In my talk I will discuss and show examples of using Apache Hadoop, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. As part of my talk I will walk through using Apache NXNet Pre-Built Models, MXNet's New Model Server with Apache NiFi, executing MXNet with Apache NiFi and running Apache MXNet on edge nodes utilizing Python and Apache MiniFi. This talk is geared towards Data Engineers interested in the basics of Deep Learning with open source Apache tools in a Big Data environment. I will walk through source code examples available in github and run the code live on an Apache Hadoop / YARN / Apache Spark cluster. This will be an introduction to executing Deep Learning Pipelines in an Apache Big Data environment. My talk at Data Works Summit Sydney was listed in top 7 -> https://hortonworks.com/blog/7-sessions-dataworks-summit-sydney-see/ Also have speak at and run Future of Data Princeton and at Oracle Code NYC. Ref: https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-running-apac.html https://dzone.com/refcardz/introduction-to-tensorflow Speaker Timothy Spann, Solutions Engineer, Hortonworks

1. 1 © Hortonworks Inc. 2011–2018. All rights reserved.
© Hortonworks, Inc. 2011-2018. All rights reserved. | Hortonworks confidential and proprietary information.
Apache Deep Learning 101
Timothy Spann, Solutions Engineer
Hortonworks @PaaSDev

2. 2 © Hortonworks Inc. 2011–2018. All rights reserved.
Disclaimer
• This document may contain product features and technology directions that are under
development, may be under development in the future or may ultimately not be
developed.
• Technical feasibility, market demand, user feedback, and the Apache Software
Foundation community development process can all effect timing and final delivery.
• This document’s description of these features and technology directions does not
represent a contractual commitment, promise or obligation from Hortonworks to deliver
these features in any generally available product.
• Product features and technology directions are subject to change, and must not be
included in contracts, purchase orders, or sales agreements of any kind.
• Since this document contains an outline of general product development plans,
customers should not rely upon it when making a purchase decision.

3. 3 © Hortonworks Inc. 2011–2018. All rights reserved.
Agenda
• Data Engineering With Deep Learning
• Apache MXNet Pre-Built Models
• Apache NiFi with Apache MXNet
• Apache MXNet at the Edge with MiniFi
• Apache MXNet Model Server With Apache NiFi
• Apache MXNet in Apache Zeppelin Notebooks
• Apache MXNet On YARN
• Apache OpenNLP and Apache Tika
• Demos
• Questions

4. 4 © Hortonworks Inc. 2011–2018. All rights reserved.
Deep Learning for Big Data Engineers
Multiple users, frameworks, languages, data sources & clusters
BIG DATA ENGINEER
• Experience in ETL
• Coding skills in Scala,
Python, Java
• Experience with Apache
Hadoop
• Knowledge of database
query languages such as
SQL
• Knowledge of Hadoop tools
such as Hive, or Pig
• Expert in ETL (Eating, Ties
and Laziness)
• Social Media Maven
• Deep SME in Buzzwords
• No Coding Skills
• Interest in Pig and Falcon
CAT AI
• Will Drive your Car
• Will Fix Your Code
• Will Beat You At Q-Bert
• Will Not Be Discussed
Today
• Will Not Finish This Talk For
Me, This Time
http://gluon.mxnet.io/chapter01_crashcourse/preface.html

5. 5 © Hortonworks Inc. 2011–2018. All rights reserved.
Use Cases
So Why Am I Orchestrating These Complex Deep Learning Workflows?
Computer Vision
• Object Recognition
• Image Classification
• Object Detection
• Motion Estimation
• Annotation
• Visual Question and Answer
• Autonomous Driving
• Speech to Text
• Speech Recognition
• Chat Bot
• Voice UI
Speech Recognition Natural Language Processing
• Sentiment Analysis
• Text Classification
• Named Entity Recognition
https://github.com/zackchase/mxnet-the-straight-dope
Recommender Systems
• Content-based
Recommendations

6. 6 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache Deep Learning Flow
Ingestion
Simple Event Processing
Engine
Stream Processing
Destination
Data Bus
Build
Predictive Model
From Historical Data
Deploy
Predictive Model
For Real-time Insights
Perishable Insights
Historical Insights

7. 7 © Hortonworks Inc. 2011–2018. All rights reserved.
© Hortonworks Inc. 2011
Streaming Apache Deep Learning
Page 7
Data Acquisition
Edge Processing
Deep Learning
Real Time Stream Analytics
Rapid Application Development
IoT
ANALYTICS
CLOUD
Acquire Move
Routing
&
Filtering
Deliver Parse Analysis
Aggregation
Modeling

8. 8 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache Deep Learning Components
Streaming Analytics
Manager
Machine Learning
Distributed queue
Buffering
Process decoupling
Streaming and SQL
Orchestration
Queueing
Simple Event Processing
REST API
Secure Spark Execution

9. 9 © Hortonworks Inc. 2011–2018. All rights reserved.
Streaming Analytics
Manager
Run everywhere
Detect metadata and data
Extract metadata and data
Content Analysis
Deep Learning Framework
Entity Resolution
Natural Language Processing
Apache Deep Learning Components

10. 10 © Hortonworks Inc. 2011–2018. All rights reserved.
http://mxnet.incubator.apache.org/
• Cloud ready
• Experienced team (XGBoost)
• AWS, Microsoft, NVIDIA, Baidu, Intel backing
• Apache Incubator Project
• Run distributed on YARN
• In my early tests, faster than TensorFlow.
• Runs on Raspberry PI, Nvidia Jetson TX1
and other constrained devices
• Great documentation
• Gluon
• Great Python Interaction
• Model Server Available
• ONNX Support
• Now in Version 1.1!
• Great Model Zoo
https://mxnet.incubator.apache.org/how_to/cloud.html
https://github.com/apache/incubator-mxnet/tree/1.1.0/example

11. 11 © Hortonworks Inc. 2011–2018. All rights reserved.
Deep Learning Architecture
HDP Node X
Node
Manager
Datanode
HBase
Region
HDP Node Y
Node
Manager
Datanode
HBase
Region
HDF Node
Apache NiFi
Zookeeper
Apache Spark
MLib
Apache Spark
MLib
GPU Node
Neural Network
Apache Spark
MLib
Apache Spark
MLib
Pipeline
GPU Node
Neural Network
Pipeline
MiNiFi Java
Agent
MiNiFi C++
Agent
HDF Node
Apache NiFi
Zookeeper
Apache Livy

12. 12 © Hortonworks Inc. 2011–2018. All rights reserved.
What do we want to do?
• MiniFi ingests camera images and
sensor data
• MiniFi executes Apache MXNet at the
edge
• Run Apache MXNet Inception to
recognize objects in image
• Apache NiFi stores images, metadata
and enriched data in Hadoop
• Apache NiFi ingests social data and
REST feeds
• Apache OpenNLP and Apache Tika for
textual data

13. 13 © Hortonworks Inc. 2011–2018. All rights reserved.
Aggregate all data from sensors, drones, logs, geo-location devices,
machines and social feeds
Collect: Bring Together
Mediate point-to-point and bi-directional data flows, delivering data
reliably to Apache HBase, Apache Hive, HDFS, Slack and Email.
Conduct: Mediate the Data Flow
Parse, filter, join, transform, fork, query, sort, dissect; enrich with weather,
location, sentiment analysis, image analysis, object detection, image
recognition, voice recognition with Apache Tika, Apache OpenNLP and
Apache MXNet.
Curate: Gain Insights

14. 14 © Hortonworks Inc. 2011–2018. All rights reserved.
Why Apache NiFi?
• Guaranteed delivery
• Data buffering
- Backpressure
- Pressure release
• Prioritized queuing
• Flow specific QoS
- Latency vs. throughput
- Loss tolerance
• Data provenance
• Supports push and pull
models
• Hundreds of processors
• Visual command and
control
• Over a fifty sources
• Flow templates
• Pluggable/multi-role
security
• Designed for extension
• Clustering
• Version Control

15. 15 © Hortonworks Inc. 2011–2018. All rights reserved.
• Apache MXNet via Execute Process (Python)
• Apache MXNet Running on Edge Nodes (MiniFi) S2S
• Apache MXNet Model Server Integration (REST API)
Not Covered Today
• *Dockerized Apache MXNet on Hadoop YARN 3 with NVidia GPU
• *Apache MXNet on Spark
Apache NiFi Integration with Apache MXNet Options

