datanecdote

As the document I linked to says, Jax autograd supports custom data types and custom gradients.

It’s honestly exhausting arguing with all you Julia boosters. You can down vote me to hell, I don’t care. I’m done engaging with this community.

You all are not winning over any market share from Python with your dismissive, arrogant, closed minded culture.

It is true that Jax cannot differentiate through C code. But it can differentiate through python code that was written to accept Numpy.

For Jax I believe this is false.

Jax is composable. In fact it’s a core design goal. Jax arrays implement the Numpy API. I routinely drop Jax arrays into other python libraries designed for Numpy. It works quite well. It’s not effortless 100% of the time but no library interop is (including Julia multiple dispatch).

I can introspect Jax. Until you wrap your function with jit(foo) it’s as introspectable as any other Python code, at least if I’m understanding what you mean by introspection.

Jax has implemented most of the Numpy functions, certainly most of the ones anyone needs to use on a regular basis. I rarely find anything missing. And if it is, I can write it myself, in python, and have it work seamlessly with the rest of Jax (autodiff, jit, etc)

How does Jax lose composability or introspection?

Lengthy, nuanced discussion about benchmarking between Turing devs and Stan devs.

When I look at google trends or redmonk rankings, Julia appears stable, not accelerating.

Thanks Viral. To be clear, I’m a python user who’s cheering for Julia, because I live the problems of python and do see the potential of Julia as a better path. But unfortunately I’m not prepared to be the early adopter (at least in my day job), and will wait until other, braver users have sanded off the rough edges. God speed and good luck.

> 2. I don't entirely follow this point. Perhaps using PyArrow's parser would be faster than what is timed here, but is that what the typical Python data science user would do?

I am a Python data science user. If data gets big enough such that loading time is a bottleneck, I use parquet files instead of CSV, and PyArrow to load them into pandas. It’s a one line change. The creator of Pandas is now leading the Arrow project. It’s very seamless. Don’t know if I’m typical but that’s me.

@Sukera

Fair, but, if I break up all the loops and if statements into functions, those functions still have “end”s

That was mostly meant as a joke, thus the “;-)”

I don’t really care much about syntax choices, but my small complaint about “end” is that it takes up a line which reduces the amount of business-logic code I can fit on one screen, especially if you ever get into lots of nested loops and conditionals.

Thanks. I watched the JuliaCon state of Julia presentation. As I wrote in my original post, I appreciate the investments the Julia core developers are making, that have improved but not eliminated this problem. I wish them luck.

Preach, brother.

I’m cautiously optimistic that JAX (or something like JAX) can save the python programming language from stagnation by essentially building a feature-complete reimplementation of the language with JIT and autograd baked into the core. I’m praying that Google diverts like 10% of TF’s budget to JAX.

That way I don’t have to learn to love a bunch of unnecessary semi colons and “end”s littering up my beautiful zero-indexed code ;-)