This document summarizes GPUs as complementing rather than replacing the Hadoop big data stack. It notes that wholesale replacement of the big data stack would be cost-prohibitive for many clients. The best approach is to sell GPUs for accelerated computation and a few other use cases to gain a foothold in the ecosystem. The functionality of Volta GPUs may change this ecosystem over time.

2. Who are we?
This slide shows that GPUs should complement the big data stack on the Hadoop ecosystem, rather than trying to
replace Hadoop etc. outright. Wholesale replacement of the big data stack will be cost-prohibitive to many clients. We
believe the right approach is to sell GPUs for accelerated computation and a few other use cases. That’s our beach
head. (Obviously, the widening functionality of the Volta will change the GPU ecosystem.)
Founded 2014
Distributed worldwide
Lots of activity in china

3. Skymind in China

4. Most deep learning frameworks
● Python based
● Symbolic variables with automatic differentiation in a computation graph
● Low level, focus on flexibility
● Supplemented by the ecosystem in python
● Run on cpu and gpu. Usually roll own distributed compute
● Big emphasis on research

5. Dl4j

6. Integrated in

7. Connectors via datavec (data types)
Supported Languages:
Mandarin
English
Japanese
Korean

8. Transform Process

9. Data Parallelism

10. Reinforcement Learning

11. UI

12. Nd4j Parameter Server
Aeron: More stable latency than GRPC and way faster
(25x!) than TF

13. Python support
PyJNIus
Jumpy (no copy
numpy nd4j
integration!)

14. Pretrained models/Transfer Learning
Model Zoo
Keras model
import
Explicit TL API

15. Upcoming
Nd4j Autodiff (yes it looks like what you’d expect)
Nd4j sparse arrays
Pipeline api