React vs Angular, Pytorch vs Tensorflow. These are just two examples among many where the Facebook framework arrives a bit later on the market but is then supported awesomely and improved continuously while the Google framework arrives earlier but then becomes a hot mess of non retro compatible upgrades and deprecations...
My “loyalty” to Facebook open source libraries just keep growing.
I mean, the cool things are cool (even if I have a really hard time with Facebook as a company) but I think it's a bit of a stretch to say Facebook has "the recipe".
Whereas PyTorch is intended, from the ground up, to be a widely useful project, and the dev team weights open-source issues at least as much as internal ones.
(Full disclosure: I used to work at Facebook, including, briefly, on PyTorch)
But my subjective point of view is: Google project = “Meh, this big opinionated framework will be probably be abandoned in 2years and all those poor folks who adopted it will have to rewrite everything”, while Facebook project = “Wow they just took the best of the state of the art, and packaged it in a simple reusable library that will be supported decently”.
Not a big fan of Facebook as a company, but as an open source contributor they are among the best in my book.
Part of what made AWS get such a head start is that Amazon actually build it for themselves. Google just throws random tech around, but most of their own tools stay internal.
My react native app does some advanced stuff like displaying 3D stuff with openGL, running separate threads, interacting with drones and running neural nets (hence why I am happy about this news!). I still have to think how to upgrade to 0.60 while supporting threads though.
While it was not always super easy to configure all this, it has stayed unbroken for all this time so again this is something I am thankful for.
However I'm a bit skeptical about doing quantization after training, in my experience you have to do quantization-aware training for there not be a large performance decrease. I guess it works though otherwise they wouldn't have released it?
[1] - https://pytorch.org/docs/master/quantization.html#quantizati...
How does CocoaPods compare to Maven, NuGet, or NPM?
I wouldn't piss on npm if it was on fire.
Members of my team have spent literal months tracking down memory leaks, the performance of these services are always sub-par to Tensorflow based ones and the less said about the atrocious memory/cpu usage the better.
What's the advantage of using PyTorch when you have things like Tensorflow Serving ready to productionize any model with ease?
