On the production side of things, having more production tooling frameworks would be helpful. TensorFlow has a lot of elaborate frameworks e.g. for serving models, and that's one area where PyTorch could improve.

I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques. I would also like to see some improvement in parallel processing. We can take advantage of the GPU and compute it.

The training of the models could be faster. However, with PyTorch, modern training becomes a bit slower because it is within the models at Python.

PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex.

So, it is somewhat complicated for a beginner to learn how to use PyTorch.

If you refer to the original documentation, there are numerous examples. And as PyTorch has become very popular compared to Tensorflow in the last couple of years, there are many resources available, making it easier for us to adopt. 

None come to mind.

There is not enough documentation about some methods and parameters. It is sometimes difficult to find information.