Hadoop summit 2016

© Hortonworks Inc. 2011 – 2014. All Rights Reserved

Deep Learning using Spark and DL4J for fun and profit

Adam Gibson and Dhruv Kumar

2015Version 1.0


Who are we?

Adam Gibson- Co founder of Skymind - Wrote DeepLearning4J, ND4J

Dhruv Kumar- Sr Solutions Architect, HWX- MS Umass, Mahout, ASF


In this talk

- What’s Deep Learning?- Architectures - Implementation and Libraries in Real Life- Demo!


Deep Learning

• One of the many pattern recognition techniques in Data Science

• Excels at rich media applications:• Image recognition• Speech translation• Voice recognition

• Loosely inspired by human brain models• Synonymous with Artificial Neural Networks, Multi Layer

Networks


Enterprise use cases


Doing this in real life for enterprise

© Hortonworks Inc. 2011 – 2014. All Rights ReservedPage 7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved

HDP FOR DATA AT REST

HDF FOR DATA IN MOTION

ACTIONABLEINTELLIGENCE

MODERN DATA APPSModern Data Applications in Enterprise: Connected, Fast, Intelligent

PERISHABLE INSIGHTS

HISTORICAL INSIGHTS

INTERNETOF

ANYTHING

How do we realize MDA in a Hadoop Centric World?

HDF

Hadoop

HDFS

HBase Hive SOLR

YARN

Storm

Service Management /

Workflow

SIEM

Spark

Raw Network Stream

Network Metadata Stream

Data Stores

Syslog

Raw Application Logs

Other Streaming Telemetry

www.hortonworks.com

NiFi 1

NiFi 2

Storm 1 Kafka 1

Storm 2 Kafka 2

Storm 3 Kafka 3

DataNode 1 HBase 1

Source 1

Source 2

Source 3

Source N

NiFi Nodes

Edge Nodes

Master NodesClients 1

Clients 2

DataNode 2 Hbase 2

DataNode 3 Hbase 3

DataNode 4 Hbase 4

DataNode 5 Hbase 5

DataNode 6 Hbase 6

DataNode 7 Hbase 7

DataNode 8 Hbase 8

DataNode 9 DataNode 10

DataNode 31 DataNode 32

Master 1

Master 2

Master 3

Master 4

Master 5

Worker Nodes

HDF

HDP

World Azure


Storm/Spark Streaming

Storm

Detailed Reference Architecture

HDF

Flume

Sink toHDFS

Transform

Interactive

UI Framework

Hive

Hive

HDFS

HDFS

SOURCE DATA

Server logs

Application Logs

Firewall Logs

CRM/ERP

Sensor

Kafka

Kafka

Stream toHDF

Forward to Storm

Real Time Storage

Spark-ML

Pig

Alerts

Bolt toHDFS

Dashboard

Silk

JMSAlerts

Hive Server

HiveServer

Reporting

BI Tools

High Speed Ingest

Real-Time

Batch Interactive

Machine LearningModels

Spark

Pig

Alerts SQOOP

Flume

Iterative ML

Hbase/Pheonix

HBaseEvent Enrichment

Spark-Thrift

Pig

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For Model Building: Typical Workflow

1. Ingest training data and store it2.Split data set into: training, testing and validation sets3.Vectorize and extract features to go into next step4.Architect multi layer network, initialize5.Feed data and train6.Test and Validate7.Repeat steps 4 and 5 until desired8.Store model9.Put model in app, start generalizing on real data.


So what do you get?

1. Ingest training data and store it using Nifi or other ingest tools2.Split data set into: training, testing and validation sets3.Vectorize and extract features to go into next step4.Architect multi layer network, initialize5.Feed data and train6.Test and Validate7.Repeat steps 4 and 5 until desired8.Store model9.Put model in app, start generalizing on real data.

Steps 2, 3, 4 and 5: Use libraries such as Deeplearning4j


Deeplearning4j Architecture


DL4J: Canova for Vectorization and Ingest

• Canova uses an input/output format system (similar to how Hadoop uses MapReduce)

• Supports all major types of input data (text, CSV, audio, image and video)

• Can be extended for specialized input formats• Connects to Kafka


ND4J:

• N-dimensional vector library• Scientific computing for JVM• DL4J uses it to do linear algebra for backpropagation• Supports GPUs via CUDA and Native via Jblas • Deploys on Android• DL4J code remains unchanged whether using GPU or

CPU


How to chose a Neural Net in DL4J core?


Demo!


Thank Youhortonworks.com

Hadoop summit 2016

Data & Analytics

Transcript of Hadoop summit 2016