DeepLearning4j, data parallel deep learning on spark

[2016-09-01 Thu 19:04:05] speaker: Adam Gibson, skymind.io event: Data Science SG

skymind started in 2013, San Francisco

JVM is slow for numerical compute

• hardware accel required
• spark - data access layer
• cuda - compute layer

why JVM/Java?

• existing systems are in Java, e.g. Hadoop, Kafka
• target the data engineering, production deployment

current landscape

• spark assumes columnar data
• audio/images are hard to work with

the solution

• javacpp (cython for java)
• off heap memory
• numerical compute layer (nd4j)
  • supports cuda, x86
• training as a spark job
• keras on python for prototyping

applications

• image search
• voice search
• recommendation
• ad targetting
• object recognition
• translation
• spam filtering