Applied Deep Learning with Spark and Deeplearning4j
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Hadoop Summit 2015
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Applied Deep Learning with Spark and Deeplearning4j
- 1. Enterprise Deep Learning with DL4J Skymind Josh Patterson Hadoop Summit 2015
- 2. Presenter: Josh Patterson Past Research in Swarm Algorithms Real-time optimization techniques in mesh sensor networks TVA / NERC Smartgrid, Sensor Collection, and Big Data Cloudera Today Patterson Consulting josh@pattersonconsultingtn.com Skymind (Advisor) josh@skymind.io / @jpatanooga Architecture, Committer on DL4J
- 3. Topics • What is Deep Learning? • What is DL4J? • Enterprise Grade Deep Learning Workflows
- 4. WHAT IS DEEP LEARNING?
- 5. We Want to be able to recognize Handwriting This is a Hard Problem
- 6. Automated Feature Engineering • Deep Learning can be thought of as workflows for automated feature construction – Where previously we’d consider each stage in the workflow as unique technique • Many of the techniques have been around for years – But now are being chained together in a way that automates exotic feature engineering • As LeCun says: – “machines that learn to represent the world”
- 9. These are the features learned at each neuron in a Restricted Boltzmann Machine (RBMS) These features are passed to higher levels of RBMs to learn more complicated things. Part of the “7” digit
- 11. Deep Learning Architectures • Deep Belief Networks – Most common architecture • Convolutional Neural Networks – State of the art in image classification • Recurrent Networks – Timeseries • Recursive Networks – Text / image – Can break down scenes
- 12. DL4J Next Generation Deep Learning with
- 13. DL4J • “The Hadoop of Deep Learning” – Command line driven – Java, Scala, and Python APIs – ASF 2.0 Licensed • Java implementation – Parallelization (Yarn, Spark) – GPU support • Also Supports multi-GPU per host • Runtime Neutral – Local – Hadoop / YARN – Spark – AWS • https://github.com/deeplearning4j/deeplearning4j
- 14. Issues in Machine Learning • Data Gravity – We need to process the data in workflows where the data lives • If you move data you don’t have big data – Even if the data is not “big” we still want simpler workflows • Integration Issues – Ingest, ETL, Vectorization, Modeling, Evaluation, and Deployment issues – Most ML tools are built with previous generation architectures in mind • Legacy Architectures – Parallel iterative algorithm architectures are not common
- 15. DL4J Suite of Tools • DL4J – Main library for deep learning • Canova – Vectorization library • ND4J – Linear Algebra framework – Swappable backends (JBLAS, GPUs): • http://www.slideshare.net/agibsonccc/future-of-ai-on-the-jvm • Arbiter – Model evaluation and testing platform
- 16. DEEP LEARNING AND SPARK Enterprise Grade
- 17. DL4J and Parallelization 17 Model Training Data Worker 1 Master Partial Model Global Model Worker 2 Partial Model Worker N Partial Model Split 1 Split 2 Split 3 … Traditional Serial Training Modern Parallel Engine (Hadoop / Spark)
- 18. DL4J Spark / GPUs via API public class SparkGpuExample { public static void main(String[] args) throws Exception { Nd4j.MAX_ELEMENTS_PER_SLICE = Integer.MAX_VALUE; Nd4j.MAX_SLICES_TO_PRINT = Integer.MAX_VALUE; // set to test mode SparkConf sparkConf = new SparkConf() .setMaster("local[*]").set(SparkDl4jMultiLayer.AVERAGE_EACH_ITERATION,"false") .set("spark.akka.frameSize", "100") .setAppName("mnist"); System.out.println("Setting up Spark Context..."); JavaSparkContext sc = new JavaSparkContext(sparkConf); MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() .momentum(0.9).iterations(10) .weightInit(WeightInit.DISTRIBUTION).batchSize(10000) .dist(new NormalDistribution(0, 1)).lossFunction(LossFunctions.LossFunction.RMSE_XENT) .nIn(784).nOut(10).layer(new RBM()) .list(4).hiddenLayerSizes(600, 500, 400) .override(3, new ClassifierOverride()).build(); System.out.println("Initializing network"); SparkDl4jMultiLayer master = new SparkDl4jMultiLayer(sc,conf); DataSet d = new MnistDataSetIterator(60000,60000).next(); List<DataSet> next = d.asList(); JavaRDD<DataSet> data = sc.parallelize(next); MultiLayerNetwork network2 = master.fitDataSet(data); Evaluation evaluation = new Evaluation(); evaluation.eval(d.getLabels(),network2.output(d.getFeatureMatrix())); System.out.println("Averaged once " + evaluation.stats()); INDArray params = network2.params(); Nd4j.writeTxt(params,"params.txt",","); FileUtils.writeStringToFile(new File("conf.json"), network2.getLayerWiseConfigurations().toJson()); } }
