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Changes include: - Added a new page for `On-Device Training` overview: [Preview](https://baijumeswani.github.io/onnxruntime/docs/get-started/on-device-training.html) - Added a new section for `On-Device Training` installation: [Preview](https://baijumeswani.github.io/onnxruntime/docs/install/#install-for-on-device-training) - Added a new section for `On-Device Training` build from source: [Preview](https://baijumeswani.github.io/onnxruntime/docs/build/training.html#build-for-on-device-training) - Updated Large Model Training overview, installation, build pages to reflect what is currently accurate. Website preview: https://baijumeswani.github.io/onnxruntime/ Pending website work: - Update links for released packages for training. - Add tutorial for on-device training - Add links to the blog posts that detail on device training.
70 lines
4 KiB
Markdown
70 lines
4 KiB
Markdown
---
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title: ONNX Runtime
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description: ONNX Runtime is a cross-platform machine-learning model accelerator
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has_children: false
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nav_order: 0
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redirect_from: /how-to
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---
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# Welcome to ONNX Runtime
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{: .no_toc }
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ONNX Runtime is a cross-platform machine-learning model accelerator, with a flexible interface to integrate hardware-specific libraries. ONNX Runtime can be used with models from PyTorch, Tensorflow/Keras, TFLite, scikit-learn, and other frameworks.
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<iframe height="315" class="table-wrapper py px" src="https://www.youtube.com/embed/waIeC3OIn70" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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## How to use ONNX Runtime
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| <span class="fs-5"> [Get started with ORT](./get-started){: .btn .mr-4 target="_blank"} </span> | <span class="fs-5"> [API Docs](./api){: .btn target="_blank"} </span> |
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| <span class="fs-5"> [Tutorials](./tutorials){: .btn target="_blank"} </span> | <span class="fs-5"> [Ecosystem](./ecosystem){: .btn target="_blank"} </span> |
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| <span class="fs-5">[ONNX Runtime YouTube](https://www.youtube.com/channel/UC_SJk17KdRvDulXz-nc1uFg/featured){: .btn .mr-4 target="_blank"} </span> |
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## Contribute and Customize
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| <span class="fs-5"> [Build ORT Packages](./build){: .btn .mr-4 target="_blank"} </span>| <span class="fs-5">[ONNX Runtime GitHub](https://github.com/microsoft/onnxruntime){: .btn target="_blank"} </span> |
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---
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## QuickStart Template
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| <span class="fs-5"> [ORT Web JavaScript Site Template](https://github.com/microsoft/onnxruntime-nextjs-template){: .btn .mr-4 target="_blank"} </span> | <span class="fs-5"> [ORT C# Console App Template](https://github.com/microsoft/onnxruntime-csharp-cv-template){: .btn .mr-4 target="_blank"} </span> |
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---
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## ONNX Runtime for Inferencing
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ONNX Runtime Inference powers machine learning models in key Microsoft products and services across Office, Azure, Bing, as well as dozens of community projects.
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Examples use cases for ONNX Runtime Inferencing include:
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* Improve inference performance for a wide variety of ML models
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* Run on different hardware and operating systems
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* Train in Python but deploy into a C#/C++/Java app
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* Train and perform inference with models created in different frameworks
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### How it works
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{: .no_toc }
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The premise is simple.
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1. **Get a model.** This can be trained from any framework that supports export/conversion to ONNX format. See the [tutorials](./tutorials) for some of the popular frameworks/libraries.
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2. **Load and run the model with ONNX Runtime.** See the [basic tutorials](./tutorials/api-basics) for running models in different languages.
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3. ***(Optional)* Tune performance using various runtime configurations or hardware accelerators.** There are lots of options here - see the [Performance section](./performance) as a starting point.
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Even without step 3, ONNX Runtime will often provide performance improvements compared to the original framework.
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ONNX Runtime applies a number of graph optimizations on the model graph then partitions it into subgraphs based on available hardware-specific accelerators. Optimized computation kernels in core ONNX Runtime provide performance improvements and assigned subgraphs benefit from further acceleration from each [Execution Provider](./execution-providers).
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---
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## ONNX Runtime for Training
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- [Large Model Training](./get-started/training-pytorch.md)
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- [On-Device Training](./get-started/training-on-device.md)
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