meta-pytorch/torchforge
PyTorch-native post-training at scale
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 42 contributors on GitHub.
Gained 74% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a PyTorch-native library for scalable post-training reinforcement learning with clear abstractions separating infrastructure from model logic.
- Enables rapid RL research and experimentation by isolating algorithm development from complex infrastructure management while supporting large-scale GPU deployments.
- Use for developing and testing new reinforcement learning algorithms without managing distributed training infrastructure.
- Use for scaling RL workloads asynchronously or synchronously across thousands of GPUs in research or production environments.
- Use for customizing RL training loops with fine-grained control over resource placement, fault handling, and communication patterns.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
BSD 3-Clause
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
The primary purpose of the torchforge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. torchforge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
⚠️ Early Development Warning torchforge is currently in an experimental stage. You should expect bugs, incomplete features, and APIs that may change in future versions. The project welcomes bugfixes, but to make sure things are well coordinated you should discuss any significant change before starting the work. It's recommended that you signal your intention to contribute in the issue tracker, either by filing a new issue or by claiming an existing one.
View torchforge's hosted documentation (coming soon)
You can also find our notebook tutorials (coming soon)
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
105+3
Open issues
119+1
Compared with peers in Machine Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| photoprism/photoprism | 40.4K | — | 65 | 1 day ago |
| huggingface/diffusers | 34.8K | — | 65 | 3 days ago |
| onnx/onnx | 21.6K | — | 63 | 31 days ago |
| lukas-blecher/LaTeX-OCR | 16.6K | — | 18 | — |
| milesial/Pytorch-UNet | 11.7K | — | 18 | — |
| meta-pytorch/torchforge | 705 | — | 40 | — |
| Category median | 21.6K | — | 63 | — |
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