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TensorFlow.js and Browser Inference Print

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Models in the browser.

WHAT IT PROVIDES

Running models in a browser, using the device's processing.

WHAT THAT SUITS

Interactive features responding immediately Processing that should not leave the device Demonstrations and prototypes

WHAT IT COSTS

Model download, on every visit unless cached Processing on the visitor's device Battery consumption

WHY THAT MATTERS

A large model downloaded by every visitor is expensive for them and slow.

WHAT TO DO

Keep models small Cache aggressively Load only when the feature is used

WHAT TO NEVER DO

Load a model on page load for a feature most visitors never use.

WHAT ACCELERATION IS AVAILABLE

Graphics processing, where the browser and device support it.

WHAT TO HANDLE

Acceleration unavailable, falling back to slower processing Devices too slow to be usable

WHAT TO PROVIDE

An indication that processing is happening, and a way to cancel.

WHAT TO CONSIDER INSTEAD

Server-side inference, where the model is large or the device is modest.

WHAT TO MEASURE

Download size, and inference time on a mid-range phone.


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