cunumeric
NNfSiX
cunumeric | NNfSiX | |
---|---|---|
9 | 46 | |
595 | 1,348 | |
0.0% | - | |
8.5 | 0.0 | |
1 day ago | 7 months ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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cunumeric
- Announcing Chapel 1.32
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Is Parallel Programming Hard, and, If So, What Can You Do About It? [pdf]
I am biased because this is my research area, but I have to respectfully disagree. Actor models are awful, and the only reason it's not obvious is because everything else is even more awful.
But if you look at e.g., the recent work on task-based models, you'll see that you can have literally sequential programs that parallelize automatically. No message passing, no synchronization, no data races, no deadlocks. Read your programs as if they're sequential, and you immediately understand their semantics. Some of these systems are able to scale to thousands of nodes.
An interesting example of this is cuNumeric, which allows you to take sequential Python programs that use NumPy, and by changing one line (the import statement), run automatically on clusters of GPUs. It is 100% pure awesomeness.
https://github.com/nv-legate/cunumeric
(I don't work on cuNumeric, but I do work on the runtime framework that cuNumeric uses.)
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GPT in 60 Lines of NumPy
I know this probably isn't intended for performance, but it would be fun to run this in cuNumeric [1] and see how it scales.
[1]: https://github.com/nv-legate/cunumeric
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Dask – a flexible library for parallel computing in Python
If you want built-in GPU support (and distributed), you should check out cuNumeric (released by NVIDIA in the last week or so). Also avoids needing to manually specify chunk sizes, like it says in a sibling comment.
https://github.com/nv-legate/cunumeric
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Julia is the better language for extending Python
Try dask
Distribute your data and run everything as dask.delayed and then compute only at the end.
Also check out legate.numpy from Nvidia which promises to be a drop in numpy replacement that will use all your CPU cores without any tweaks on your part.
https://github.com/nv-legate/legate.numpy
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Learning more about HPC as a python guy
Something for the HPC tools category: https://github.com/nv-legate/legate.numpy
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Unifying the CUDA Python Ecosystem
You might be interested in Legate [1]. It supports the NumPy interface as a drop-in replacement, supports GPUs and also distributed machines. And you can see for yourself their performance results; they're not far off from hand-tuned MPI.
[1]: https://github.com/nv-legate/legate.numpy
Disclaimer: I work on the library Legate uses for distributed computing, but otherwise have no connection.
- Legate NumPy: An Aspiring Drop-In Replacement for NumPy at Scale
NNfSiX
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Are there any books I should read to learn machine learning from scratch?
I've been rather enjoying "Neural Networks from Scratch" (https://nnfs.io/)
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Ask HN: Those learning about neural networks, what do you find most difficult?
I haven't gotten super deep into it yet, but https://nnfs.io/ has been good in my opinion. The book slowly replaces written and explained code with numpy equivalents to keep the examples fast. Plus the accompanying animations are also useful. I would be curious what others think on it too.
- Gutes Einführungsbuch zu KI
- [Deep Learning] Neural Networks from Scratch in Python
- What do I get a programming obsessed high school boy for his birthday? I actually need advice
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GPT in 60 Lines of NumPy
For those curious to writing "gradient descent with respect to some loss function" starting from an empty .py file (and a numpy import, sure), can't recommend enough Harrison "sentdex" Kinsley's videos/book Neural Networks from Scratch in Python [1].
[1] https://youtu.be/Wo5dMEP_BbI?list=PLQVvvaa0QuDcjD5BAw2DxE6OF... https://nnfs.io
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Ask HN: What are the foundational texts for learning about AI/ML/NN?
Not sure if foundational (quite a tall order in such a fast-moving field), but for sure a nice introduction into neural networks, and even mathematics in general (because it's nice to see numbers in action beyond school-level algebra):
Harrison Kinsley, Daniel Kukiela, Neural Networks from Scratch, https://nnfs.io, https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0Qu...
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Ask HN: How to get back into AI?
Have you had a look at https://nnfs.io/ ? I bought the book and am gearing up to start working through it, I would be interested to know your thoughts. Generally I want to chart a personal curriculum from data engineer to practical application of modern AI to real business problems.
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Programming an AI as a beginner
You can check out Neural Networks from Scratch in Python for an introduction to neural networks, which can be used for image classification. Please be forewarned that you'll need the mathematics necessary to read through this book - however, I'm assuming that since you've selected writing such an algorithm(s) in Python for your final school project that you're aware of such.
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Moved to amd today and holy it's amazing
I am planning on working my way through Neural Networks From Scratch (https://nnfs.io/) in a few months just to build my understanding. After that I'm hoping to be able to figure out the best path for a couple of projects I have in mind.
What are some alternatives?
cupy - NumPy & SciPy for GPU
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
ML-From-Scratch - Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
CUDA.jl - CUDA programming in Julia.
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
numba - NumPy aware dynamic Python compiler using LLVM
deepnet - Educational deep learning library in plain Numpy.
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
grcuda - Polyglot CUDA integration for the GraalVM
ProjectOne - The project is to build a neural network from scratch. The motivation for this project is from nnfs.io a website build by @Sentdex. Nnfs.io is actually meant for a book that teaches the fundamentals of neural network and help us to build our own network. Let's build a new neural network where we can learn the fundamentals and make a great hands-on work space for aspiring machine learning engineers and the GitHub community