tmu VS cupy

Code: https://github.com/cair/tmu

Quick Read: https://www.marktechpost.com/2023/01/10/this-artificial-intelligence-ai-research-from-norway-introduces-tsetlin-machine-based-autoencoder-for-representing-words-using-logical-expressions/ Paper: https://arxiv.org/pdf/2301.00709.pdf Github: https://github.com/cair/tmu

Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to define other words like “coffee,” thus being human-understandable. I raise the question in the heading because our logical embedding performs competitively on several intrinsic and extrinsic benchmarks, matching pre-trained GLoVe embeddings on six downstream classification tasks. Thanks to my clever PhD student Bimal, we now have even more fun and exciting research ahead of us. Our long term research goal is, of course, to provide an energy efficient and transparent alternative to deep learning. You find the paper here: https://arxiv.org/abs/2301.00709 , an implementation of the Tsetlin Machine Autoencoder here: https://github.com/cair/tmu, and a simple word embedding demo here: https://github.com/cair/tmu/blob/main/examples/IMDbAutoEncoderDemo.py.

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/

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:

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.

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.

u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/

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 ).

Cupy replicates the numpy and scipy APIs but runs on the GPU.

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/