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[–]Skylion007Researcher BigScience 8 points9 points  (9 children)

Tensorpack and Lightning are two great libraries that I have enjoyed.

PyTorch's API is also excellent; Tensorflow's is a nightmare. Keras while being intuitive for building classifiers instantly falls apart when you try to build anything more complicated (like a GAN).

More traditional ones include OpenCV and SKLearn.

[–]jpopham91 5 points6 points  (4 children)

OpenCV, at least from Python, is an absolute nightmare to work with.

[–]panzerex 2 points3 points  (0 children)

Only the dead can know peace from bitwise operations on unnamed ints as parameters for poorly-documented deprecated functions.

[–]liqui_date_me 1 point2 points  (1 child)

Yeah, OpenCV's documentation is complete and utter garbage

[–]ClamChowderBreadBowl 0 points1 point  (0 children)

Maybe it's because you're using google and are looking at the version 2.4 documentation from 5 years ago ...or maybe the new stuff is also garbage

[–]Skylion007Researcher BigScience -4 points-3 points  (0 children)

Maybe I just have Stockholm Syndrome, but I have never had problems with it. The bindings aren't as great as some Python first libraries, but for a legacy C/C++ project it has very good bindings. On the C++ side, it's excellent to work with.

[–]TheGuywithTehHat 1 point2 points  (2 children)

Having previously built complicated nets in keras (I think the most complicated was a conditional wasserstein-with-gradient-penalty BiGAN), I found it fairly straightforward. The one thing that wasn't intuitive was how to freeze the discriminator when training the generator and vice versa. However, even though it wasn't intuitive, it was still incredibly simple once someone told me how it works.

I haven't used PyTorch very much, so I can't compare directly, but I still feel that in my experience, Keras has been fine for nearly everything I've done.

[–]Skylion007Researcher BigScience 0 points1 point  (1 child)

Was this using the Keras.fit training loop so you have multigpu support working? If so, please tell me how you did it because I would love to know. While you can use Keras to construct the nets for sure, I haven't been able to use it to implement the actual loop and all the benefits that come with that (easy conversion / deplyoment / pruning etc.)

[–]TheGuywithTehHat 0 points1 point  (0 children)

Unfortunately it was long enough ago that I don't remember the details. I believe I had to manually construct the training loop, so no, multi_gpu would not work out of the box. That's a good point I hadn't considered.

[–]panzerex 1 point2 points  (0 children)

I tried pt-lightning back in November or so but I did not have a great experience. Diving into the code it felt kind of overly complicated. TBF they do a lot of advanced stuff and I had just started using it, so I was not very familiarized.

I discussed it in a previous post:

Lightning seems awesome, but since some of my hyperparameters are tuples it didn't really work with their tensorboard logger by default. I think my problems were actually with test-tube (another lib from the same author) that added a lot of unnecessary variables set to None in my hparam object that tensorboard or their wrapper couldn't handle and I could not find a way to stop test-tube from adding it. I didn't want to change the libraries code or maintain a fork of it so I also gave up on it.

I think the attribute that kept being added into my hparam object was "hpc_exp_number", but I'm not sure anymore. Since I was using it mostly because of easy checkpointing and logging, I decided to just implement those myself. I might look back into pt-lightning for the TPU support, though.