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* copy changes from trt_and_mem * second edits * Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines * Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines * Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines * change to cuda 11.4 * build with cuda 11.4 * Update Dockerfile.ubuntu_cuda11_1_tensorrt7_2 * add cmake extra defines * cmake architectures * fix cmake arch * Delete ubuntu-18.04.Dockerfile * Rename Dockerfile.ubuntu_cuda11_1_tensorrt7_2 to Dockerfile.ubuntu_cuda11_4_tensorrt7_2 * Update linux-gpu-tensorrt-ci-perf-pipeline.yml * Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines * removing previous ort args * rename to cuda 11.4 * remove cuda 10_2 * delete trt 7.1 * remove 7.1 * Passing in cuda architecture to reduce build time * always add submodule sync due to recursive cloning * fix run command * add and * take away unused arms and share python installation script * Update linux-gpu-tensorrt-ci-perf-pipeline.yml * Update Dockerfile.tensorrt * cleanup file * install python directly on dockerfile - move to scripts in future * Update Dockerfile.custom-trt-perf * adding cuda 11.1 for missing Libnvrtc.so.11.1 * Delete install_python.sh |
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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.