1) The document discusses using Apache MXNet for industrial IoT applications. MiniFi ingests camera images and sensor data at the edge and runs Apache MXNet to recognize objects in images. The data is then stored in Hadoop. 2) It describes using Apache MXNet on edge devices like the Raspberry Pi and Nvidia Jetson TX1 to perform tasks like image recognition from cameras and sensors. 3) The document provides information on setting up Apache MXNet on various IoT devices and edge servers to enable machine learning and deep learning capabilities for industrial IoT applications.

1. 1 © Hortonworks Inc. 2011–2018. All rights reserved.
© Hortonworks, Inc. 2011-2018. All rights reserved. | Hortonworks confidential and proprietary information.
Apache MXNet For IIoT
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.
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

4. 4 © Hortonworks Inc. 2011–2018. All rights reserved.
Aggregate all data from sensors, drones, logs, geo-location devices, images,
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, image recognition and object detection with Apache MXNet.
Curate: Gain Insights

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IIoT
Multiple devices, protocols, frameworks, languages, data types, sensors and networks
Protocols
• MQTT
• HTTPS / SSL (REST/JSON)
• OPC UA
• CoAP
• AMQP
• JSON
• XML
• CSV
• Raw Text
• Images (JPEG, PNG)
• Raw Data Streams
Data Types Sensors
• Cameras
• Temperature/Humidity
• IR
• Proximity
• Motion Sensors
• GPS
Protocols
• NVidia Jetson TX1
• Raspberry Pi
• Arduino
• TS-7800 V2
• ESP8266
• DragonBoard 410c
• BeagleBone Black

6. 6 © Hortonworks Inc. 2011–2018. All rights reserved.
HDF Data-In-Motion Platform – with HDF 3.1 GA Release

7. 7 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MiNiFi
• NiFi lives in the data center. Give it an
enterprise server or a cluster of them.
• MiNiFi lives as close to where data is born
and is a guest on that device or system
“Let me get the key parts of NiFi close to where data begins and provide
bidirectional data transfer"

8. 8 © Hortonworks Inc. 2011–2018. All rights reserved.
Edge Intelligence with Apache MiNiFi
 Guaranteed delivery
 Data buffering
‒ Backpressure
‒ Pressure release
 Prioritized queuing
 Flow specific QoS
‒ Latency vs. throughput
‒ Loss tolerance
 Data provenance
 Recovery / recording a rolling log
of fine-grained history
 Designed for extension
Different from Apache NiFi
 Design and Deploy
 Warm re-deploys
Key Features

9. 9 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
Java Agent C++ Agent
Multi Cloud HDP Clusters
HBase Druid
YARN
HDFS
Hive
Weather
Location
Sensors

10. 10 © Hortonworks Inc. 2011 – 2017. 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

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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

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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

13. 13 © Hortonworks Inc. 2011 – 2017. All Rights Reserved
Apache MXNet Raspberry Pi Setup
pip install --upgrade pip
pip install scikit-image
git clone https://github.com/tspannhw/mxnet_rpi.git
sudo apt-get update -y
sudo apt-get install python-pip python-opencv python-scipy python-picamera -y
sudo apt-get -y install git cmake build-essential g++-4.8 c++-4.8 liblapack* libblas* libopencv*
git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet --branch 1.1.0
cd incubator-mxnet
export USE_OPENCV = 0
make
pip install mxnet==1.1.0
https://mxnet.incubator.apache.org/tutorials/embedded/wine_detector.html

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Apache MXNet Raspberry Pi Setup
curl --header 'Host: data.mxnet.io' --header 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11;
rv:45.0) Gecko/20100101 Firefox/45.0' --header 'Accept:
text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-
US,en;q=0.5' --header 'Referer: http://data.mxnet.io/models/imagenet/' --header 'Connection: keep-
alive' 'http://data.mxnet.io/models/imagenet/inception-bn.tar.gz' -o 'inception-bn.tar.gz' -L
tar -xvzf inception-bn.tar.gz
cp Inception-BN-0126.params Inception-BN-0000.params

15. 15 © Hortonworks Inc. 2011–2018. All rights reserved.
Apache MXNet Running on IoT Devices
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

16. 16 © Hortonworks Inc. 2011–2018. All rights reserved.
GPU Powered Nodes with NVidia Jetson TX1 Running Apache MXNet
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-running-apac.html

17. 17 © Hortonworks Inc. 2011–2018. All rights reserved.
Using Apache MXNet on Devices with Sensors and Intel Movidius
https://community.hortonworks.com/articles/176932/apache-deep-learning-101-using-apache-mxnet-on-
the.html

18. 18 © Hortonworks Inc. 2011–2018. All rights reserved.
IIoT Processing
Data Acquisition
Edge Processing
Real Time Stream Analytics
Rapid Application Development
IoT
ANALYTICS
CLOUD
Acquire Move
Routing
&
Filtering
Deliver Parse Analysis
Aggregation
Modeling

19. 19 © Hortonworks Inc. 2011–2018. All rights reserved.
Run everywhere
Orchestration
Queueing
Simple Event Processing
Deep Learning Framework
Enabling Record Processing
Schema Management
Open Source Components
Edge Ingest
Data Buffering
Device Integration

20. 20 © Hortonworks Inc. 2011–2018. All rights reserved.
Questions?

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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
@PaasDev

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