience the team behind DeepSpeed just move faster. There is also fairscale from the FAIR team which seems to be a staging ground for experimental optimizations before they move into PyTorch. If you use Lightning, it's easy enough to try out these various libraries (docs here)
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How to Train Large Models on Many GPUs?
DeepSpeed [1] is amazing tool to enable the different kind of parallelisms and optimizations on your model. I would definitely not recommend reimplementing everything yourself.
Probably FairScale [2] too, but never tried it myself.
[1]: https://github.com/microsoft/DeepSpeed
[2]: https://github.com/facebookresearch/fairscale
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[P] PyTorch Lightning Multi-GPU Training Visualization using minGPT, from 250 Million to 4+ Billion Parameters
It was helpful for me to see how DeepSpeed/FairScale stack up compared to vanilla PyTorch Distributed Training specifically when trying to reach larger parameter sizes, visualizing the trade off with throughput. A lot of the learnings ended up in the Lightning Documentation under the advanced GPU docs!
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[D] Training 10x Larger Models and Accelerating Training with ZeRO-Offloading
I created a feature request on the FairScale project so that we can track the progress on the integration: Support ZeRO-Offload · Issue #337 · facebookresearch/fairscale (github.com)
ColossalAI
Posts with mentions or reviews of ColossalAI.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-22.
- FLaNK AI-April 22, 2024
- Making large AI models cheaper, faster and more accessible
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with a RLHF Pipeline
> open-source a complete RLHF pipeline ... based on the LLaMA pre-trained model
I've gotten to where when I see "open source AI" I now know it's "well, except for $some_other_dependencies"
Anyway: https://scribe.rip/@yangyou_berkeley/colossalchat-an-open-so... and https://github.com/hpcaitech/ColossalAI#readme (Apache 2) can save you some medium.com heartache at least
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Meet ColossalChat: An Open-Source AI Solution For Cloning ChatGPT With A Complete RLHF Pipeline
Quick Read: https://www.marktechpost.com/2023/04/01/meet-colossalchat-an-open-source-ai-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline/ Github: https://github.com/hpcaitech/ColossalAI Examples: https://chat.colossalai.org/
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A top AI researcher reportedly left Google for OpenAI after sharing concerns the company was training Bard on ChatGPT data
One of the current methods for training competing models is to have ChatGPT literally create prompt -> completion data sets. That's what was used for https://github.com/hpcaitech/ColossalAI. A model based off of the Llama weights released by facebook, then fine tuned on ChatGPT3.5 prompt + completions. So yes, there is a good chance that google is literally using ChatGPT in the training loop.
- Colossal-AI: open-source RLHF pipeline based on LLaMA pre-trained model
- ColossalChat
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with RLHF Pipeline
Here's the github from the article:
https://github.com/hpcaitech/ColossalAI
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Open source solution replicates ChatGPT training process
The article talks about their RLHF implementation briefly. There’s details on their RLHF implementation here: https://github.com/hpcaitech/ColossalAI/blob/a619a190df71ea3...
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how can I make my own chatGPT?
Here’s the project on GitHub: https://github.com/hpcaitech/ColossalAI
What are some alternatives?
When comparing fairscale and ColossalAI you can also consider the following projects:
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.