At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered this lightning talk to a sold-out crowd. Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface. Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.

1. Deeplearning4J
François Garillot, @huitseeker

2. Neural Networks & Deep Learning
• graphical models w/ inputs and outputs
• represents composition of differentiable functions
• deep learning : expressivity exponential w.r.t depth

3. Interesting results
• cat paper by Andrew Ng & Goole
• AlexNet by Toronto
• last week CNTK at speech
recognition parity with humans

4. Industrial results
• Autonomous Driving : Drive.ai, Comma.ai + the usual
suspects
• Drugs discovery : Deep Genomics (Frey) & Bayer
• Predictive Maintenance : Thales, Bosch
• optimistic pessimism (Moghimi, Manulife Financial Corp.)

5. DeepLearning in two steps : training,
applying
• training tends to require lots of data, (R)
• but applying does not (embedded, etc).
So that applying pre-trained models (Tensorframes) not the
technical/business challenge.
Enterprise : have lots of data yourself, what to apply ?

6. Benchmarks aren't distributed

7. Training, but how ?
New Amazon GPU instances ?

9. Deep Learning Training
• Facebook, Amazon, Google, Baidu, Microsoft have this
distributed
• But what if you’re not one of them ?

10. Training, but how ?

11. Distributing training
• basically distributing SGD (R)
• challenge is AllReduce Communication
• Sparse updates, async communications

12. Deeplearning4J
• the first commercial-grade, open-source, distributed deep-
learning library written for Java and Scala
• Skymind its commercial support arm

13. Scientific computing on the JVM
• libnd4j : Vectorization, 32-bit addressing, linalg (BLAS!)
• JavaCPP: generates JNI bindings to your CPP libs
• ND4J : numpy for the JVM, native superfast arrays
• Datavec : one-stop interface to an NDArray
• DeepLearning4J: orchestration, backprop, layer definition
• ScalNet: gateway drug, inspired from (and closely following)
Keras

14. Reinforcement learning

15. Killing the bottlenecks : generic
• swappable net backend : netty -> aeron (Hi Lightbend !)
• better support for binary data : big indexed tables
Binary, columnar, off-heap
• and more (Tamiya Onodera's group @ IBM Japan):
http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark

16. And if you don't care about Deep
Learning ?
• Spark-6442 : better linear algebra than breeze, please.
(sparse, performant, Java-compatible, and an OK license)
• SystemML got a best paper at VLDB'16, how about helping
out on nd4j ?
• ND4J only lacks sparse, but not for long ...

17. Questions ?