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11 lines
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11 lines
1.2 KiB
ReStructuredText
Overview
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Learning on the edge 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 edge is useful when the data is sensitive and cannot be shared with a server or a cloud. It is also useful for the task of personalization where the model needs to be trained on the user's device.
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`onnxruntime-training` offers an easy way to efficiently train and infer a wide range of ONNX models on edge devices. The training process is divided into two phases:
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- The offline phase: In this phase, training artifacts are prepared on a server, cloud or a desktop. These artifacts can be generated by using the `onnxruntime-training`'s :doc:`artifact generation python tools<training_artifacts>`.
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- The training phase: Once these artifacts are generated, they can be deployed on an edge device. The onnxruntime-training's :doc:`training API<training_api>` can be used to train a model on the edge device.
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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 for inferencing with onnxruntime.
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