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TF2JAX

As explained in Deep Learning with JAX

An experimental library for converting TensorFlow functions or graphs into JAX functions.

TF2JAX is an experimental library for converting TensorFlow functions or graphs into JAX functions, so existing TensorFlow models can be reused and fine-tuned in JAX codebases. section 2.8 section 12.1.3

It sits among tools that move models across framework boundaries, alongside JAX ONNX Runtime and framework-unifying systems such as Keras 3 and Ivy. section 2.8 section 12.1.3

Cross-framework reuse

A TensorFlow model can be converted, then continued in JAX rather than rebuilt from scratch. section 2.8 section 12.1.3

How Deep Learning with JAX covers it

How deep it goes
Defines the conversion at a high level and stops short of implementation details or conversion rules.
Examples
Uses TensorFlow functions or graphs and reused TF models as the concrete teaching examples.
What it assumes
Assumes TensorFlow and JAX familiarity, then introduces TF2JAX as a bridge between them.
What you can do after
Apply converted TensorFlow models in JAX codebases for reuse and fine-tuning.
How it connects
Links TensorFlow, JAX, JAX ONNX Runtime, Keras 3, and Ivy.
Cover of Deep Learning with JAX
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with JAX. The section links point to the parts of the book it draws on.