http://flink-forward.org/kb_sessions/deep-learning-with-apache-flink-and-dl4j/ Deep Learning has become very popular over the last few years in areas such as Image Recognition, Fraud Detection, Machine Translation etc. Deep Learning has proved to be very useful in handling unstructured data and extracting value from them. A big challenge with having to build deep learning models was the high cost of training them. With the recent advent of distributed frameworks like Apache Flink, Apache Spark etc.. it’s faster to train Deep Learning models in parallel on modern platform architecture. In this talk, we’ll be showing how to use Apache Flink Streaming with the open source Deep Learning framework, DeepLearning4j to perform large scale deep learning model training. We will show a demo of a Recurrent Neural Net that is trained for language modeling and have it generate text.

1. Deep Learning with Apache
Flink and DeepLearning4J
Flink Forward 2016,
Berlin, Germany
Suneel Marthi
@suneelmarthi

2. About me
•Senior Principal Software Engineer, Office of Technology, Red Hat Inc.
•Member of the Apache Software Foundation
•PMC member on Apache Mahout, Apache Pirk, Apache Incubator
•PMC Chair, Apache Mahout (April 2015 - April 2016)

3. Outline
● What is Deep Learning?
● Overview of DeepLearning4J Ecosystem
● Deep Learning Workflows
● ETL & Vectorization with DataVec
● Apache Flink and DL4J

4. What is Deep Learning?

5. Handwriting Recognition
Face Recognition (Facebook)
Image Generation
Self-Driving Cars

6. DL has been very successful with Image Classification
Dogs v/s Cats
https://www.kaggle.com/c/dogs-vs-cats

9. ● Deep Learning is a series of steps for automated feature extraction
o Based on techniques that have been around for several years
o Several techniques chained together to automate feature engineering
o “Deep” due to several interconnected layers of nodes stacked together
between the input and the output.

10. “Deep learning will make you acceptable to the learned; but it is only
an obliging and easy behaviour, and entertaining conversation, that
will make you agreeable to all companies”
- James Burgh

11. Popular Deep Neural Networks
● Deep Belief Networks
o Most popular architecture
● Convolutional Neural Networks
o Successful in image classification
● Recurrent Networks
o Time series Analysis
o Sequence Modelling

12. Deep Learning in Enterprise
● Ability to work with small and big data easily
o Don’t want to change tooling because we moved to Hadoop
● Ability to not get caught up in things like vectorization and ETL
o Need to focus on better models
o Understanding your data is very important
● Ability to experiment with lots of models

13. DeepLearning4J

14. ● “The Hadoop of Deep Learning”
o Command line driven
o Java and Scala APIs
o ASF 2.0 Licensed
● Java implementation
o Parallelization
o GPU support
 Support for multi-GPU per host
● Runtime Neutral
o Local, Spark, Flink
o AWS

15. DL4J Suite of Tools
● DeepLearning4J
o Main library for deep learning
● DataVec
o Extract, Transform, Load (ETL) and Vectorization library
● ND4J
o Linear Algebra framework
o Swappable backends (JBLAS, GPUs)
o Think NumPy on the JVM
● Arbiter
o Model evaluation, Hyperparameter Search and testing platform

17. DL4J: DataVec for Data Ingest and Vectorization
● Uses an Input/Output format
● Supports all major types of Input data (Text, Images, Audio, Video,
SVMLight)
● Extensible for Specialized Input Formats
● Interfaces with Apache Kafka

18. DL4J: ND4J
● Scientific computing library on JVM (think NumPy on JVM)
● Supports N-dimensional vector computations
● Supports GPUs via CUDA and Native JBlas

19. Learning Progressive Layers

20. Deep Learning Workflows

21. ● Data Ingestion and storage.
● Data cleansing and transformation.
● Split the dataset into Training, Validation and Test Data sets
- Apache Flink DataSet API for Data Ingestion and Transformation
Data Ingestion and Munging

22. DL Model Building
● Build Deep Learning Network and Train with Training Data
● Parameter Averaging
● Test and Validate the Model
● Repeat until satisfied
● Persist and Deploy the Model in Production

23. Prediction and Scoring
Deployed Model used to make predictions against Streaming data
-- Streaming Predictors using Apache Flink DataStream API

