cupy
LavaMoat
cupy | LavaMoat | |
---|---|---|
21 | 16 | |
7,787 | 819 | |
1.2% | 2.1% | |
9.9 | 9.8 | |
5 days ago | 4 days ago | |
Python | JavaScript | |
MIT 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.
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" โ 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 โ 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/
LavaMoat
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Ledger's NPM account has been hacked
Just yesterday I watched a talk [0] at WarsawJS about LavaMoat [1], a set of tools to protect against malicious behaviour from npm dependencies. Guess itโs time to look into it deeper.
[0]: https://naugtur.pl/pres3/lava/2023end.html
[1]: https://github.com/LavaMoat/LavaMoat
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Dozens of malicious PyPI packages discovered targeting developers
You are basically talking about Lavamoat. It provides tooling and policies for SES, which aims to make it into standards.
https://github.com/LavaMoat/LavaMoat
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Supply chain security - prevent, not avoid
Enter: lavamoat. https://github.com/LavaMoat/LavaMoat
- LavaMoat: Tools for sandboxing your dependency graph
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Deno.js in Production. Key Takeaways.
You should check out Lavamoat: https://github.com/LavaMoat/LavaMoat
It attempts to do what you're essentially describing. It was built by the MetaMask team, where supply chain attacks are an obviously huge risk.
I've spent some time trying to get it working in an app, but haven't been able to get it all the way working. It's still pretty beta and not well documented.
- Node.js packages don't deserve your trust
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How to respond to growing supply chain security risks?
And it is happening right now. Github is opening the GitHub Advisory Database to community submissions. Awesome community NodeSecure builds cool things like scanner and js-x-ray. There are also lockfile-lint, LavaMoat, Jfrog-npm-tools (and I am sure there is more).
- On node-ipc and the importance of trusting trust
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NPM package compromised by author: erases files on RU / BY computers on install
There is a proposal to add OCAPs on a language level in TC39[0]. There is already a drop-in implementation which already works in both Nodejs and browsers[1].
As a developer who wants to sandbox your own (recursive) dependencies, this is made accessible today in Lavamoat[2]. Basically a package or app can provide a policy manifest specifying which capabilities (e.g. network or filesystem access) should be granted for each dependency. Also comes with a tool that will auto-generate a starting point from your existing dependency tree.
IMO this is the future. Currently it does come with a performance penalty but hopefully this idea will catch on and make it into runtime implementations.
Lavamoat is still marked as "preprod" on npm but talking to the author it's a matter of days or weeks until the first stable release.
[0]: https://news.ycombinator.com/item?id=30703817
[1]: https://github.com/endojs/endo/tree/master/packages/ses
[2]: https://github.com/LavaMoat/LavaMoat
- Node runtime that sandboxes all NPM dependencies by default
What are some alternatives?
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
metamask-extension - :globe_with_meridians: :electric_plug: The MetaMask browser extension enables browsing Ethereum blockchain enabled websites
Numba - NumPy aware dynamic Python compiler using LLVM
create-vue - ๐ ๏ธ The recommended way to start a Vite-powered Vue project
scikit-cuda - Python interface to GPU-powered libraries
vue-cli - ๐ ๏ธ webpack-based tooling for Vue.js Development
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
cli - the package manager for JavaScript
bottleneck - Fast NumPy array functions written in C
handlebars-helpers - 188 handlebars helpers in ~20 categories. Can be used with Assemble, Ghost, YUI, express.js etc.
dpnp - Data Parallel Extension for NumPy
EventSource - a polyfill for http://www.w3.org/TR/eventsource/