napari
cupy
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napari | cupy | |
---|---|---|
3 | 21 | |
2,053 | 7,753 | |
2.2% | 2.1% | |
9.7 | 9.9 | |
5 days ago | 6 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
napari
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Introspect type hints Pythonically in O(1) time.
Much thanks to @tlambert03 – who also authors Napari, a fast multidimensional image viewer in Python you might also enjoy.
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`nme` - package to simplify data persistence when upgrading data structures
I would like to share my python package nme. This package is for simplifying loading data from the older versions of code. It was initially created for napari plugins for improving science reproducibility, but I think that it may be useful for other projects.
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AUR python package and /usr/lib/python write permissions
It's napari. Those "assets" are some qt-recourses that need to be compiled from svg. I don't know the details on how this works. I wanted to understand if this is something I should try to solve with my PKGBUILD, before opening a PR and going down the rabbithole :)
cupy
- CuPy: NumPy and SciPy for GPU
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Keras 3.0
I did not expect anything interesting, but this is actually cool.
> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.
I suppose it's like https://cupy.dev/
- Progress on No-GIL CPython
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Fedora 40 Eyes Dropping Gnome X11 Session Support
What was the difference in runtime performance, and did you try CuPy?
https://github.com/cupy/cupy :
> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.
Projects using CuPy:
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How does one optimize their functions?
It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.
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Speed Up Your Physics Simulations (250x Faster Than NumPy) Using PyTorch. Episode 1: The Boltzmann Distribution
I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.
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ELI5: Why doesn't numpy work on GPUs?
u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/
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Help!!! Training neural net in vs code
Not sure how VS Code is relevant here as it's just you IDE, shouldn't have any influence on this. Now, seeing as you're using numpy (which has no gpu support), you could try and use something like CuPy in place of numpy. I'm not sure about the interoperability because I've never used this myself, but if you're lucky it could be as simple as just replacing all numpy calls with the same CuPy calls (or replacing all import numpy as np with import cupy as np ).
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What's the best thing/library you learned this year ?
Cupy replicates the numpy and scipy APIs but runs on the GPU.
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Making Python fast for free – adventures with mypyc
For that, you can use cupy[0], PyTorch[1] or Tensorflow[2]. They all mimic the numpy's API with the possibility to use your GPU.
[0] https://cupy.dev/
What are some alternatives?
glumpy - Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
mlcourse.ai - Open Machine Learning Course
Numba - NumPy aware dynamic Python compiler using LLVM
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
scikit-cuda - Python interface to GPU-powered libraries
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
bottleneck - Fast NumPy array functions written in C
Poetry - Python packaging and dependency management made easy
dpnp - Data Parallel Extension for NumPy