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jax | dalle-playground | |
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82 | 35 | |
27,936 | 2,763 | |
4.0% | - | |
10.0 | 3.2 | |
1 day ago | 4 months ago | |
Python | JavaScript | |
Apache License 2.0 | MIT 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
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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
dalle-playground
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Discord bot with a locally-hosted SD backend.
Built on dalle-playground because it is simple and I like it.
- Neural photo engine with Intel compute stick?
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Free/open-source AI Text-To-Image Models that can be run on AWS?
[1] https://github.com/saharmor/dalle-playground
- ai_irl
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Running Dall-e mini on Windows? (Or: Are there any equivalent text-to-image AI's I can run on a windows PC with a 2080 TI?)
If you decide to abandon the idea of running locally and want to run in the cloud instead, https://github.com/saharmor/dalle-playground has a Google Colab notebook that's relatively easy to run (although Google Colab's free tier is relatively limited).
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
Next, we want the code in the https://github.com/hrichardlee/dalle-playground repo, and we want to construct a pip environment from the backend/requirements.txt file in that repo. We were almost able to use the saharmor/dalle-playground repo as-is, but we had to make one change to add the jax[cuda] package to the requirements.txt file. In case you haven’t seen jax before, jax is a machine-learning library from Google, roughly equivalent to Tensorflow or PyTorch. It combines Autograd for automatic differentiation and XLA (accelerated linear algebra) for JIT-compiling numpy-like code for Google’s TPUs or Nvidia’s CUDA API for GPUs. The CUDA support requires explicitly selecting the [cuda] option when we install the package.
- Dream's over guys...
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How to run DALLE-2 locally
Is their any way to run DALLE-2 inside of a docker container similarly to this DALLE-PLAYGROUND repo on github?
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How difficult would it be to set up your own DALL-E (mini/mega) API for side-projects?
I know there are open source projects like dalle-playground. Would it literally be enough to host this app on an EC2 instance with the mini model?
- an AI image generator capable of taking a prompt and making it come to life.
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
dalle-mini - DALL·E Mini - Generate images from a text prompt
functorch - functorch is JAX-like composable function transforms for PyTorch.
Dannjs - Easy to use Deep Neural Network Library for JavaScript.
julia - The Julia Programming Language
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
min-dalle - min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
Cython - The most widely used Python to C compiler
nano-neuron - 🤖 NanoNeuron is 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn"
jax-windows-builder - A community supported Windows build for jax.
pollinations - Generate Art