microsoft/onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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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 5 days ago, with 402 contributors on GitHub.
Gained 11% more stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a cross-platform, high-performance machine learning inference and training accelerator supporting multiple frameworks and hardware.
- Delivers optimal performance by leveraging hardware accelerators and graph optimizations across diverse operating systems and devices.
- Use for accelerating inference of deep learning models from PyTorch, TensorFlow, and classical ML libraries like scikit-learn.
- Use for speeding up multi-node NVIDIA GPU training of transformer models with minimal code changes in PyTorch scripts.
- Use for integrating efficient ML model deployment in applications requiring cross-platform compatibility and hardware acceleration.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
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
ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
4.4K+301
Open issues
1.8K+326
Compared with peers in Deep Learning
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 |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 78.3K | +1.6K | 75 | 1 day ago |
| SYSTRAN/faster-whisper | 26.5K | — | 40 | 368 days ago |
| microsoft/onnxruntime | 22.2K | — | 65 | 5 days ago |
| GeeeekExplorer/nano-vllm | 16.3K | — | 12 | — |
| Lightning-AI/litgpt | 13.8K | — | 58 | 129 days ago |
| data-privacy-stack/presidio | 11.8K | — | 64 | 10 days ago |
| Category median | 16.3K | — | 58 | — |
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