Other tools like MXNet deserve a shoutout as well, and it would be interesting to see how a wider group compares. MXNet also integrates seamlessly into R, something of a rarity in deep learning tools (excepting the also excellent h2o package).
* Cross platform: Windows, MacOS, Linux; CPU and CUDA. Though their CMake needs work.
* Easy to embed: straightforward C FFI, JSON for metadata and parameter serialization, no weird runtime.
* Flexible: not too specialized to vision. Static unrolling of RNNs possible now (with mirroring this can still be very memory efficient [0]), basic support for the fast new cuDNN 5 RNN layers [1] (contributed by colleague of mine). Dynamic unrolling is on the horizon I hear.
* Hackable: once you're familiar with the codebase, custom elementwise unary or binary ops = few minutes, custom layers = 1+ hours (depending on complexity). And if you can leverage mshadow primitives for your layer implementation, you don't even have to touch CUDA. Also fairly active on github, responsive to PRs etc.
[0] https://arxiv.org/pdf/1606.03401.pdf
[1] https://devblogs.nvidia.com/parallelforall/optimizing-recurr...
Clearly room for optimizing the CPU versions of things. That may be Eigen. Intel now has a preview out of their DNN toolkit -- I wonder if we'll see the same speed convergence as we did with CuDNN.
What do you mean "so slow"? It's by far the fastest framework covered by the paper in scenarios where threads don't outnumber CPU cores.
Taken from the article itself:
"However, Torch still achieves the best performance in our experiments in which Torch has nearly 12x speed up compared with TensorFlow under 4-thread setting."
You're right that Torch is faster than TensorFlow in RNN. But Torch is slower than TesnorFlow in AlexNet and ResNet. There is a set of benchmarks for many DL approaches as found in https://github.com/soumith/convnet-benchmarks
