One of the things I really like about PyTorch is that it doesn't break with every update or deletion. That's why I switched from TensorFlow to PyTorch. I can still run the code I wrote three years ago in PyTorch on the latest version.

It's very backward compatible, and it's also very simple to use. It's not overly technical, and the flow is pretty intuitive.

And now that PyTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily.

The most valuable feature would be the solution’s performance. The product is more advanced than the other libraries that I have used. Since every functionality is production-ready, I can easily write code. I don't have to rewrite the code for production. It has production-ready code from the start. The tool is very user-friendly. It took us a week to learn how to use it. It's straightforward to learn.

The framework of the solution is valuable.

The best feature is that it allows us to do batched mode, distributed data parallelism, and model parallelism. This enables distributed training, and the overall API interface is quite simpler compared to a data flow API. 

It makes sense both when using it yourself and when teaching it to more junior team members. It's much easier to discuss.  

Additionally, high-level APIs like Hugging Face simplify building on top of PyTorch. If you want to do things from scratch, the relative learning curve is simpler compared to Tensorflow, but beginners still need some help.

I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code. It's one of the easiest tools and takes the hint on its own. I was quite happy about its existence.

Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful.