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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.
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---
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title: Large Model Training
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parent: Get Started
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nav_order: 12
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---
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# Get started with Large Model Training with ORTModule
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{: .no_toc }
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`ONNX Runtime Training`'s `ORTModule` offers a high performance training engine for models defined using the `PyTorch` frontend. `ORTModule` is designed to accelerate the training of large models without needing to change the model definition and with just a single line of code change (the `ORTModule` wrap) to the entire training script.
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Using the ORTModule class wrapper, ONNX Runtime runs the forward and backward pass of the training script using an optimized automatically-exported ONNX computation graph.
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## ORT Training Example
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In this example we will go over how to use ORT for Training a model with PyTorch.
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```sh
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# Installs the torch_ort and onnxruntime-training Python packages
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pip install torch-ort
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# Configures onnxruntime-training to work with user's PyTorch installation
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python -m torch_ort.configure
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```
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**Note**: This installs the default version of the `torch-ort` and `onnxruntime-training` packages that are mapped to specific versions of the CUDA libraries. Refer to the install options in [onnxruntime.ai](https://onnxruntime.ai).
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- Add ORTModule in the `train.py`
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```diff
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+ from torch_ort import ORTModule
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.
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.
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.
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- model = build_model() # Users PyTorch model
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+ model = ORTModule(build_model())
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```
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## Samples
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[ONNX Runtime Training Examples](https://github.com/microsoft/onnxruntime-training-examples) |