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Adds QNN EP HTP shared memory allocator. The HTP shared memory allocator (`HtpSharedMemoryAllocator`) calls the rpcmem shared library (libcdsprpc.so/dll) to allocate and free memory that can be shared between HTP and CPU. The allocator can be enabled by setting QNN EP option `enable_htp_shared_memory_allocator` to `1`. `QNNExecutionProvider::CreatePreferredAllocators()` will then return an instance of `HtpSharedMemoryAllocator`. For each QNN context, we also need to register and unregister memory handles in order to use the HTP shared memory. This memory handle management is added to `QnnBackendManager`, which also manages the QNN context handles. For more information about using HTP shared memory with QNN, see: https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/htp_shared_buffer_tutorial.html#shared-buffer-tutorial Limitations: - HTP shared memory usage is only supported for graph inputs and outputs. Intermediate values are not supported. - An allocation is assigned to a single shared memory buffer. The allocator is not smart enough to have multiple allocations share a single shared memory buffer. Co-authored-by: Baiju Meswani <bmeswani@microsoft.com> |
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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 documentation 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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Releases
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.
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.