---
title: On-Device Training
parent: Get Started
nav_order: 11
---

# On-Device Training with ONNX Runtime
{: .no_toc }
`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:

- Personalization tasks, where the model needs to be trained on the user's data.
- 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.
- Improving data privacy and security, especially when working with sensitive data that cannot be shared with a server or a cloud.
- Training locally (without impacting application functionality) when network connectivity is unreliable or limited.


`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:
- [the offline phase](#the-offline-phase)
- [the training phase](#the-training-phase).

## The Offline Phase
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.

Refer to the [installation instructions](./../install/index.md#offline-phase---prepare-for-training)

## The Training Phase
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.

Refer to the [installation instructions](./../install/index.md#training-phase---on-device-training) for a complete list of all language bindings.

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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## Installation

Refer to the [installation instructions](./../install/index.md#install-for-on-device-training) for details on how to install for your scenario.

## Building from Source

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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## Feature Request, Bug Report or Help Needed

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+).