4 ms·

I like the language but I can't help but feel it missed the train and that the ergonomics improvements it offers are too small to switch over from Python.

pjmlp 2y ago
Depends on which train Julia folks want to board into.
- NeutralForest 2y ago
  It felt to me like they wanted to be the language for ML/DL, which they haven't achieved. They clearly have been working more towards scientific stuff + ML, all the differential equations and math packages are a testament to that (as well as the pharma stuff with Puma). I'm not aware of what the vision is currently tbh
  - affinepplan 2y ago
    I think one really good use case is complex simulations.
    - deleted 2y ago
      [deleted]
  - mbauman 2y ago
    The key for me — as someone who has been around for a long time and is at JuliaHub — is that Julia excels most at problems that don't already have an efficient library implementation. If your work is well-served by existing libraries, great! There's no need to compete against something that's already working well. But that's frequently not the case for modeling, simulation, differential equations, and SciML.
    - catgary 2y ago
      The ODEs stuff in Julia is nice, but I think diffusers/JAX is a reasonable backbone to copy over whatever you need from there. I do think Julia is doing will in stats and has gotten some mindshare from R in that regard. But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily (especially if you want to use the GPU). I do miss Tullio, though.
      - lagrange77 2y ago
        > But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily You mean in terms of the ODE stuff, Julia provides?
        catgary 2y ago
        Diffusers is pretty well done (I think the author was basically rewriting some Julia libraries and adapting them to JAX). I can’t imagine it being too hard to adapt most SciML ODE solvers. For simulations, JAX will choke on very “branchy” computations. But, honestly I’ve had very little success differentiating through those computations in the first place and they don’t run well on the GPU. Thus, I’m generally inclined to use wrappers around C++ (or ideally Rust) for those purposes (my use-case is usually some rigid-body dynamics style simulation).
        deleted 2y ago
        [deleted]
      - ssivark 2y ago
        But how does one get the composability of multiple dispatch? A problem with the Python world IME is the need to reinvent everything in Jax ecosystem, in the Pytorch ecosystem, etc -- because of fundamental language limitations. This is tedious, and feels like a lot of wasted effort. Another example: It's frustrating that Flax had to implement it's own "lifted" transformations instead of being able to just use jax transformations -- which makes it impossible to just slot a Flax model into a jax library that integrates ODEs. Equinox might be better on this front, but that means that all the models now need to be re-implemented in Equinox. The fragmentation and churn in the Python ecosystem is outrageous -- the only reason it doesn't collapse under its own weight is how much funding and manpower ML stakeholders are able to pour into the ecosystem. Given how much the ecosystem depends on that sponsored effort, the popular frameworks will likely prioritize ML applications, and corollary use cases will be second class citizens in case of design tradeoffs. Eg: framework overheads matter less when one is trying to use large NN models -vs- when one is trying to use small models, or other parametric approaches.
dv_dt 2y ago
It does feel like julia will not make the leap to displace python, but for a long time python offered too few improvements over perl, so its not completely out of the question.
- fnands 2y ago
  Yeah, at some point I was hoping it would. But after attending a day of JuliaCon last year: Julia is nowhere close to replacing Python, but it might put MATLAB in the ground. It looks like Julia has found a few niches though: HPC and numerical modelling among them.

pjmlp 2y ago

Depends on which train Julia folks want to board into.

NeutralForest 2y ago
It felt to me like they wanted to be the language for ML/DL, which they haven't achieved. They clearly have been working more towards scientific stuff + ML, all the differential equations and math packages are a testament to that (as well as the pharma stuff with Puma). I'm not aware of what the vision is currently tbh
- affinepplan 2y ago
  I think one really good use case is complex simulations.
  - deleted 2y ago
    [deleted]
- mbauman 2y ago
  The key for me — as someone who has been around for a long time and is at JuliaHub — is that Julia excels most at problems that don't already have an efficient library implementation. If your work is well-served by existing libraries, great! There's no need to compete against something that's already working well. But that's frequently not the case for modeling, simulation, differential equations, and SciML.
  - catgary 2y ago
    The ODEs stuff in Julia is nice, but I think diffusers/JAX is a reasonable backbone to copy over whatever you need from there. I do think Julia is doing will in stats and has gotten some mindshare from R in that regard. But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily (especially if you want to use the GPU). I do miss Tullio, though.
    - lagrange77 2y ago
      > But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily You mean in terms of the ODE stuff, Julia provides?
      - catgary 2y ago
        Diffusers is pretty well done (I think the author was basically rewriting some Julia libraries and adapting them to JAX). I can’t imagine it being too hard to adapt most SciML ODE solvers. For simulations, JAX will choke on very “branchy” computations. But, honestly I’ve had very little success differentiating through those computations in the first place and they don’t run well on the GPU. Thus, I’m generally inclined to use wrappers around C++ (or ideally Rust) for those purposes (my use-case is usually some rigid-body dynamics style simulation).
        deleted 2y ago
        [deleted]
    - ssivark 2y ago
      But how does one get the composability of multiple dispatch? A problem with the Python world IME is the need to reinvent everything in Jax ecosystem, in the Pytorch ecosystem, etc -- because of fundamental language limitations. This is tedious, and feels like a lot of wasted effort. Another example: It's frustrating that Flax had to implement it's own "lifted" transformations instead of being able to just use jax transformations -- which makes it impossible to just slot a Flax model into a jax library that integrates ODEs. Equinox might be better on this front, but that means that all the models now need to be re-implemented in Equinox. The fragmentation and churn in the Python ecosystem is outrageous -- the only reason it doesn't collapse under its own weight is how much funding and manpower ML stakeholders are able to pour into the ecosystem. Given how much the ecosystem depends on that sponsored effort, the popular frameworks will likely prioritize ML applications, and corollary use cases will be second class citizens in case of design tradeoffs. Eg: framework overheads matter less when one is trying to use large NN models -vs- when one is trying to use small models, or other parametric approaches.

dv_dt 2y ago

It does feel like julia will not make the leap to displace python, but for a long time python offered too few improvements over perl, so its not completely out of the question.

fnands 2y ago
Yeah, at some point I was hoping it would. But after attending a day of JuliaCon last year: Julia is nowhere close to replacing Python, but it might put MATLAB in the ground. It looks like Julia has found a few niches though: HPC and numerical modelling among them.