Published on

TensorFlow @ PyData SG


Google just released a fantastic-looking deep learning library called TensorFlow, complete with tutorials, and model-zoo-like examples.

Fortunately, the framework is very reminiscent of Theano, and has a Python front-end over a computation graph construction machine in C++ / CUDA (no OpenCL as far as I can tell).

Because it's new, and Python-related, I gave a "Lightning Talk" at the November meeting of the Singapore PyData group.

Presentation Screenshot

If there are any questions about the presentation please ask below, or contact me using the details given on the slides themselves.

Presentation Content Example

#Slide Notes (outline)

These are the notes I put together for myself before preparing the slides. Almost everything made it into the presentation, but only because I split the positives into 2 slides, and the not-so-positives into 4 slides...


  • Cross-platform : CPUs, GPUs, Android, iOS (soon), etc

  • Open Source (Apache 2)

    • and Google actively reviews and responds to PRs
  • Theano-like definition and optimisation of calculation graph

    • backend in C++
  • Python is first-class citizen (other is C++)

  • TensorBoard (images?)

  • Hiring tool


There are several points worth highlighting:

  • Nvidia devices supported much have 'Compute Capability' >= 3.5

    • This includes 900-series cards, and Titan, and the 'K cards'
    • But not 700-series (apart from 750), nor Amazon EC GPUs
      • "A g2.2xlarge is a downclocked GK104 (797 MHz), that would make it 1/4 the speed of the recently released TitanX and 2.7x slower than a GTX 980."
  • Not many ops available in GPU

    • Vector embedding example can't be run on GPU, for instance
      • Theano can do this
  • No OpenCL

  • Memory hungry - far prefers a 16Gb machine to an 8Gb one

  • Inefficient operations

    • No in-place ReLU, for instance
  • C++ build environment requires bazel which is a Java-based horror story

  • PR submission process is via (painful) Gerrit rather than GitHub

  • Legacy Nvidia drivers :

    • 7.0 (rather than 7.5) for the main driver
    • 6.5 (rather than 7.0+) for cuDNN, which is now 'archive', so not even supported
  • No distributed computation (yet), even though that's what Google uses

  • Only Python and C++ APIs (others will be 'community implemented')

  • Incorporates something like fuel (from blocks) as a data-feed engine

  • Whole approach requires implementing many 'client' operations on the TensorFlow 'server' side

  • Currently targets Python 2.7 (though 3.3+ looks like it's coming soon)

  • Would definitely benefit from 'deep learning library' like lasagne

    • theras author has already stated that they'll target TensorFlow