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jax | colorama | |
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82 | 8 | |
27,936 | 3,430 | |
4.0% | - | |
10.0 | 3.2 | |
2 days ago | 25 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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jax
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The Elements of Differentiable Programming
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.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
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...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
colorama
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[Newbie question] struggling with colour change on user input
Try using https://github.com/tartley/colorama, that should straighten out most low level problems. If you still have issues, you need to adjust your color scheme in pycharm.
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New IP Osint Tool!
Pyshark: https://github.com/KimiNewt/pyshark Requests: https://github.com/psf/requests Colorama:https://github.com/tartley/colorama
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Terminology: a simpler alternative to Colorama
from colorama import Fore, Style # Colorama doesn't support Underlined text because it # is struggling to make it work for windows users # https://github.com/tartley/colorama/issues/38 print(Fore.RED + Style.BRIGHT + 'Danger' + Style.NORMAL)
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Terminology: a much simpler alternative to python's Colorama library
There are obviously a lot of libraries in python that color text, but I never loved their syntaxes, so I ended up creating this one a few years back. For comparison, this is how you print a red, bold, underlined string in **terminology**\: from terminology import in_red print(in_red('Danger').in_bold().underlined()) and this is how you do it with the other alternatives: **colorama** from colorama import Fore, Style # Colorama doesn't support Underlined text because it # is struggling to make it work for windows users # https://github.com/tartley/colorama/issues/38 print(Fore.RED + Style.BRIGHT + 'Danger' + Style.NORMAL)
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Get Colored Console Output In Python Using Colorama
The link to its github repository is this: colorama.
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Rendering color in vscode console output
Escape sequences may vary depending on the terminal you use. You can try some package like Colorama which escapes those sequences and gives you the correct color in Windows. There is also Blessings.
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Build CLI with Hype
As I mentioned earlier, Hype doesn't rely on any third-party library but then there are some plugins that third-party library powered Hype. For example, the color printing that is powered by colorama.
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What are some of your gold standard Python open source repos you discovered here or elsewhere that have very high quality, commented and understandable code that use best practices?
Looks really cool! Way more functionality than I'd ever need. I've been using colorama for color coding info, warning, and error messages in my Python projects. No complaints. From the feature list of rich it sounds like it probably pulls in a lot more dependencies?
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
functorch - functorch is JAX-like composable function transforms for PyTorch.
asciimatics - A cross platform package to do curses-like operations, plus higher level APIs and widgets to create text UIs and ASCII art animations
julia - The Julia Programming Language
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
click - Python composable command line interface toolkit
Cython - The most widely used Python to C compiler
clint - Python Command-line Application Tools
jax-windows-builder - A community supported Windows build for jax.
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.