Sidenote:
We always say the hardest things in software is cache invalidation and naming things, but I also found the problem to be dependency tracking and reproducibility. It's just so hard to get started with ANY non trivial software project and your os/tools/libs expire way before you finish the projects. Software should be cheap and repeatable, but for some reason it takes active maintenance and is therefore very expensive. If a superhuman intelligence looked at us from afar, we would probably look like how ants look to us: Millions of small workers with very inefficient probabilistic behavior. Sure, we get the job done, but very slowly with a lot of waste. That said, ants lived for ever, so maybe it is the right thing to do ;)
Either way, I often find myself choosing backward compatibility and stability over innovation and polish and choose to learn vim and bash instead of replacing the silver bullet every year.
Shameless plug, I am working on a platform similar to FloydHub, but for frontend engineers [0]. The problem is a real one.
I can definitely resonate with this. Deep learning is in such an early stage, the frameworks and tooling are still maturing and evolving rapidly. This makes it really hard to reproduce other's work. Maybe there will be one winner in the frameworks war (Tensorflow?) and things will be better.
Either way, I often find myself choosing backward compatibility and stability over innovation and polish and choose to learn vim and bash instead of replacing the silver bullet every year.
Shameless plug, I am working on a platform similar to FloydHub, but for frontend engineers [0]. The problem is a real one.
[0] https://pipez.io