b327e89efa
* squashed commit for standalone tvm execution provider * critical fix for correct python build with stvm ep * get tuning log file from ep options. It has priority over AUTOTVM_TUNING_LOG * updates and fixes * update parsing of stvm provider options * add support of external data for onnx model * add conditional dump of subgraphs * remove unused code * get input tensor shapes through provider options. get output shapes for fixed input ones by TVM API * support AUTO_TVM tuning log file inside ORT. Selector for Ansor and Auto_TVM is provider option (tuning_type) * add fp16 * add functionality of conversion of model layout to NHWC if need. Necessary parameter was added to STVM provider options * fix license text in header. fix log format * small fixes * fix issues from flake8 * remove model proto construction from GetCapability * reserve memory for vector of DLTensors * add simple tutorial for STVM EP * STVM docs * jroesch/tvm -> apache/tvm * remove dead code, unneccessary logs and comments * fix in readme * improve tutorial notebook * tvm update * update STVM_EP.md * fix default value * update STVM_EP.md * some TODOs for the future development * shorten long lines * add hyperlink to STVM_EP.md * fix Linux CI error * fix error in csharp test Co-authored-by: Jared Roesch <jroesch@octoml.ai> Co-authored-by: Valery Chernov <valery.chernov@deelvin.com> Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru> |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
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| WebAssembly |
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.