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Advanced spark deep learning
This document discusses DeepLeanring4j, a framework for data parallel deep learning on Spark. It provides an overview of the current landscape of deep learning tools, and proposes a solution using Javacpp, libnd4j, and SKIL (Skymind Intelligence Layer) to leverage Spark and the JVM ecosystem while allowing for heterogeneous compute. Key aspects include efficient access to image and audio data, an ND4j library for numerical compute, deployment via Juju, and an ETL pipeline interface in Canova. The goal is to build on the JVM's strengths while addressing its limitations for numerical compute through hardware acceleration.
Advanced spark deep learning
- 1. DeepLeanring4j Data Parallel deeplearning on spark
- 2. The JVM istoo slow for numerical compute Great at network I/O and data access Great streaming infrastructure Hardware accel required Spark - Data Access Layer. Cuda - Compute layer
- 3. Current Landscape Spark assumescolumnar data Binary (audio/images) is becoming more important HDFS is great for storing blobs SQL doesn’t work for pixels and audio frames The ingredients are here for something great
- 4. The solution Javacpp (cythonfor java) 64 bit pointers for efficient contiguous access of image and audio data Leverage java’s distributed systems ecosystem Add new numerical compute layer (libnd4j) Allow for heterogeneous compute Off heap memory Easy deployment Data pipelines as a first concern
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- 6. JavaCpp Auto generate JNIbindings for C++ by parsing classes Allows for easy maintenance and deployment of c++ binaries in java Write efficient ETL pipelines for images via opencv (javacv) Integrate other c++ deep learning frameworks (tensorflow,caffe,..) Allows for productionization of fast (but academic) C++ code using java (kafka,spark) for ETL 64 bit pointers (wasn’t possible before)
- 7. “Actual” Streaming frameworks Kafka Flink SparkStreaming Apex
- 9. Nd4j Heterogenous codebase Supports cuda,x86 and soon (power) Shared indexing logic for writing ndarray routines Memory management in java (even cuda memory!) Openmp on cpu + routines for common things such as reduce Pinned memory and async operations JIT allocation Spark friendly (runs on multiple threads and devices)
- 10. Deployment Juju Runs as sparkjob Easy to embed in production
- 11. Canova One interface forETL Integrates with spark Easy to extend to write your own custom data pipelines One interface for generating NDArrays
- 12. Conclusion Built to befriendly to the JVM ecosystem Allows java to do what its good at Numpy in java means easy to port things like scikit learn Data Parallel means commodity hardware JVM assumes works
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