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This adds a new "Graph Capture" option to the DML ep, similar to the cuda graph functionality. Here's how graph capture works: - A user can enable graph capture in the session options by setting `ep.dml.enable_graph_capture` to `true` - When they want to capture a run, they set `gpu_graph_id` in their `RunOptions` to a number bigger than 0 (0 is reserved for internal use according to the cuda graph documentation). - Then, when they start the inference, the graph will be captured and stored in the DML EP for future use - When they execute the run for a second time with the same id, the `ReplayGraph` function in the DML EP will be called instead of executing the kernels, resulting in very low overhead and avoiding kernel recompilation. This feature can give up-to-par or even better performance than specifying the static dimensions at session creation time, but is also much more flexible. |
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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 |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.