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It's been awhile, but last time I was doing serious work in Julia things were a little janky. For example, the REPL would segfault sometimes if I Ctrl-C'ed duri
by Buttons840 4y ago
It's been awhile, but last time I was doing serious work in Julia things were a little janky. For example, the REPL would segfault sometimes if I Ctrl-C'ed during heavy computations. And Flux at first seems like it will work on any code, which seems amazing, but then you find out at runtime that one of the operations you used isn't supported and get a runtime error. PyTorch might not work on regular Python code, but at least I know the APIs provided by PyTorch will work, even though they are a subset of what can be done in regular Python.
Still, most things worked in Julia, and there have been many improvements since then so I suspect the few remaining rough spots are being smoothed out. In the future I will be happy if I get to work with Julia more.
- xiaodai 4y agoYeah. It's a chicken and egg thing. Imagine if resources applied to PyTorch was spent implementing in Julia. But then there's not enough users... so the cycle continues until one day Julia hits critical mass and a tipping point is reached.
- eastWestMath 4y agoI used to be very optimistic about Julia, but my enthusiasm has really cooled off the last year. I find JAX+jax_dataclasses gives me 90% of what I wanted from Julia (a nice monad library that is compatible with jit’ed code and the ability to cache compiled code would make it a solidly better language).
- staunton 4y agoI don't know whether the monad library is nice but caching compiles code is something they are making great progress on. Veraion 1.9 seems able to store most compiled code on disk.
- v3ss0n 4y agoIt was even close to death
- anst 4y agoIn a winner-takes-all finite world a tipping point may never happen...