Deep Learning with Apache
Flink and DeepLearning4J
Flink Forward 2016,
Berlin, Germany
Suneel Marthi
@suneelmarthi
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)
Outline
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What is Deep Learning?
Overview of DeepLearning4J Ecosystem
Deep Learning Workflows
ETL & Vectorization with DataVec
Apache Flink and DL4J
What is Deep Learning?
Handwriting Recognition
Face Recognition (Facebook)
Self-Driving Cars
Image Generation
DL has been very successful with Image Classification
Dogs v/s Cats
https://www.kaggle.com/c/dogs-vs-cats
● 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.
“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
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
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
DeepLearning4J
● “The Hadoop of Deep Learning”
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Command line driven
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Java and Scala APIs
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ASF 2.0 Licensed
Java implementation
o Parallelization
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GPU support
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Support for multi-GPU per host
Runtime Neutral
o Local, Spark, Flink
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AWS
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
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
DL4J: ND4J
● Scientific computing library on JVM (think NumPy on JVM)
● Supports N-dimensional vector computations
● Supports GPUs via CUDA and Native JBlas
Learning Progressive Layers
Deep Learning Workflows
Data Ingestion and Munging
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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
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
Prediction and Scoring
Deployed Model used to make predictions against Streaming data
-- Streaming Predictors using Apache Flink DataStream API
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();
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
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);
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
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
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
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
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… ]
DL4J + Apache Flink
• Apache Flink support for Dl4J : DataVec (In progress)
• Streaming Predictors using Flink : Kafka (In progress)
• Possible
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.
https://github.com/deeplearning4j
Credits
Skymind.io Team
•
Adam Gibson
•
Chris V. Nicholson
•
Josh Patterson
Questions ???