1cdc23aba4
* update java API for STVM EP. Issue is from PR#10019 * use_stvm -> use_tvm * rename stvm worktree * STVMAllocator -> TVMAllocator * StvmExecutionProviderInfo -> TvmExecutionProviderInfo * stvm -> tvm for cpu_targets. resolve onnxruntime::tvm and origin tvm namespaces conflict * STVMRunner -> TVMRunner * StvmExecutionProvider -> TvmExecutionProvider * tvm::env_vars * StvmProviderFactory -> TvmProviderFactory * rename factory funcs * StvmCPUDataTransfer -> TvmCPUDataTransfer * small clean * STVMFuncState -> TVMFuncState * USE_TVM -> NUPHAR_USE_TVM * USE_STVM -> USE_TVM * python API: providers.stvm -> providers.tvm. clean TVM_EP.md * clean build scripts #1 * clean build scripts, java frontend and others #2 * once more clean #3 * fix build of nuphar tvm test * final transfer stvm namespace to onnxruntime::tvm * rename stvm->tvm * NUPHAR_USE_TVM -> USE_NUPHAR_TVM * small fixes for correct CI tests * clean after rebase. Last renaming stvm to tvm, separate TVM and Nuphar in cmake and build files * update CUDA support for TVM EP * roll back CudaNN home check * ERROR for not positive input shape dimension instead of WARNING * update documentation for CUDA * small corrections after review * update GPU description * update GPU description * misprints were fixed * cleaned up error msgs Co-authored-by: Valery Chernov <valery.chernov@deelvin.com> Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru> Co-authored-by: Thierry Moreau <tmoreau@octoml.ai> |
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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 | |||
| Mac | |||
| Android | |||
| iOS | |||
| 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.