tracel-ai/burn
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 7 days ago, with 364 contributors on GitHub.
Peaked at #24 in the weekly ranking the week of Jan 18, its best in the past year.
Use Cases & Benefits
- Provides a next-generation deep learning framework and tensor library emphasizing flexibility, efficiency, and portability using Rust.
- Delivers high performance through automatic kernel fusion, asynchronous execution, and intelligent memory management across multiple hardware backends.
- Use for building and training neural networks with thread-safe modules and multi-device support leveraging Rust's ownership system.
- Use for deploying deep learning models on diverse hardware including GPUs, CPUs, embedded devices, and WebAssembly environments with seamless backend switching.
- Use for importing and running ONNX, PyTorch, or Safetensors models to leverage existing pretrained models within a performant Rust-based framework.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
Because we believe the goal of a deep learning framework is to convert computation into useful intelligence, we have made performance a core pillar of Burn. We strive to achieve top efficiency by leveraging multiple optimization techniques described below.
Click on each section for more details 👇
Burn strives to be as fast as possible on as many hardwares as possible, with robust implementations. We believe this flexibility is crucial for modern needs where you may train your models in the cloud, then deploy on customer hardwares, which vary from user to user.
Compared to other frameworks, Burn has a very different approach to supporting many backends. By design, most code is generic over the Backend trait, which allows us to build Burn with swappable backends. This makes composing backend possible, augmenting them with additional functionalities such as autodifferentiation and automatic kernel fusion.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+496 stars over 87 days across 8 snapshot days; best day +20 on Oct 9.
Forks
1.1K+106
Open issues
171-126
Compared with peers in Mobile
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| milind-soni/OpenMausBot | 20.6K | +8.4K | 75 | today |
| stablyai/orca | 121.4K | +6.7K | 75 | today |
| cjpais/Handy | 38.3K | +1.0K | 75 | 8 days ago |
| elie222/rakazo | 7.8K | +958 | 75 | today |
| rustdesk/rustdesk | 128.9K | +859 | 75 | 1 day ago |
| tracel-ai/burn | 16.2K | — | 65 | 7 days ago |
| Category median | 38.3K | +1.0K | 75 | — |
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