[pjmlp]

Not the OP, the community approach to performance, that rather rewrites code into C, still calling it Python (?), instead of being more supportive of ongoing JIT endeavours.

Yes Python is very dynamic, not more than Smalltalk, SELF or Common Lisp, all with quite good JIT engines.

[agumonkey]

That's my opinion, and coming from you (from what I get you're seasoned in many area of the computing field) it's not surprising. But the current mainstream is not really aware of all this. There's tiny python cult due to scipy et al.

[pjmlp]

Yep same wavelength here.

There is another thing, most of the major libraries where Python is used as DSL, written in a mix of C, C++ and Fortran, can be used in other languages just as well, nothing special about Python there other than lack of awareness of what everyone else is doing.

[_hzrk]

In the ML, DL world or in the physical simulators, people just compose a task and throw it to a CPU/GPU/TPU or a cluster of these and let it run for a long time. I don't see how Julia will be different for this kind of tasks. I understand that in Julia you solve the 2-language problem and all the goodies that multiple dispatch brings but the Python ecosystem progressed a lot in the past 2 years, now you have Numba JIT, Jax JIT, PyTorch JIT, XLA JIT and many other proprietary JITs that are not open-sourced. Since JAX (as an example) is mostly numpy and Python, you can leverage your existing knowledge instead of having to learn a fundamentally new paradigm. I would say that Python has many "specialised" JIT engines and it seems to work great for the community. Don't get me wrong, Julia is interesting, I can't deny that but I expect a huge adoption period for it. It can find its niche as C++ did for extremely high performance computing or Scala for Big Data (though Java is starting to replace many use cases). If you ask me now, I would say that the world converges around Java, C++ and Python when it comes to data, the old trio and it will remain this way for at least another decade.

[FridgeSeal]

> but the Python ecosystem progressed a lot in the past 2 years, now you have Numba JIT, Jax JIT, PyTorch JIT, XLA JIT and many other proprietary JITs that are not open-sourced.

Python has a bunch of specific use-case, non-interoperable limited JIT’s that you have to learn separately.

Not the case with Julia: you write some arbitrary code, it gets optimised, so much simpler. The Julia community did some cool things with Flux where complex and field-specific equations were dropped wholesale into neural-network definitions without having to rewrite anything. That sort of power is invaluable.

[pjmlp]

In what concerns my use cases, Java and .NET languages can make use of the same GPGPU libraries just as well.

I follow Python and Julia more as language geek than anything else.

Specially since ecosystems like CUDA have been polyglot since the early days.