Apache MXNet for IoT with Apache NiFi. Using Apache MXNet with Apache NiFi and MiniFi for IoT use cases. Ingesting, managing, orchestration and running IoT workloads.

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© 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.

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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

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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

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HDF Data-In-Motion Platform – with HDF 3.1 GA Release

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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"

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

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Java Agent C++ Agent
Multi Cloud HDP Clusters
HBase Druid
YARN
HDFS
Hive
Weather
Location
Sensors

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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

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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

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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

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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

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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

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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

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Run everywhere
Orchestration
Queueing
Simple Event Processing
Deep Learning Framework
Enabling Record Processing
Schema Management
Open Source Components
Edge Ingest
Data Buffering
Device Integration

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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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