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PyTorch for Beginners: 7 Good Practices to Improve Your Model Development
Latest   Machine Learning

PyTorch for Beginners: 7 Good Practices to Improve Your Model Development

Last Updated on January 29, 2024 by Editorial Team

Author(s): Ruite Xiang

Originally published on Towards AI.

Build a Strong PyTorch Foundation and Create Reliable Machine Learning Models.

As a beginner, you want a list of tips or good practices because you will make fewer mistakes, have a better starting point, and improve faster.

But the information is often scattered around blog posts, YouTube, and GitHub, and it would take you ages to filter them. I wish I had a list like this when I started, which is why I did it for you!

Image by Freepik

I compiled a list of 7 good practices from my own experience and many other sources.

However, when I say beginner I mean that you should have some basic notions about programming and you want to get familiar with the basics of PyTorch as well.

The reason is simple, you can run your script on any machine without changing anything in the code.

You define what device you can use (CPU/GPU) at the beginning and move your tensors, models, etc., there

import torchdevice = "cuda" if torch.cuda.is_available() else "cpu"torch.ones(2,3, device=device)

There are many areas where reproducibility is important like in research, finance, robotics (including autonomous vehicles), healthcare, etc.

But even outside of these areas, having reproducible code accelerates experimentation, you know randomness is not the reason your performance has changed, and debugging, easier to reproduce the error.

Some people think that… Read the full blog for free on Medium.

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