PyTorch – Tensor computation with strong GPU acceleration

by tmlee • 7 years ago

42 12 7 years ago

abakus• 7 years ago

https://github.com/blue-season/pywarm PyWarm is a lightweight, high-level neural network construction API for PyTorch. It enables defining all parts of NNs in the functional way.

ru999gol• 7 years ago

I don't see much of a reason you want to clutter your code with that, if you need a wrapper around pytorch use fastai not some obscure library nobody has ever heard about.

wodenokoto• 7 years ago

Thank god you weren’t around when keras was released.

jsinai• 7 years ago

Use fastai if you want your code to be unreadable, unmaintable and prone to mysterious bugs.

psv1• 7 years ago

Apart from the official tutorials what are the best resources out there for learning PyTorch?

abiro• 7 years ago

What exactly are you looking for? I think Pytorch is so ergonomic that there is no need for other resources. The only gotchas I found were in the data loading utilities.

kingrolo• 7 years ago

I really enjoyed this book for RL. https://www.packtpub.com/gb/big-data-and-business-intelligen...

If you learn well from videos many rave about the free fast.ai courses which now use PyTorch I believe. Seems to start with image classification. http://fast.ai

sandGorgon• 7 years ago

The #1 NLP repo on Github and the author of the most popular NLP course is moving from PyTorch to Tensorflow 2.0/Keras

https://twitter.com/GokuMohandas/status/1174497967232782336

We’ll still use @PyTorch but more for research lessons, I’ll post more on this decision soon! I did deliberate for several weeks on this though but ultimately it sped up development and decreased overhead for the practical lessons by a lot.

amelius• 7 years ago

decreased overhead for the practical lessons by a lot.

What do they mean by that?

dual_basis• 7 years ago

Presumably that it was easier to use to illustrate the actual content as opposed to implementation details. This is definitely the case, at least in TensorFlow 1.x there were a lot of details which made converting from [the way we think about the problem] to [practical implementation] more cumbersome. Some of these existed for good reason (eg. performance) while others were simply architectural cruft which the benefit of hindsight makes unnecessary.

kartayyar• 7 years ago

This is really old, it came out in 2016.

Why did you reshare it ?

baalimago• 7 years ago

The framework which put my trust back into frameworks. Very good!

PyTorch – Tensor computation with strong GPU acceleration

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