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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
3b73ef2bf7
Bumps [torch](https://github.com/pytorch/pytorch) from 1.13.1 to 2.2.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/pytorch/pytorch/releases">torch's releases</a>.</em></p> <blockquote> <h2>PyTorch 2.2: FlashAttention-v2, AOTInductor</h2> <h1>PyTorch 2.2 Release Notes</h1> <ul> <li>Highlights</li> <li>Backwards Incompatible Changes</li> <li>Deprecations</li> <li>New Features</li> <li>Improvements</li> <li>Bug fixes</li> <li>Performance</li> <li>Documentation</li> </ul> <h1>Highlights</h1> <p>We are excited to announce the release of PyTorch® 2.2! PyTorch 2.2 offers ~2x performance improvements to <code>scaled_dot_product_attention</code> via FlashAttention-v2 integration, as well as AOTInductor, a new ahead-of-time compilation and deployment tool built for non-python server-side deployments.</p> <p>This release also includes improved torch.compile support for Optimizers, a number of new inductor optimizations, and a new logging mechanism called TORCH_LOGS.</p> <p><strong>Please note that we are <a href="https://redirect.github.com/pytorch/pytorch/issues/114602">deprecating macOS x86 support</a>, and PyTorch 2.2.x will be the last version that supports macOS x64.</strong></p> <p>Along with 2.2, we are also releasing a series of updates to the PyTorch domain libraries. More details can be found in the library updates blog.</p> <p>This release is composed of 3,628 commits and 521 contributors since PyTorch 2.1. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.2. More information about how to get started with the PyTorch 2-series can be found at our <a href="https://pytorch.org/get-started/pytorch-2.0/">Getting Started</a> page.</p> <p>Summary:</p> <ul> <li><code>scaled_dot_product_attention</code> (SDPA) now supports FlashAttention-2, yielding around 2x speedups compared to previous versions.</li> <li>PyTorch 2.2 introduces a new ahead-of-time extension of TorchInductor called AOTInductor, designed to compile and deploy PyTorch programs for non-python server-side.</li> <li><code>torch.distributed</code> supports a new abstraction for initializing and representing ProcessGroups called device_mesh.</li> <li>PyTorch 2.2 ships a standardized, configurable logging mechanism called TORCH_LOGS.</li> <li>A number of torch.compile improvements are included in PyTorch 2.2, including improved support for compiling Optimizers and improved TorchInductor fusion and layout optimizations.</li> <li>Please note that we are deprecating macOS x86 support, and PyTorch 2.2.x will be the last version that supports macOS x64.</li> <li><code>torch.ao.quantization</code> now offers a prototype <code>torch.export</code> based flow</li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href=" |
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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.