by crubier on 10/10/19, 7:19 PM
This is amazing! It might be a personal feeling, but my opinion is that Facebook is SO much better than Google at delivering open source libraries that people want, and support them in the best way.
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.
by Nimitz14 on 10/10/19, 5:56 PM
Yes yes yes! I've been anticipating this for so long.
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?
by orf on 10/10/19, 11:07 PM
Does anyone else have to maintain backend PyTorch based services? Is it just me or is it a complete mess?
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?
by reducesuffering on 10/10/19, 8:34 PM
Does this leverage the Neural Engine part of the chip on iOS devices like Core ML does? Can anyone compare using this to Core ML Apple API's?
by spicyramen on 10/11/19, 3:36 AM
This is a common trend for being second in market, when we see Pytorch and TensorFlow 2.0, TF 2.0 was created to compete directly with Pytorch pythonic implementation (Keras based, Eager execution). Facebook at least on Pytorch has been delivering a quality product. Although for us running production pipelines TF is still ahead in many areas (GPU, TPU implementation, TensorRT, TFX and other pipeline tools) I can see Pytorch catching up on the next couple of years which by my prediction many companies will be running serious and advanced workflows and we may be able to see a winner there.
by faceshapeapp on 10/10/19, 6:59 PM
This is super exciting! I wish they also had browser support without having to go through onnx.
by umanwizard on 10/10/19, 7:34 PM
If anyone from the team is reading, can you comment on how much code size this adds to iOS apps?
by tmoot on 10/10/19, 6:42 PM
Well, there goes my weekend :)
by mikkelam on 10/11/19, 10:52 AM
Might be a stupid question, but what's the advantage of using PyTorch Mobile, compared to converting to ONNX-> TF -> TF lite
(or something similar)
by diffset on 10/10/19, 10:12 PM
What advantage does this provide over Apple's CoreML?
by wil421 on 10/11/19, 12:45 AM
As a side point, looking at maven and the org.pytorch gave me a cold shudder remembering my java days.
How does CocoaPods compare to Maven, NuGet, or NPM?
by bjornjaja on 10/10/19, 8:07 PM
Curious, what does PyTorch do for embedded when Torch uses Lua? Can’t imagine python for embedded is better than Lua?