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### Description 1. Renames all references of on device training to training apis. This is to keep the naming general. Nothing really prevents us from using the same apis on servers\non-edge devices. 2. Update ENABLE_TRAINING option: With this PR when this option is enabled, training apis and torch interop is also enabled. 3. Refactoring for onnxruntime_ENABLE_TRAINING_TORCH_INTEROP option: - Removed user facing option - Setting onnxruntime_ENABLE_TRAINING_TORCH_INTEROP to ON when onnxruntime_ENABLE_TRAINING is ON as we always build with torch interop. Once this PR is merged when --enable_training is selected we will do a "FULL Build" for training (with all the training entry points and features). Training entry points include: 1. ORTModule 2. Training APIs Features include: 1. ATen Fallback 2. All Training OPs includes communication and collectives 3. Strided Tensor Support 4. Python Op (torch interop) 5. ONNXBlock (Front end tools for training artifacts prep when using trianing apis) ### Motivation and Context Intention is to simply the options for building training enabled builds. This is part of the larger work item to create dedicated build for learning on the edge scenarios with just training apis enabled. |
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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 documention 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
Build Pipeline Status
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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.