24. DL4J API Example
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.iterations(1)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(0.05)
.l2(0.001)
.list(4)
.layer(0, new DenseLayer.Builder().nIn(784).nOut(250)
.weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD)
.activation("relu").build())
.layer(1, new DenseLayer.Builder().nIn(250).nOut(10)
.weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD)
.activation("relu").build())
.layer(2, new DenseLayer.Builder().nIn(10).nOut(250)
.weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD)
.activation("relu").build())
.layer(3, new OutputLayer.Builder().nIn(250).nOut(784)
.weightInit(WeightInit.XAVIER)
.updater(Updater.ADAGRAD)
.activation("relu").lossFunction(LossFunctions.LossFunction.MSE)
.build())
.pretrain(false).backprop(true)
.build();

25. Building Deep Learning Workflows
● Flexibility to build / apply the model
o Local
o AWS, Spark, Flink (WIP)
● Convert data from a raw format into a baseline raw vector
o Model the data
o Evaluate the Model
● Traditionally all of these are tied together in one tool
o But this is a monolithic pattern

26. Load Existing Models in DL4J
String jsonModelConfig = loadTextFileFromDisk( pathToModelJSON );
MultiLayerConfiguration configFromJson = MultiLayerConfiguration.fromJson( jsonModelConfig );
FSDataInputStream hdfsInputStream_ModelParams = hdfs.open(new Path(
hdfsPathToModelParams ));
try (DataInputStream dis = new DataInputStream( hdfsInputStream_ModelParams )) {
INDArray newParams = Nd4j.read( dis );
}
MultiLayerNetwork network = new MultiLayerNetwork( configFromJson );
network.init();
network.setParameters(newParams);

27. Vectorizing Data - Iris Data Set
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
vectorized to
0.0 1:0.1666666666666665 2:1.0 3:0.021276595744680823 4:0.0
0.0 1:0.08333333333333343 2:0.5833333333333334 3:0.021276595744680823 4:0.0
0.0 1:0.0 2:0.7500000000000002 3:0.0 4:0.0
1.0 1:0.9583333333333335 2:0.7500000000000002 3:0.723404255319149 4:0.5217391304347826

28. DataVec - Command Line Vectorization
● Library of tools to vectorize - Audio, Video, Image, Text, CSV, SVMLight
● Convert the input data into vectors in a standardized format (SVMLight,
Text, CSV etc)
o Adaptable with custom input/output formats
● Open Source, ASF 2.0 Licensed
o https://github.com/deeplearning4j/DataVec
o Part of DL4J suite

29. Workflow Configuration (iris_conf.txt)
canova.input.header.skip=false
canova.input.statistics.debug.print=false
canova.input.format=org.canova.api.formats.input.impl.LineInputFormat
canova.input.directory=src/test/resources/csv/data/uci_iris_sample.txt
canova.input.vector.schema=src/test/resources/csv/schemas/uci/iris.txt
canova.output.directory=/tmp/iris_unit_test_sample.txt
canova.output.format=org.canova.api.formats.output.impl.SVMLightOutputFormat

30. Iris Canova Vector Schema
@RELATION UCIIrisDataset
@DELIMITER ,
@ATTRIBUTE sepallength NUMERIC !NORMALIZE
@ATTRIBUTE sepalwidth NUMERIC !NORMALIZE
@ATTRIBUTE petallength NUMERIC !NORMALIZE
@ATTRIBUTE petalwidth NUMERIC !NORMALIZE
@ATTRIBUTE class STRING !LABEL

31. Model Iris using Canova Command Line
./bin/canova vectorize -conf /tmp/iris_conf.txt
Output vectors written to: /tmp/iris_svmlight.txt
./bin/dl4j train –conf /tmp/iris_conf.txt
[ …log output… ]
./bin/arbiter evaluate –conf /tmp/iris_conf.txt
[ …log output… ]

32. DL4J + Apache Flink

33. • Apache Flink support for Dl4J : DataVec (In progress)
• Streaming Predictors using Flink : Kafka (In progress)
• Possible

34. Present DL4J – Flink work in progress
• Support for DL4J : DataVec
• Streaming Predictions with Apache Flink
Future Work
• Flink support for DL4J: Arbiter for Hyperparameter Search
• Flink support for DeepLearning4J to be able to build MultiLayer DL
configurations.

35. https://github.com/deeplearning4j

36. Credits

37. Skymind.io Team
• Adam Gibson
• Chris V. Nicholson
• Josh Patterson

38. Questions ???