JAX Comparisons
Comments primarily compare the discussed ML library or tool to Google's JAX, focusing on differences in performance, JIT compilation, functional style, and features relative to PyTorch, TensorFlow, and Numba.
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How does this compare against Google's JAX?
How different is this from JAX in practice?
How is PyTorch compares to JAX and its stack?
This seems to have similarities to JAX, ie defining as functions and getting some DAGs and auto vectorization
Which structural limits of TF2 and PyTorch were fixed via the Jax ecosystem?
You should give JAX a go.https://github.com/google/jax
What does this mean for JAX (light-weight ML library from Google Brain) vs Tensorflow (from Deepmind)?
Funfact: you can probably JIT compile that using JAX for an easy performance gain.
Don’t forget JAX! It’s my preferred library for “i want to write numpy but want it to run on gpu/tpu with auto diff etc”
Is this in direct competition with JAX from google ?