**TF2JAX and jax2tf.call_tf are both tools designed for interoperability between TensorFlow and JAX, but they serve different purposes and have distinct characteristics in terms of performance and usability.
**TF2JAX is a library that converts TensorFlow functions and models into JAX functions, allowing users to reuse and fine-tune existing TensorFlow models within JAX codebases. This conversion enables JAX users to apply JAX transforms like `jit`, `grad`, and `vmap` to the converted functions. TF2JAX supports a subset of TensorFlow ops and is particularly useful for debugging and introspection due to its transparent conversion process[1][5]. However, it may not support all TensorFlow functionalities, and performance can vary depending on the specific ops used.
**jax2tf.call_tf, on the other hand, allows JAX functions to call TensorFlow functions directly. This approach supports all TensorFlow ops by deferring to TensorFlow during eager execution and to XLA for compiled code. It is beneficial for using TensorFlow libraries or reloading TensorFlow SavedModels within JAX. However, it only supports a limited set of JAX transforms (`jit`, `grad`, `pmap`, and `remat`), and functions appear as a "black box" to JAX, limiting further transformations like `vmap`[1][3].
In terms of performance, TF2JAX can potentially offer better performance for operations that are well-supported by JAX, especially when combined with JAX's `jit` compilation. However, jax2tf.call_tf might be slower due to the overhead of calling TensorFlow functions from JAX, although it provides comprehensive support for all TensorFlow ops.
Regarding usability, TF2JAX is more suitable for users who want to integrate TensorFlow models into JAX workflows while leveraging JAX's advanced features like `vmap`. jax2tf.call_tf is more convenient for users who need to utilize specific TensorFlow libraries or models directly within JAX without converting them.
Overall, the choice between TF2JAX and jax2tf.call_tf depends on whether you prioritize the ability to leverage JAX's advanced features on converted TensorFlow models or need to seamlessly integrate TensorFlow functionalities into your JAX workflow.
Citations:[1] https://github.com/google-deepmind/tf2jax
[2] https://www.reddit.com/r/MachineLearning/comments/1b08qv6/d_is_it_worth_switching_to_jax_from/
[3] https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md
[4] https://discuss.ai.google.dev/t/is-google-replacing-tensorflow-with-jax/18015
[5] https://modelzoo.co/model/tf2jax
[6] https://stackoverflow.com/questions/66711706/jax-jit-and-dynamic-shapes-a-regression-from-tensorflow
[7] https://stackoverflow.com/questions/70356126/is-there-a-module-to-convert-a-tensorflow-nn-to-jax
[8] https://livebook.manning.com/book/deep-learning-with-jax/chapter-2/v-11/