Is there any tooling that lets one profile the individual parts of the compute graph? In CUDA land, I can imagine how to build such a thing. No clue how it would work on Mobile.
I wonder if future versions of PyTorch will automatically apply mixed precision if not specified, the article makes it seem like a no brainer to use them by default
Some parts of a module may not work well in lower precision and need to be in higher precision. If you ever create new tensors within the forward pass, you need to manually adjust your code to automatically use the right datatype. You still need to figure out which low-precision dtype you want to use in your model (and perhaps use different ones in different parts of your model). Etc.
TF is pretty much dead. The examples often do not work, the docs are not up to date and I don't think any recent paper/projects use TF so you'll also find a better community and better resources around Pytorch.
Debugging is a lot easier in PyTorch. Although you can debug the compiled graph in Tensorflow, from experience, the local state might not be the same in debug mode as in compiled mode.
Also, I've encountered strange performance regression issues with the newest Docker releases of Tensorflow, with 10x slow-downs compared to previous minor releases. And the docker version was always slower than the local version. Something something Nvidia & CUDA I guess. I had not performance differences with PyTorch when using docker.
It should be said that Tensorflow was generally 10 to 20% faster for similar models. But that could be down to my ineptitude.
One reason is that overall there are more PyTorch based ML projects out there, which translates to larger exploration space and wider support base. Around the beginning of 2021 PyTorch overtook TensorFlow as the ML framework of choice, see https://trends.google.com/trends/explore?date=today%205-y&q=...
PyTorch has a very good record of backwards compatibility compared to Tensorflow; your code is much less likely to be broken/deprecated if you use PyTorch.
At 100% zoom on Firefox and Edge, the tops & bottoms of lower case letters have some very strange thinning/bolding going on. The top of the `e` char in "Syed Ahmed" is thinner (maybe 1px in height) than the lower curve of the same `e` character (maybe 3px in height). It looks like they have different font-weights for the top and bottom of the characters somehow.
Zooming in to 125% the effect goes away, and the font-weight at the top and bottom appear equally thick.
[+] [-] brutus1213|2 years ago|reply
[+] [-] hatthew|2 years ago|reply
https://pytorch.org/tutorials/intermediate/tensorboard_profi...
[+] [-] jerpint|2 years ago|reply
[+] [-] hatthew|2 years ago|reply
Some parts of a module may not work well in lower precision and need to be in higher precision. If you ever create new tensors within the forward pass, you need to manually adjust your code to automatically use the right datatype. You still need to figure out which low-precision dtype you want to use in your model (and perhaps use different ones in different parts of your model). Etc.
[+] [-] aborsy|2 years ago|reply
[+] [-] NeutralForest|2 years ago|reply
[+] [-] dog436zkj3p7|2 years ago|reply
[+] [-] pcwelder|2 years ago|reply
I switched to Pytorch after I encountered this bug in a very normal use case back in v1.13 https://colab.research.google.com/drive/1D-kgD7NiRXTNTNwVr18...
I've never encountered such a bug in Pytorch in the last 4-5 years.
[+] [-] david-gpu|2 years ago|reply
As a researcher, Pytorch was also much easier to tinker with, which is perhaps a factor that explains why it rapidly gained popularity in academia.
[+] [-] fransje26|2 years ago|reply
Also, I've encountered strange performance regression issues with the newest Docker releases of Tensorflow, with 10x slow-downs compared to previous minor releases. And the docker version was always slower than the local version. Something something Nvidia & CUDA I guess. I had not performance differences with PyTorch when using docker.
It should be said that Tensorflow was generally 10 to 20% faster for similar models. But that could be down to my ineptitude.
[+] [-] blitzar|2 years ago|reply
[+] [-] armcat|2 years ago|reply
[+] [-] logicchains|2 years ago|reply
[+] [-] skadamat|2 years ago|reply
[+] [-] brutus1213|2 years ago|reply
[+] [-] dist-epoch|2 years ago|reply
[+] [-] imtringued|2 years ago|reply
[+] [-] olives|2 years ago|reply
[+] [-] Shrezzing|2 years ago|reply
Zooming in to 125% the effect goes away, and the font-weight at the top and bottom appear equally thick.
[+] [-] keithalewis|2 years ago|reply
[+] [-] hatthew|2 years ago|reply
[+] [-] blitzar|2 years ago|reply
[+] [-] atomlib|2 years ago|reply
[+] [-] unknown|2 years ago|reply
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[+] [-] unknown|2 years ago|reply
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