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Is it actually completely painless? YMMV, but for me I found that there were many unsupported parts of Numpy I had to work around which meant I was effectively

• garyrob 5y ago
  It just happened wasn't using Numpy except in the most basic possible ways. I had hand-written algorithms in python. That's where it shines. So, yes YMMV! It worked really well for me.
• nextos 5y ago
  I think Numba has some pain points. I have also encountered issues with multi-dimensional arrays. The beauty of Julia is that relatively naive Ruby-like code is already quite quick. And if you implement inner loops in an imperative way, with an eye towards not generating excessive allocations, it can approach C++ speed while still being nice high-level code that is close to mathematics or business logic. Besides, the other strong point of Julia is composability. The ecosystem is made up by lots of small libraries that can interact in ways the original designers did not expect or plan for. In contrast, Python has exceptional libraries, but they tend to be big monoliths. The problem with Julia right now is that some libraries are not sufficiently mature. For example, there's no mature native replacement for XLA or PyTorch. I know about Flux, but it's nowhere close if you wanna create, say, a large transformer. Or say you are working with GLMs. GLM.jl is nowhere close to R. Some other Julia libraries represent the state of the art, though. I just can't wait to get all foundations complete! It's a really promising space for probabilistic and differentiable programming.