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### Description Cherry-picks 26 commits to the release branch. Most cherry-picks are clean merges. Except: 1. When I got conflicts in cgmanifest.json and download-deps.yml, I choose to ignore the conflicts and regenerate the two files 2. There were some conflicts in cmake/deps.txt, onnxruntime_c_api.cc PR list: [js/webgpu] fix Transpose with non-float tensor (#15819) [js/web] fix terser reserved symbols for worker (#15864) [JSEP] fix constructor for OrtDevice (#15805) Bump engine.io from 6.4.1 to 6.4.2 in /js/web (#15799) Bump engine.io from 6.4.0 to 6.4.2 in /onnxruntime/test/wasm (#15798) [wasm] revert emsdk to v3.1.19 (#15793) [wasm/JSEP] add threaded build to artifacts (#15777) [js/web] add target ort.webgpu.min.js (#15780) update ort extensions to 94142d8391c9791ec71c38336436319a2d4ac7a0 (#15688) fix: setting builder optimization level to TRT 8.6 default (#15897) Adust GetVersionString() GetBuildInfoString() signatures and move them to OrtApi (#15921) Fix segfault for multiple GPU run (regression) (#15823) android package fix (#15999) [CoreML EP] Minor changes to allow CoreML EP to handle more nodes and models. (#15993) Adding support for conv fp16 fusion on Resnet50v1 (#15474) update onnx release 1.14 for docker files (#15680) Avoid generating training documentation during packaging (#15795) Update Conv-Add-Relu Fusion Transformation (#15834) Fix symbolic shape infer empty value_info (#15842) NhwcFusedConv: Add before Activation (#15837) use __hmul2 instead of __hmul2_rn (#15852) change the EP device to default OrtDevice() for memoryType equals CPU Input (#15903) Fixing NhwcFusedConv fp16 (#15950) fix topo sort in quantization tool (#16003) [doc] add LeakyRelu to coreml supported ops (#15944) [DML EP] Add frequent upload heap flushing (#15960) Co-authored-by: Yulong Wang Co-authored-by: dependabot[bot] Co-authored-by: Guenther Schmuelling Co-authored-by: Shalva Mist Co-authored-by: Maximilian Müller Co-authored-by: Dmitri Smirnov Co-authored-by: pengwa Co-authored-by: Ashwini Khade Co-authored-by: Edward Chen Co-authored-by: Jian Chen Co-authored-by: liqun Fu Co-authored-by: Baiju Meswani Co-authored-by: Tianlei Wu Co-authored-by: Chen Fu Co-authored-by: Ye Wang Co-authored-by: cao lei Co-authored-by: Yufeng Li Co-authored-by: Rachel Guo Co-authored-by: Patrice Vignola |
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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
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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 opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.