BigDL Deep Learning Library on HDInsight 5/23/2018 1:25 PM THR3040 BigDL Deep Learning Library on HDInsight Microsoft Ignite September , 2017 Xiaoyong Zhu, Microsoft Sergey Ermolin, Intel © Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

BigDL Deep Learning Library on HDInsight Microsoft Ignite September , 2017 Xiaoyong Zhu, Microsoft Sergey Ermolin, Intel

BIGDL WITHIN SPARK FRAMEWORK End-to-end Big Data Analytics with Deep Learning Functionalities Directly on Spark Natively integrated with Big Data (Hadoop/Spark) ecosystem Massively distributed, scale out Sends compute to data Fault tolerance Elasticity Incremental scaling Dynamic resource sharing BigDL https://software.intel.com/bigdl

BigDL features BigDL Python API Scala API Examples Documents Seq2Seq Vgg ResNet Lenet Inception SGD Adagrad Cross Entropy Distributed Training Batch Normalization Other 100+ Layers Tensor MKL Integration Spatial Convolution RELU LRN RNN Pooling BigDL

https://software.intel.com/bigdl BigDL Features Distributed Deep learning applications (training, fine-tuning & prediction) on Apache Spark* No changes to the existing Hadoop/Spark clusters needed https://software.intel.com/bigdl

https://github.com/intel-analytics/BigDL BIGDL benefits Allows to write deep learning applications as standard Spark programs Runs on top of existing Spark or Hadoop/Hive clusters Adds rich Deep Learning functionalities to Apache Spark Feature parity with Caffe and TensorFlow. High performance - Intel MKL and multi-threaded programming Efficient scale-out with an all-reduce communications on Spark https://github.com/intel-analytics/BigDL BigDL has been open-sourced since 2016: https://software.intel.com/bigdl

BigDL can re-use/fine-tune models from other frameworks BigDL Model File Load existing Caffe/Torch/TF Model Allows for transition from single-node to distributed application deployment Useful for inference Allows for minor model tuning Allows for model sharing between Data Scientists and Production Engr. Scoring can be done *outside of Spark*, as a Java app Caffe Model File Load BigDL TensorFlow Model File Save Torch Model File Storage https://software.intel.com/bigdl

BigDL integration with spark streaming Integration with Spark Streaming for runtime training and prediction HDFS/S3 Kafka Flume Kinesis Twitter BigDL Model RDDs Train Spark Streaming Evaluator StreamWriter Predict https://software.intel.com/bigdl

https://software.intel.com/bigdl Python API Support Based on PySpark, Python API in BigDL allows use of existing Python libs: Numpy Scipy Pandas Scikit-learn Matplotlib $pip install bigdl https://software.intel.com/bigdl

Jupyter Notebook support Running BigDL applications directly in Jupyter notebooks Share and Reproduce Notebooks can be shared with others Easy to reproduce and track Rich Content Texts, images, videos, LaTeX and JavaScript Code can also produce rich contents Rich toolbox Apache Spark, from Python, R and Scala Pandas, scikit-learn, ggplot2, dplyr, etc https://software.intel.com/bigdl

Visualization of optimization process - tensorboard BigDL integration with TensorBoard TensorBoard is a suite of web applications from Google for visualizing and understanding deep learning applications https://software.intel.com/bigdl

HDInsight on Linux Overview

HDInsight (Linux) supports… Hive & Hive LLAP & Standard Hadoop:  ETL, reporting, ad hoc queries, data mining and analysis, log analysis, data warehousing… Spark: real-time analysis, streaming analysis, machine learning, ETL, graph analysis, real-time SQL query R Server: advanced analytics over big data, machine learning, statistical analysis Hbase & Phoenix: No SQL storage with SQL friendly interfaces (Phoenix), suitable for key-value store or schema-changing logs Storm: real-time streaming analysis Kafka: high throughput data ingestion engine

Scale compute & storage independently Gateway nodes Head Worker Edge Zookeeper nodes Azure Blob Storage or Azure Data Lake Store

Demo

Train a CNN model on MNIST dataset Install BigDL on HDInsight – easy as 1-2-3 Configure Spark settings Set up BigDL parameters Set up network topologies Run, train, and see results

Set up HDInsight Cluster in a few steps

Monitor HDInsight Cluster via Ambari GUI

BigDL is easily installed and built (“Deploy to Azure”)

Spark Session configuration

Network Layout

To learn more about BigDL + HDInsight github.com/intel-analytics/BigDL software.intel.com/bigdl https://blogs.msdn.microsoft.com/azuredatalake/2017/03/17/ho w-to-use-bigdl-on-apache-spark-for-azure-hdinsight/

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5/23/2018 1:25 PM © Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.