RIP
MILA and the future of Theano
51–60 of 63 posts
Re: MILA and the future of Theano
#52Earlier quoted context omitted.
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 t…
PyTorch does have ONNX, a tool to convert from PyTorch models to Caffe2 models: http://pytorch.org/tutorials/advanced/super_resolution_with_...
Re: MILA and the future of Theano
#53Reminds 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.
I’m doing research (not deployment) and have the same feeling. PyTorch has inspired a blog post [1], Tensorflow didn’t.
Briefly, the benefits of PyTorch are
* easy conversion to NumPy arrays (meaning rest of Python can be used!). This is a bottleneck in Tensorflow; for reasonable sizes, PyTorch is 1000x faster.
* trackbacks are easy to follow (because defines graph by running)
* it’s as fast as tensorflow [2] (or at least torch is, which calls the same C functions as PyTorch, and there’s a tweet [4] by a core dev saying to expect the same speeds). Plus on the web I’ve only found anecdotes that support PyTorch faster than tensorflow.
* it’s easy to extend; everything is a simple Python class. e.g., see their different optimizers [3]
[1]:http://stsievert.com/blog/2017/09/07/pytorch/
[2]:https://github.com/soumith/convnet-benchmarks
[3]:https://github.com/pytorch/pytorch/tree/master/torch/optim
[4]:https://twitter.com/soumithchintala/status/83545486710789734...
Re: MILA and the future of Theano
#54Earlier quoted context omitted.
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.
Chrome was so out of left field. A desktop browser? Made by Google ?! In an environment where people have celebratory launch parties for Firefox?? Of course, the business goals made sense, but I don't think anyone guessed it would overtake the market share so effortlessly.
One of the boldest design decisions they made was to make the URL bar and search bar into one whereas every other browser (can't remember what Opera did) separated the two. That immediately hooked me from using Safari/Firefox. That and it felt faster.
Re: MILA and the future of Theano
#55Reminds 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.
Try chrome on a Mac, it constantly revs up the CPU and eats way more memory than safari
Re: MILA and the future of Theano
#56Earlier quoted context omitted.
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.
Chrome was so out of left field. A desktop browser? Made by Google ?! In an environment where people have celebratory launch parties for Firefox?? Of course, the business goals made sense, but I don't think anyone guessed it would overtake the market share so effortlessly.
Come to think of it, google has pissed away so much user goodwill over the years. There was a time when they could do no wrong. I guess same as MS in early 90s. Now they are nearing a point where with every product release they have to prove they don’t suck and aren’t screwing the user. It’s a very difficult regime to operate under.
Re: MILA and the future of Theano
#57Reminds 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.
Thoughts on Keras on top of tensorflow?
I have not yet committed to a deep learning framework, as up until now, I was mostly either using scikit-learn or building neural networks from scratch, straight numpy (lol)
I've heard a nice thing about Keras is that it forms more of an abstraction on top of other libraries, though I could be misunderstanding.
Re: MILA and the future of Theano
#58Reminds 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.
Try chrome on a Mac, it constantly revs up the CPU and eats way more memory than safari
I was wondering why these people were praising Chrome so much about until you pointed out the problems on Mac, i.e. I mainly use Mac for browsing, these other people must be using non-Macs, you use both.
Re: MILA and the future of Theano
#59Earlier quoted context omitted.
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 t…
> massive cluster
No you can't because Google doesn't release those functions in the open source version of TF
Also PyTorch will soon by able to export directly to Caffe2 / CNTK
Re: MILA and the future of Theano
#60Can'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.
You should give PyTorch a try