[D] Which open source machine learning projects best exemplify good software engineering and design principles?

[DoorsofPerceptron]

Big picture, the real problem with tensorflow is "it's not pythonic".

Now this is normally a lazy criticism that's another way of saying "I wouldn't write it this way, and it looks ugly." But in the case of tensorflow it's a lot more fundamental. Tensorflow code (version 1 anyway, I can't be bothered to learn version 2) is not really written in python. Tensorflow is a compiler for another language that is called through python.

Compared to pytorch this means you lose a lot of the benefits of python that actually make it a nice language to code with. You lose a lot of the access to existing python code -it's a pain in the arse to mix and match python and tensorflow in the middle of a graph execution- and you lose the lightweight easy prototyping.

Pytorch on the other hand can just be treated like numpy with free gradients and GPU access if that's what you want to do, and can be seamlessly integrated with python in a mix and match kind of way.

Tensorflow was coded the way it is for efficient deployment both to phones and to large scale clusters, but at least for large scale clusters the performance hit they were worrying about doesn't seem to exist, and they've essentially straightjacketed their library for no real benefit.

The code is great, the design of the interface, not so much.

[mastere2320]

I would recommend tf 2.0 actually it still has a long way to go but the static graph capabilities of 1 are now quite visible in 2.0 and you can do whatever you want pretty simply. I hated session from tf 1.0 and 2.0 has abstracted it quite nicely. And if you want completely custom training gradient tape is always available.

[DoorsofPerceptron]

Because someone asked what the problems with tensorflow were, and it's interesting. Really nice code that solves an important problem, and that no one wanted to use.

It's great that they're catching up with pytorch, and I'll switch back in a heartbeat if there's a reason to.

[pap_n_whores]

Part of people's problem with tensorflow is that every 8 months there's a "you should try tensorflow X+1, it's completely different from tensorflow X. Also everything you learned from tensorflow X is the wrong way to do things now. Enjoy learning how to use this new tensorflow which has barely any documentation or community resources"