Onnxruntime
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Onnxruntime | 10,419 | | 8 | 59 | 18 hours ago | 34 | June 16, 2023 | 2,041 | mit | C++ |
| ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator | ||||||||||
| Tvm | 10,330 |  | 3 | 19 hours ago | 1 | February 23, 2021 | 764 | apache-2.0 | Python | |
| Open deep learning compiler stack for cpu, gpu and specialized accelerators | ||||||||||
| Qkeras | 498 | 2 days ago | 1 | July 07, 2021 | 35 | apache-2.0 | Python | |||
| QKeras: a quantization deep learning library for Tensorflow Keras | ||||||||||
| Lbann | 214 | a day ago | 194 | other | C++ | |||||
| Livermore Big Artificial Neural Network Toolkit | ||||||||||
| Knowledge Extraction Recipes Forms | 194 | 2 months ago | 2 | mit | Jupyter Notebook | |||||
| Knowledge Extraction For Forms Accelerators & Examples | ||||||||||
| Tvm Vta | 170 | 9 months ago | 2 | apache-2.0 | Scala | |||||
| Open, Modular, Deep Learning Accelerator | ||||||||||
| Ai Serving | 141 | 17 days ago | 4 | apache-2.0 | Scala | |||||
| Serving AI/ML models in the open standard formats PMML and ONNX with both HTTP (REST API) and gRPC endpoints | ||||||||||
| Learning Nvdla Notes | 117 | 5 years ago | 17 | |||||||
| NVDLA is an Open source DL/ML accelerator, which is very suitable for individuals or college students. This is the NOTES when I learn and try. Hope THIS PAGE may Helps you a bit. Contact Me:[email protected] | ||||||||||
| Cs217.github.io | 89 | 7 months ago | 3 | bsd-3-clause | CSS | |||||
| Course Webpage for CS 217 Hardware Accelerators for Machine Learning, Stanford University | ||||||||||
| Arrayfire Ml | 88 | 5 years ago | 20 | bsd-3-clause | C++ | |||||
| ArrayFire's Machine Learning Library. | ||||||||||
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 →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
License
This project is licensed under the MIT License.
