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JAX – Augments numpy and Python code with function transformations (2019)

65 points by auraham 4y ago

l1n 4y ago
Needs a [2019]
pestatije 4y ago
> JAX is a Python library which augments numpy and Python code with function transformations which make it trivial to perform operations common in machine learning programs.
- tpoacher 4y ago
  why?
  - Q6T46nT668w6i3m 4y ago
    NumPy doesn’t provide automatic differentiation or heterogenous compute.
funks_ 4y ago
> JAX is just numpy (mostly) Or you could say JAX is a DSL that looks like numpy to trick Python programmers into using a compiled functional programming language. ;)
- fnands 4y ago
  Shhh, don't tell them! ;-)
- bouchard 4y ago
  I've recently started using Taichi (https://taichi-lang.org/ https://taichi-lang.org/) for numerical codes and the fact it doesn't try to trick you into thinking it's numpy is a nice "feature". ;)
thanatropism 4y ago
Such an inciting title. I "know" Jax; I also know Jack from Legal. I'd been using Jax and Numpyro like learning from tutorials pretending it's numpy and then they brought the pros from Dover who knew all kinds of insane advanced techniques. I was hoping for a tutorial on those.
- imurray 4y ago
  While I didn't like the form of the title, I assumed it was referring to other tutorials with similar titles. Indeed there exists "you don't know JS" and "you don't know bash".
fernandotakai 4y ago
>We'll use a neural network with a single hidden layer with 3 neurons and a hyperbolic tangent nonlinearity, trained with the cross-entropy loss via stochastic gradient descent. i gotta be super honest, this reads like a sentence written by ChatGPT.
- eternalban 4y ago
  I have be super honest too, as your comment says more about your English skills than whether writing a coherent sentence is beyond the reach of humans. Nothing in that sentence says machine generated. But whoever wrote it thinks clearly and precisely, that much is obvious.
Q6T46nT668w6i3m 4y ago
I love JAX … but JAX, frustratingly, doesn’t obey NumPy promotion semantics! Enforcing double precision is understandable from a performance perspective but it breaks NumPy compatibility and frequently introduces subtle and difficult to debug numerical bugs. Nevertheless, JAX has encouraged so much progress in ML software engineering, e.g., I’ve been cribbing from diffrax and torchopt for my PyTorch work.

65 points by auraham 4y ago

l1n 4y ago

Needs a [2019]

> JAX is a Python library which augments numpy and Python code with function transformations which make it trivial to perform operations common in machine learning programs.

tpoacher 4y ago
why?
- Q6T46nT668w6i3m 4y ago
  NumPy doesn’t provide automatic differentiation or heterogenous compute.

funks_ 4y ago

> JAX is just numpy (mostly) Or you could say JAX is a DSL that looks like numpy to trick Python programmers into using a compiled functional programming language. ;)

fnands 4y ago
Shhh, don't tell them! ;-)
bouchard 4y ago
I've recently started using Taichi (https://taichi-lang.org/ https://taichi-lang.org/) for numerical codes and the fact it doesn't try to trick you into thinking it's numpy is a nice "feature". ;)

thanatropism 4y ago

Such an inciting title. I "know" Jax; I also know Jack from Legal. I'd been using Jax and Numpyro like learning from tutorials pretending it's numpy and then they brought the pros from Dover who knew all kinds of insane advanced techniques. I was hoping for a tutorial on those.

imurray 4y ago
While I didn't like the form of the title, I assumed it was referring to other tutorials with similar titles. Indeed there exists "you don't know JS" and "you don't know bash".

fernandotakai 4y ago

>We'll use a neural network with a single hidden layer with 3 neurons and a hyperbolic tangent nonlinearity, trained with the cross-entropy loss via stochastic gradient descent. i gotta be super honest, this reads like a sentence written by ChatGPT.

eternalban 4y ago
I have be super honest too, as your comment says more about your English skills than whether writing a coherent sentence is beyond the reach of humans. Nothing in that sentence says machine generated. But whoever wrote it thinks clearly and precisely, that much is obvious.

Q6T46nT668w6i3m 4y ago

I love JAX … but JAX, frustratingly, doesn’t obey NumPy promotion semantics! Enforcing double precision is understandable from a performance perspective but it breaks NumPy compatibility and frequently introduces subtle and difficult to debug numerical bugs. Nevertheless, JAX has encouraged so much progress in ML software engineering, e.g., I’ve been cribbing from diffrax and torchopt for my PyTorch work.