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Sorry, can you clarify on what you think is a purely functional abstraction? Flux is incredibly flexible and can do all sorts of things that are not limited to

• chillee 7y ago
  Super late reply, so it's likely you won't see this... (Too bad HN doesn't notify on replies). I'm not complaining about Flux in general, I'm talking about the specific example (the UNet) he brought up that he uses to claim that Julia is so elegant. Can you elaborate on what Flux can do that Pytorch can't?
  • ChrisRackauckas 7y ago
    At this point, the DiffEqFlux neural differential equation library fits the neural ODE example from the original paper in 29 seconds [1]. The forward pass of torchdiffeq on trivial ODEs without neural networks takes 47 seconds [2] (and of course adding in neural networks makes it a lot more expensive). This is a massive real-world difference. It means that the Julia packages are building animations by looking at real-time fitting plots, while it's a hours long ordeal in PyTorch. Being able to use optimized packages instead of hardcoding a simple version of things really pays off in the long run, and here using a real ODE solver suite is not a small difference but rather it's multiple orders of magnitude. That's the real benefit of differentiable programming. [1] https://github.com/JuliaDiffEq/DiffEqFlux.jl#training-a-neural-ordinary-differential-equation [2] https://gist.github.com/ChrisRackauckas/cc6ac746e2dfd285c28e0584a2bfd320