16. 16 © Hortonworks Inc. 2011–2018. All rights reserved.
• https://github.com/apache/incubator-mxnet/tree/master/tools/coreml
• https://github.com/Leliana/WhatsThis
• https://github.com/apache/incubator-mxnet/tree/master/amalgamation/jni
• https://hub.docker.com/r/mxnet/
• https://github.com/apache/incubator-mxnet/tree/master/scala-
package/spark
Other Options

17. 17 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Pre-Built Models
• CaffeNet
• SqueezeNet v1.1
• Inception v3
• Single Shot Detection (SSD)
• VGG19
• ResidualNet 152
• LSTM
http://mxnet.incubator.apache.org/model_zoo/index.html
https://github.com/dmlc/mxnet-model-gallery

18. 18 © Hortonworks Inc. 2011–2018. All rights reserved.
python3 -W ignore analyze.py
{"uuid": "mxnet_uuid_img_20180208204131", "top1pct": "30.0999999046", "top1":
"n02871525 bookshop, bookstore, bookstall", "top2pct": "23.7000003457", "top2":
"n04200800 shoe shop, shoe-shop, shoe store", "top3pct": "4.80000004172", "top3":
"n03141823 crutch", "top4pct": "2.89999991655", "top4": "n04370456 sweatshirt",
"top5pct": "2.80000008643", "top5": "n02834397 bib", "imagefilename":
"images/tx1_image_img_20180208204131.jpg", "runtime": "2"}
Apache MXNet via Python (OSX Local with WebCam)

19. 19 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Running on with Apache NiFi Node

20. 20 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Running on Edge Nodes (MiniFi)
https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html
https://github.com/tspannhw/mxnet_rpi
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-
running-apac.html

21. 21 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Model Server with Apache NiFi
https://community.hortonworks.com/articles/155435/using-the-new-mxnet-model-server.html
sudo pip3 install mxnet-model-server --upgrade

22. 22 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Running in Apache Zeppelin

23. 23 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet on Apache YARN
https://github.com/tspannhw/nifi-mxnet-yarn
https://community.hortonworks.com/articles/174399/apache-deep-learning-101-using-apache-
mxnet-on-apa.html
dmlc-submit --cluster yarn --num-workers 1 --server-cores 2
--server-memory 1G --log-level DEBUG --log-file mxnet.log analyzeyarn.py

24. 24 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache OpenNLP for Entity Resolution
Processor
https://github.com/tspannhw/nifi-nlp-
processor
Requires installation of NAR and Apache
OpenNLP Models
(http://opennlp.sourceforge.net/models-1.5/).
This is a non-supported processor that I wrote
and put into the community. You can write
one too!
Apache OpenNLP with Apache NiFi
https://community.hortonworks.com/articles/80418/open-nlp-example-apache-nifi-processor.html

25. 25 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache Tika with Apache NiFi
https://community.hortonworks.com/articles/163776/parsing-any-document-with-apache-nifi-15-with-apac.html
https://community.hortonworks.com/articles/81694/extracttext-nifi-custom-processor-powered-by-apach.html
https://community.hortonworks.com/articles/76924/data-processing-pipeline-parsing-pdfs-and-identify.html
https://github.com/tspannhw/nifi-extracttext-processor
https://community.hortonworks.com/content/kbentry/177370/extracting-html-from-pdf-excel-and-word-
documents.html

26. 26 © Hortonworks Inc. 2011–2018. All rights reserved.
Questions?

27. 27 © Hortonworks Inc. 2011–2018. All rights reserved.
Contact
https://github.com/tspannhw/ApacheDeepLearning101
https://community.hortonworks.com/users/9304/tspann.html
https://dzone.com/users/297029/bunkertor.html
https://www.meetup.com/futureofdata-princeton/
https://twitter.com/PaaSDev
https://community.hortonworks.com/articles/155435/using-the-new-mxnet-model-server.html
https://github.com/dmlc/dmlc-core/tree/master/tracker/yarn
https://news.developer.nvidia.com/nvidias-2017-open-source-deep-learning-frameworks-
contributions
https://unsplash.com/ https://pixabay.com/

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