- 19. Turn on GPUs and Spark <dependency> <groupId>org.deeplearning4j</groupId> <artifactId>dl4j-spark</artifactId> <version>${dl4j.version}</version> </dependency> <dependency> <groupId>org.nd4j</groupId> <artifactId>nd4j-jcublas-7.0</artifactId> <version>${nd4j.version}</version> </dependency>
- 20. Building Deep Learning Workflows • We need to get data from a raw format into a baseline raw vector – Model the data – Evaluate the Model • Traditionally these are all tied together in one tool – But this is a monolithic pattern – We’d like to apply the unix principles here • The DL4J Suite of Tools lets us do this
- 21. Modeling UCI Data: Iris • We need to vectorize the data – Possibly with some per column transformations – Let’s use Canova • We then need to build a deep learning model over the data – We’ll use the DL4J lib to do this • Finally we’ll evaluate what happened – This is where Arbiter comes in
- 22. Canova for Command Line Vectorization • Library of tools to take – Audio – Video – Image – Text – CSV data • And convert the input data into vectors in a standardized format – Adaptable with custom input/output formats • Open Source, ASF 2.0 Licensed – https://github.com/deeplearning4j/Canova – Part of DL4J suite
- 23. Vectorization with Canova • Setup the configuration file – Input Formats – Output Formats – Setup data types to vectorize • Setup the schema transforms for the input CSV data • Generate the SVMLight vector data as the output – with the command line interface
- 25. 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
- 26. Model UCI Iris From CLI ./bin/canova vectorize -conf /tmp/iris_conf.txt File path already exists, deleting the old file before proceeding... 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… ]
- 27. Skymind as DL4J Distribution • Just as Redhat was to Linux – A distribution of Linux with enterprise grade packaging • Just as Cloudera/Horton are to Hadoop – A distribution of Apache Hadoop with enterprise grade packaging • Skymind is to DL4J – A distribution of DL4J (+tool suite) with enterprise grade packaging
- 28. Questions? Thank you for your time and attention “Deep Learning: A Practitioner’s Approach” (Oreilly, October 2015)
Notes de l'éditeur
- we plot the learned filter for each hidden neuron, one per column of W. Each filter is of the same dimension as the input data, and it is most useful to visualize the filters in the same way as the input data is visualized. In the cases of image patches, we show each filter as an image patch
- we plot the learned filter for each hidden neuron, one per column of W. Each filter is of the same dimension as the input data, and it is most useful to visualize the filters in the same way as the input data is visualized. In the cases of image patches, we show each filter as an image patch
- we plot the learned filter for each hidden neuron, one per column of W. Each filter is of the same dimension as the input data, and it is most useful to visualize the filters in the same way as the input data is visualized. In the cases of image patches, we show each filter as an image patch
- POLR: Parallel Online Logistic Regression Talking points: wanted to start with a known tool to the hadoop community, with expected characteristics Mahout’s SGD is well known, and so we used that as a base point
- API as early adopter entry point
- This is how we enable spark execution and gpu integration for the back end Code is slightly different for Spark job Code is same linear algebra, no changes, for math / nd4j impl
- Next release: we’re expanding the userbase with a CLI front end to the whole thing The domain expert rarely knows how to code Want to make it easier for someone who knows “bash” to get involved (still will need some eng help)