Re:TensorFlow Fold: Deep Learning with Dynamic Computation G...
by general_ai on Wednesday February 08, 2017 @01:31AM
(#13596271)
The way I see it, TF is about to pull _way_ ahead thanks to XLA JIT/AOT. All of a sudden you get the ability to fuse things at a much more granular level, which could reduce memory bandwidth requirements by a lot. Frameworks like Torch can't do any fusing at all, since their computation is fully imperative. Tactical win for imperative frameworks, I suppose, but strategically functional graph is the way to go. DB people realized this in the 70s, ML people are realizing this now.
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Re:TensorFlow Fold: Deep Learning with Dynamic Computation G...
by congerous on Wednesday February 08, 2017 @05:22AM
(#13597207)
TF is way behind on UI, which is why it's making Keras its front-end. It's fairly slow on multi-GPUs compared to Torch and neon. It might pull ahead in performance on GCE, but that's just for lockin.
Re:TensorFlow Fold: Deep Learning with Dynamic Computation G...
by general_ai on Wednesday February 08, 2017 @01:04PM
(#13600467)
TF is in a fortunate position of having several UIs at this point. It's a lower level framework with a lot of power. If you don't need all that power, Keras or TFLearn or Slim are pretty great. If you do, it's there for you. I see no evidence that Google's goal with TF is to lock you into anything, and especially GCE. I'm a former Google employee, and I can tell you unequivocally — that's not how Google actually works.