Atlanta Hadoop Users Meetup 09 21 2016
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Atlanta Hadoop Users Meetup 09 21 2016
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Atlanta Hadoop Users Meetup 09 21 2016
- 1. TENSORFLOW + SPARK DATAFRAMES = TENSORFRAMES Atlanta Hadoop Users Group Sept 21, 2016 Chris Fregly Research Scientist @ http://pipeline.io Thank You for Hosting, HashMap!!
- 2. WHO AM I Chris Fregly • Currently Research Scientist @ PipelineIO (http://pipeline.io) Contributor @ Apache Spark Committer @ Netflix Open Source Founder @ Advanced Spark andTensorFlow Meetup Author @ Advanced Spark (http://advancedspark.com) Creator @ PANCAKE STACK (http://pancake-stack.com) • Previously Streaming Data Engineer @ Netflix, Databricks,IBM Spark
- 3. ADVANCED SPARK AND TENSORFLOW MEETUP 4,400+ Members! Top 4 Spark Meetup!! Github Repo Stars + Forks DockerHub Repo Pulls
- 4. SPARK MEETUP TOMORROW NIGHT!
- 5. MLCONF 2 DAYS FROM NOW!!
- 6. CURRENT PIPELINE.IO RESEARCH • Model Deploying andTesting • Model Scaling and Serving • Online ModelTraining • Dynamic Model Optimizing
- 7. PIPELINE.IO DELIVERABLES • 100% Open Source!! • Github: • https://github.com/fluxcapacitor/ • DockerHub • https://hub.docker.com/r/fluxcapacitor
- 9. AGENDA • Neural Networks • GPUs • Tensorflow • TensorFrames
- 10. WHAT ARE NEURAL NETWORKS? • Like All Machine Learning, Goal is to Minimize Loss (Error) • Mostly Supervised Learning Classification • Many labeled training samples exist • Training • Step 1: Start with Random Guesses for Input Weights • Step 2: Calculate ErrorAgainst Labeled Data • Step 3: Determine Gradient Amount and Direction (+ or -) • Step 4: Back-propagate Gradient to Update Each Input Weight • Step 5: Repeat Step 1 until Convergence or Max Epochs Reached
- 12. CONVOLUTIONAL NEURAL NETWORKS • Apply Many Layers (aka. Filters) to Input • Each Layer/Filter Picks up on Features • Features not necessarily human-grokkable • Brute Force –Try Diff numLayers & layerSizes • Filter Examples • 3 Color Filters: RGB • Moving AVG for Time Series
- 13. MY FAVORITE USE CASE – STITCH FIX StitchFix Strata Conf SF 2016: Using Deep Learning to Create New Clothing Styles!
- 14. RECURRENT NEURAL NETWORKS Maintain State Enables Learning of Sequential Patterns Uses forText/NLP Prediction
- 15. CHARACTER RNNS Preserving State differentiates between 1st and 2nd ‘l’ to improve prediction
- 16. AGENDA • Neural Networks • GPUs • Tensorflow • TensorFrames
- 17. CPU VS GPU • Fundamentally Different than CPUs • Therefore,GPU/CUDA Programming Fundamentally Different
- 18. SAME INSTRUCTION, MULTIPLE DATA
- 19. MINIMIZE DATA DEPENDENCIES • More natural for structured,independent data • Tasks perform identical instructions in parallel on same-structured data • Reduce data dependencies as they limit parallelism Previous Instruction Previous Loop Iteration
- 20. MEMORY AND CORES
- 22. AGENDA • Neural Networks • GPUs • Tensorflow • TensorFrames
- 23. WHAT IS TENSORFLOW? • Google Open Source General Purpose Numerical Computation Engine • Happens to be Good for Neural Networks! • Tooling • Tensorboard (port 6006 == `goog` upside down!) à • DAG-based like Spark! • Computation graph is logical plan • Stored in Protobuf’s • Tensorflow converts logical to physical plan • Lots of Libraries • TFLearn (Tensorflow’s Scikit-learn Impl) • Tensorflow Serving (Prediction Layer) à
- 25. DEMO! AWS + Docker + GPU + Docker + Tensorflow
- 27. AGENDA • Neural Networks • GPUs • Tensorflow • TensorFrames
- 28. WHAT ARE TENSORFRAMES? • Bridge between Spark (JVM) and Tensorflow (C++) • Python and Scala Bindings for Application Code • Uses JavaCPP for JNI-level Integration • Must Install TensorFrames C++ Runtime Libs on All Spark Workers • Developed by Old Co-worker @ Databricks,Tim Hunter • PhD inTensors – He’s ”Mr..Tensor”
- 29. WHY TENSORFRAMES? • Why Not?! • Best of BothWorlds: Legacy Spark Support +Tensorflow • Mix and Match Spark ML + Tensorflow AI on Same Data • Tensorflow is DAG-based Similar to Spark • Enables Data-Parallel Model Training
- 30. DATA-PARALLEL MODEL TRAINING • Large Dataset are Partitioned Across HDFS Cluster • Computation Graph (Logical Plan) Passed to SparkWorkers • Workers Train on Each Data Partition in Parallel • Workers Periodically Aggregate (ie.AVG) Results • Aggregations happen in “Parameter Server” • Spark Master/Driver is Parameter Server
- 31. TENSORFLOW + MULTIPLE HOSTS/GPUS Multi-GPU,Data-ParallelTraining Step 1: CPU transfers model replica and (initial) gradients to each GPU Step 2: CPU synchronizes and waits for all GPUs to process batch Step 3: CPU copies all training results (gradients) back from GPU Step 4: CPU averages gradients from all GPUs Step 5: Repeat Step 1 with (new) gradients Code https://github.com/tensorflow/tensorflow/blob/master/ tensorflow/models/image/cifar10/ cifar10_multi_gpu_train.py
- 32. TENSORFRAME PERFORMANCE • Depends on Algorithm and Dataset, of course! • TensorFrames Require Extra Serialization JVM <-> C++ • What about Python Serialization from Python Bindings? • Should be minimal unless using Python UDFs • PySpark keeps small logical plan in Python layer • Physical operations happen in JVM (except Python UDFs!)
- 33. DEMO! TensorFrames in Python and Scala
- 34. THANK YOU!! Chris Fregly,Research Scientist @ PipelineIO • LinkedIn: https://linkedin.com/in/cfregly • Twitter: @cfregly http://pipeline.io