Live data from Hacker News

TensorFlow: open-source library for machine intelligence

tensorflow.org

31–40 of 211 posts

Re: TensorFlow: open-source library for machine intelligence

#31
I can't wait to try this tonight. I have a fun "messing around" project (I am using GnuGo to generate as much training data as I need for playing Go on a very small board, and I am almost to the point of starting to train and test models).

BTW, Ruby has always been my scripting language but because of the wealth of libraries for deep learning I am thinking of switching to Python.

Re: TensorFlow: open-source library for machine intelligence

#33

I can't wait to try this tonight. I have a fun "messing around" project (I am using GnuGo to generate as much training data as I need for playing Go on a very small board, and I am almost to the point of starting to train and test models). BTW, Ruby has always been my scripting language but because of the wealth of libraries for deep learning I am thinking of switching to Python.

Why switch? I use Ruby, when I can and Python when I can't.

Would really love to see ruby forks of popular ml libs.

Or is it because Python is better perfomance-wise?

Re: TensorFlow: open-source library for machine intelligence

#34
post #15
post #6

> This open source release supports single machines and mobile devices. Can someone clarify if "single machines" means the Apache license only applies to single machines and not distributed systems? Sidenote: I wish every open source project had a release video.

There's a note on the site about being able to move computation to a process on a different machine, but it does mention copying the model. If I understand correctly, I think models tend to be quite small, though. Does it actually make sense to train a model simultaneously on different machines? I can understand running the same model on lots of machines, of course.

Yes, there are two types of parallelism: model and data. Data parallelism is simply training the the model on multiple computers with different minibatches, and aggregating the gradients. Model parallelism is hosting different parts of the model on different computers. Of course, these two parallelisms can be combined. A good explanation is the One Weird Trick paper: http://arxiv.org/abs/1404.5997

Re: TensorFlow: open-source library for machine intelligence

#35
post #15
post #6

> This open source release supports single machines and mobile devices. Can someone clarify if "single machines" means the Apache license only applies to single machines and not distributed systems? Sidenote: I wish every open source project had a release video.

There's a note on the site about being able to move computation to a process on a different machine, but it does mention copying the model. If I understand correctly, I think models tend to be quite small, though. Does it actually make sense to train a model simultaneously on different machines? I can understand running the same model on lots of machines, of course.

If there is randomness in some part of the training process (and most surely there will be), then it makes sense. Different trainings may end up in different local minimums/maximums, so there is value in running multiple trainings simultaneously in different machines.

Re: TensorFlow: open-source library for machine intelligence

#39

I can't wait to try this tonight. I have a fun "messing around" project (I am using GnuGo to generate as much training data as I need for playing Go on a very small board, and I am almost to the point of starting to train and test models). BTW, Ruby has always been my scripting language but because of the wealth of libraries for deep learning I am thinking of switching to Python.

Why switch? I use Ruby, when I can and Python when I can't. Would really love to see ruby forks of popular ml libs. Or is it because Python is better perfomance-wise?

Really good question since I really enjoy using Ruby, more so even than Clojure and Haskell.

I too would like to see Ruby ports of some of the popular ML libraries. The classifier gem provides an example.

Re: TensorFlow: open-source library for machine intelligence

#40
post #8

This is really significant. At this moment in history, the growth of computer power has made a bunch of important signal-processing and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it's been very difficult to take advantage of the available computational power, because it's in the form of GPUs and clusters. TensorFlow is a library design…

What about Torch or Theano which allow you to use multiple GPUs and clusters? they also have a wide array of libraries which allows you to extend capabilities (itorch etc.). torch is also very fast, most parts written in C so I don't know if Tensorflow would be really that fast compared to existing librairies.

One thing find I found interesting is the ability to use the software you designed in research directly in production (without having to rewrite the whole thing in java)

That said, I'm excited to see Google release this. Hopefully it would encourage Facebook or Baidu to also release

Post reply on HN