PyTorch is the bridge across CUDA moat for AMD & other hardware vendors

[Marees]

The PyTorch Drawbridge​

In the HPC sector, CUDA-enabled applications rule the GPU-accelerated world. Porting codes can often realize a speed-up of 5-6x when using a GPU and CUDA. (Note: Not all codes can achieve this speed up, and some may not be able to use the GPU hardware.) However, in GenAI, the story is quite different.

Initially, TensorFlow was the tool of choice for creating AI applications using GPUs. It works both with CPUs and was accelerated with CUDA for GPUs. This situation is changing rapidly.

An alternative to TensorFlow is PyTorch, an open-source machine learning library for developing and training neural network-based deep learning models. Facebook’s AI research group primarily develops it.

In a recent blog post by Ryan O’Connor, a Developer Educator at AssemblyAI notes that the popular site HuggingFace, (that allows users to download and incorporate trained and tuned state of the art models into application pipelines with just a few lines of code), 92% of models available are PyTorch exclusive.

In addition, as shown in Figure One, a comparison of Machine Learning papers shows a significant trend toward PyTorch and away from TensorFlow.

( Figure One: Percentage of papers that utilize PyTorch, TensorFlow, or another framework over time, with data aggregated quarterly, from late 2017, Source: assemblyai.com. )


Of course, underneath PyTorch are calls to CUDA, but that is not required because PyTorch insulates the user from the underlying GPU architecture. There is also a version of PyTorch that uses AMD ROCm, an open-source software stack for AMD GPU programming. Crossing the CUDA moat for AMD GPUs may be as easy as using PyTorch.

https://www.hpcwire.com/2023/10/05/how-amd-may-get-across-the-cuda-moat/

 

[uOpt]

Remind me I still need to port my pet project from TensorFlow to Torch.

I just wish all this stuff wasn't that Python-heavy.