A while back I decided to stick with the PyTorch library instead of Keras/Tensorflow for neural networks. However that decision seems to be limiting me a bit too much. One of the reasons was because trying to get Tensorflow to work after I had set PyTorch up to work with the GPU caused errors that I couldn’t resolve.
Well, there is a way to resolve them, and that is to run Tensorflow in a Docker container. I’ve already tried that a couple of times, it works, although I’m not going to try to get it to work with the GPU from inside a Docker container! It probably can be done, but not by me.
Anyway the main codebase I want to explore, using Tensorflow, is some Quantra courses involving deep learning. They use Tensorflow, and my attempts to convert to PyTorch were not as successful as I hoped they would be. I think it’s because that codebase is just a bit too complex for my current level of understanding. Quantra also use ta-lib quite a bit for indicators. That has to be built from source, and luckily I found some code on StackOverflow that does exactly that, so I now have a Docker container with both Tensorflow and TA-Lib installed and hopefully that will be enough (in addition to all the usual data science packages of course). I forgot to install Jupyter Notebook but I can live without that. The original Quantra files are all Jupyter Notebooks, but I find that’s hopeless for debugging and I prefer to reconfigure them all as ordinary py files anyway.
So after lots of very frustrating explorations of apps that never seem to work, for a range of reasons, I’m back to the original app that got me started in Reinforcement Learning. Maybe I can actually get it up and running this time, and more importantly, understand it well enough to get it to work with my own data and not just the data supplied in the course.