StatsBase.jl VS diffrax

so finally I tried running Pkg.add(Pkg.PackageSpec(url="https://github.com/JuliaStats/StatsBase.jl", rev="v0.24.0")) but encountered an error saying in needed to download dependencies like DataStructures.

Well, out of the issues mentioned, the ones still open can be categorized as (1) aliasing problems with mutable vectors https://github.com/JuliaLang/julia/issues/39385 https://github.com/JuliaLang/julia/issues/39460 (2) not handling OffsetArrays correctly https://github.com/JuliaStats/StatsBase.jl/issues/646, https://github.com/JuliaStats/StatsBase.jl/issues/638, https://github.com/JuliaStats/Distributions.jl/issues/1265 https://github.com/JuliaStats/StatsBase.jl/issues/643 (3) bad interaction of buffering and I/O redirection https://github.com/JuliaLang/julia/issues/36069 (4) a type dispatch bug https://github.com/JuliaLang/julia/issues/41096

So if you avoid mutable vectors and OffsetArrays you should generally be fine.

As far as the argument "Julia is really buggy so it's unusable", I think this can be made for any language - e.g. rand is not random enough, Java's binary search algorithm had an overflow, etc. The fixed issues have tests added so they won't happen again. Maybe copying the test suites from libraries in other languages would have caught these issues earlier, but a new system will have more bugs than a mature system so some amount of bugginess is unavoidable.

Most of these seem to be about packages in the ecosystem (which, after clicking through all links, actually almost all got fixed in a very timely manner, sometimes already in a newer version of the packages than the author was using), not about the language itself. Other than that, the message of this seems to be "newer software has bugs", which yes is a thing..?

For example, the majority of issues referenced are specific to a single package, StatsBase.jl - which apparently was written before OffsetArrays.jl was a thing and thus is known to be incompatible:

> Yes, lots of JuliaStats packages have been written before offset axes existed. Feel free to make a PR adding checks.

https://github.com/JuliaStats/StatsBase.jl/issues/646#issuec...

Optimistix has high-level APIs for minimisation, least-squares, root-finding, and fixed-point iteration and was written to take care of these kinds of subroutines in Diffrax.

Diffrax (https://github.com/patrick-kidger/diffrax).

Here is the GitHub: https://github.com/patrick-kidger/optimistix

The elevator pitch is Optimistix is really fast, especially to compile. It

Sure. So I've got some PyTorch benchmarks here. The main take-away so far has been that for a neural ODE, the backward pass takes about 50% longer in PyTorch, and the forward (inference) pass takes an incredible 100x longer.

FWIW this worked for me. :D My full-time job is now writing JAX libraries at Google. Equinox for neural networks, Diffrax for differential equation solvers, etc.

It's relatively early days yet, but JAX is in the process of developing its nascent scientific computing / scientific machine learning ecosystem. Mostly because of its strong autodifferentiation capabilities, excellent JIT compiler etc. (E.g. to show off one of my own projects, Diffrax is the library of diffeq solvers for JAX.)

Diffrax - solving ODEs with Jax and computing it's derivatives automatically functools - love partial and lru_cache fastprogress - simpler progress bar than tqdm

At least prior to this announcement: JAX was much faster than PyTorch for differentiable physics. (Better JIT compiler; reduced Python-level overhead.)

E.g for numerical ODE simulation, I've found that Diffrax (https://github.com/patrick-kidger/diffrax) is ~100 times faster than torchdiffeq on the forward pass. The backward pass is much closer, and for this Diffrax is about 1.5 times faster.

It remains to be seen how PyTorch 2.0 will compare, or course!

Right now my job is actually building out the scientific computing ecosystem in JAX, so feel free to ping me with any other questions.

https://github.com/patrick-kidger/diffrax

Which are neural network and differential equation libraries for JAX.

[Obligatory I-am-googler-my-opinions-do-not-represent- your-employer...]

Autodifferentiable programming!

Neural networks are the famous example of this, of course -- but this can be extended to all of scientific computing. ODE/SDE solvers, root-finding algorithms, LQP, molecular dynamics, ...

These days I'm doing all my work in JAX. (E.g. see Equinox or Diffrax: https://github.com/patrick-kidger/equinox, https://github.com/patrick-kidger/diffrax). A lot of modern work is now based around hybridising such techniques with neural networks.

I'd really encourage anyone interested to learn how JAX works under-the-hood as well. (Look up "autodidax") Lots of clever/novel ideas in its design.