Comment by 50CNT | Hacker News Reader

Comment by 50CNT | Hacker News Reader

    TensorFlow is admirably easier to install than some other frameworks

I thought most frameworks are fairly easy to install in python, usually with a single call to pip. NLTK takes one "pip install nltk" and then "python", "import nltk", "nltk.download()" to download all the corpuses and miscellaneous data. Installing tensorflow seems complicated compared to that.

    # Ubuntu/Linux 64-bit, CPU only:
    $ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

    # Ubuntu/Linux 64-bit, GPU enabled. Requires CUDA toolkit 7.5 and CuDNN v4.  For
    # other versions, see "Install from sources" below.
    $ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

Not that either are particularly complicated, but saying other frameworks (assuming they're referring to python frameworks) are "a lot harder to install" seems disingenuous.

That said, I haven't played around with AI frameworks too much, so I might just be missing a real stinker.

once you have to install a GPU device driver, CUDA, tensorflow itself, related python packages like numpy, and you have choices of native installer, Linux packages, pip, conda, there's potential to find yourself in dependency hell, all of those layers are evolving rapidly.

I found these helpful (on AWS)

- http://ramhiser.com/2016/01/05/installing-tensorflow-on-an-a...

- http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws...

of course you don't need to install CUDA just to learn, can run tensorflow on CPU only, but part of the point of the graph paradigm is to design a computation and offload it to GPU.

Compared to other frameworks which allow for deep learning, it's much easier to install.

I see. The article was a bit vague for me in what it means by frameworks (does NLTK count? Django? DL frameworks in other languages?), and since I don't know the area too well, that struck out to me as odd.

It honestly looks like pretty cool stuff, looking forward to having time to play around with it some day.

Thanks! I didn't want to name names in the comparison to other frameworks, but I was thinking mostly of Caffe, which doesn't have any install path as simple as just a `pip install`. Other DL frameworks would be things like torch, theano, etc.

Fwiw, Deeplearning4j is also quite easy to install:

http://deeplearning4j.org/quickstart

https://github.com/deeplearning4j

Deeplearning frameworks aren't your normal machine learning frameworks. The real problem is CUDA, which isn't allowed to be distributed separately.

By comparison, here's what you need to install (manually!) for Torch on OSX:

  Homebrew
  Xcode
  cmake
  wget
  readline
  QT
  ffmpeg
  gnuplot
  opencv
  CUDA (and there's a whole other thread trying to get that to work..)

http://e-lab.github.io/html/wiki-torch7-installation.html

clearly you haven't tried to install caffe

Like I said, I have not played around with a lot of DL frameworks, only more general python things. I'll take note of that, I'm really interested on how difficult different DL frameworks are to set up.

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