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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.
48 lines
2.8 KiB
Markdown
48 lines
2.8 KiB
Markdown
---
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title: On-Device Training
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parent: Get Started
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nav_order: 12
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---
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# On-Device Training with ONNX Runtime
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{: .no_toc }
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`On-Device Training` refers to the process of training a model on an edge device, such as mobile phones, embedded devices, gaming consoles, web browsers, etc. This is in contrast to training a model on a server or a cloud. Training on the device can be used for:
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- Personalization tasks, where the model needs to be trained on the user's data.
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- Federated learning tasks, where the model is locally trained on data that is distributed across multiple devices in an effort to build a more robust aggregated global model.
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- Improving data privacy and security, especially when working with sensitive data that cannot be shared with a server or a cloud.
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- Training locally (without impacting application functionality) when network connectivity is unreliable or limited.
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`ONNX Runtime Training` offers an easy way to efficiently train and infer ONNX models on edge devices. The training process is divided into two phases:
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- [the offline phase](#the-offline-phase)
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- [the training phase](#the-training-phase).
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## The Offline Phase
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In this phase, training artifacts are prepared on a server, cloud or a desktop that does not have access to user data. These artifacts can be generated by using the `ONNX Runtime Training`'s [artifact generation Python tools](./../api/python/on_device_training/training_artifacts.html) available in the Python package.
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Refer to the [installation instructions](./../install/index.md#offline-phase---prepare-for-training)
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## The Training Phase
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Once these artifacts are generated, they can be deployed to production scenarios on edge devices. `ONNX Runtime` offers a wide range of packages in multiple language bindings.
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Refer to the [installation instructions](./../install/index.md#training-phase---on-device-training) for a complete list of all language bindings.
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Once training on the edge device is complete, an inference-ready ONNX model can be generated on the edge device itself. This model can then be used with ONNX Runtime for inferencing.
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[comment]: <> (Learn more from the blogs. Links to the blogs go here.)
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## Installation
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Refer to the [installation instructions](./../install/index.md#install-for-on-device-training) for details on how to install for your scenario.
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## Building from Source
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Refer to the [build instructions](./../build/training.md#build-for-on-device-training) for details on how to build for your custom scenario.
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[comment]: <> (Learn more from the tutorials. Links to the demo and website tutorial go here.)
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[comment]: <> (Also link to the onnxruntime-training-examples repo goes here.)
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## Feature Request, Bug Report or Help Needed
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In case you need help, please open an [issue](https://github.com/microsoft/onnxruntime/issues/new?assignees=&labels=training&projects=&template=06-training.yml&title=%5BTraining%5D+).
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