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MILA and the future of Theano

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Re: MILA and the future of Theano

#41
I find myself very impressed with the humility of the group taking a bow at the right time.

This is the crux of the matter it would seem: "Even with the increasing support of external contributions from industry and academia, maintaining an older code base and keeping up with competitors has come in the way of innovation."

Very mature move Theano team, you all did a great job and raised the bar at your peak with the solid innovations that became standard as you identified. Best wishes.

Re: MILA and the future of Theano

#42
post #22
post #2

Reminds me of Chrome. Lots of competing alternatives. Google comes late yet still manage to take over the market organically very quickly by producing a superior and more robust alternative.

It's something of a pattern for Google. Existing products (say, Theano and Torch) were revolutionary, but had a number of pain points that were difficult to remove[0]. TensorFlow is essentially Theano without those problems, and with the guarantee of continued relevance thanks to Google's backing[1]. And of course, they reap the benefits of making it "open-source" while keeping a closed version for private use, simil…

Apple is arguably another example of this, but in a completely different arena.

They are really good at polishing cutting edge technology into nice hardware products.

Re: MILA and the future of Theano

#43
post #2

Reminds me of Chrome. Lots of competing alternatives. Google comes late yet still manage to take over the market organically very quickly by producing a superior and more robust alternative.

" by producing a superior and more robust alternative." In a commodity market - those with brand and distribution will win. Chrome isn't really that much better than others. It may be in some areas, not in others. But when the 'company that owns the web' is promoting it, they can get any number of downloads they chose to as long as the product is competitive. And distribution is one thing, brands have incredible powe…

[deleted]

Re: MILA and the future of Theano

#44
post #8

Earlier quoted context omitted.

Tensorflow does everything Theano does, but with the backing of the big G. That's basically why Theano is being sunsetted.

As I understand it Tensorflow does not handle a large number of variables very well[0], which is the killer feature of NUTS in pymc3. This makes it a bit of a non-starter for pymc3. [0] https://github.com/pymc-devs/pymc3/issues/1650

That comment states that TensorFlow does not handle a large number of operations well, not a large number of variables. There's a large difference between those statements :). But yes, the general point is correct, especially for models that do not use many matrix multiplications.

Re: MILA and the future of Theano

#45
post #22

Earlier quoted context omitted.

It's something of a pattern for Google. Existing products (say, Theano and Torch) were revolutionary, but had a number of pain points that were difficult to remove[0]. TensorFlow is essentially Theano without those problems, and with the guarantee of continued relevance thanks to Google's backing[1]. And of course, they reap the benefits of making it "open-source" while keeping a closed version for private use, simil…

This is going to sound wildly ignorant, is Google Ultron a real thing?! I just remember it as a joke "super secret ultimate browser used by NASA" from that "Tales of IT" green text story [0]. [0]: https://imgur.com/gallery/iJD8f edit: Whoops, it was mentioned in part 2 - https://imgur.com/gallery/AOz0d

It's as real as you want it to be: http://ultronbrowser.io/

Re: MILA and the future of Theano

#46
post #44

Earlier quoted context omitted.

As I understand it Tensorflow does not handle a large number of variables very well[0], which is the killer feature of NUTS in pymc3. This makes it a bit of a non-starter for pymc3. [0] https://github.com/pymc-devs/pymc3/issues/1650

That comment states that TensorFlow does not handle a large number of operations well, not a large number of variables. There's a large difference between those statements :). But yes, the general point is correct, especially for models that do not use many matrix multiplications.

Oops! Right you are. I just recalled that there was some issue with a large number of . I should have reread the threat before linking it.

Re: MILA and the future of Theano

#47
post #2

Reminds me of Chrome. Lots of competing alternatives. Google comes late yet still manage to take over the market organically very quickly by producing a superior and more robust alternative.

organically? Please, Google promoted Chrome through ads for months! -- disclaimer, I love Chrome!

Re: MILA and the future of Theano

#48
Can't say I'm not sad to hear this... I've always much preferred the Theano API. IMHO, the way you have to explicitly build the graph in Tensorflow is cumbersome, compared to how it automagically happens behind the scenes in Theano. The code to simply multiply a couple of matrices, for example, reads much nicer in th than tf.

Annyywhoo, better brush up on TensorFlow I guess.

Re: MILA and the future of Theano

#49
post #7
post #5

Earlier quoted context omitted.

I think s/he meant (other browsers) : chrome :: theano : tensorflow.

Correct. When Tensorflow came out, it seemed that there was already too many good alternatives (Theano, Caffe, Torch). When Chrome came out, Firefox, Safari and the latest version of IE also seemed decent options.

when google.com itself came out, the search engine market was practically covered by yahoo, altavista and co.

Re: MILA and the future of Theano

#50
post #32
post #2

Reminds me of Chrome. Lots of competing alternatives. Google comes late yet still manage to take over the market organically very quickly by producing a superior and more robust alternative.

Anybody else has the feeling that PyTorch is to TensorFlow what Chrome was to other browsers? I started PyTorch about a month ago and was impressed how effortless everything was compared to TF.

Tensorflow's advantage is that once you build your model, you can run on everything from a massive cluster to a mobile GPU without significant modification. Because you're just writing a description of a computation graph, it's easy for backend systems to process that description and optimize the execution of your model.

PyTorch's imperative semantics (where the computation graph is implicitly defined at runtime by the execution of your Python code) definitely make it cleaner to do research prototyping. But AFAIK most PyTorch models need be reimplemented in lower-level code, or maybe something like Caffe2, before they can be used in production. That's a fairly significant tradeoff, which makes it hard to see PyTorch totally replacing Tensorflow anytime in the near future. That said PyTorch is obviously a great tool and it's exciting to see how it will develop and be used.

(disclaimer: I work for Google, opinions are my own)

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