jax VS julia

localscope is a small Python package that disassembles functions to check if they access global variables they shouldn't. I wrote this a few years ago to detect scope bugs which are common in Jupyter notebooks. It's recently come in handy writing jax code (https://github.com/jax-ml/jax) because it requires pure functions. Thought I'd share.

If you like high-performance array programming a la "numpy with JIT" I suggest looking at JAX. It's very suitable for general numeric computing (not just ML) and a very mature ecosystem.

https://github.com/jax-ml/jax

Nope, changing graph shape requires recompilation: https://github.com/google/jax/discussions/17191

I found a GitHub issue that seemed similar (missing ptxas) and saw a suggestion to install nvidia-cuda-toolkit. Alright: but that exploded the container size from 6.5 GB to 12.13 GB … unacceptable 😤 (Incidentally, this is too large for Cloud Shell to build on its limited persistent disk.)

The dual numbers exist just as surely as the real numbers and have been used well over 100 years

https://en.m.wikipedia.org/wiki/Dual_number

Pytorch has had them for many years.

https://pytorch.org/docs/stable/generated/torch.autograd.for...

JAX implements them and uses them exactly as stated in this thread.

https://github.com/google/jax/discussions/10157#discussionco...

As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.

On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.

Source: https://github.com/google/jax/discussions/5199#discussioncom...

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

In this post, I’m exploring dev tools for data scientists, specifically Julia and Pluto.jl. I interviewed Mandar, a data scientist and software engineer, about his experience adopting Pluto, a reactive notebook environment similar to Jupyter notebooks. What’s different about Pluto is that it’s designed specifically for Julia, a programming language built for scientific computing and machine learning.

https://github.com/JuliaLang/julia/issues/57483 yes, yes it should.

Julia Seasons of Contributions (JSoC)

_ __ _(_)_ | Documentation: https://docs.julialang.org (_) | (_) (_) | _ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 1.11.2 (2024-12-01) _/ |\__'_|_|_|\__'_| | Official https://julialang.org/ release |__/ | julia>

The paper examines the current state of the Julia programming language for scientific machine learning (SML). Julia is a relatively new language that is designed to be fast, easy to use, and well-suited for scientific and numerical computing.

Download and Install Julia: Head over to https://julialang.org/ and download the appropriate installer for your operating system. Follow the installation instructions.

The above two steps is only for getting warm up, now you need to start coding on a programming language. Most of the AI community uses Python and there are other programming languages like Julia which is similar to python but it is faster than python, R used for statistical analysis and data visualization. Just try to learn one programming language with the Data Structure and Algorithm(DSA) and Object Oriented Programming System (OOPS) concepts.