IoT Edge Processing with Deep Learning on HDF 3.2 and HDP 3.0 - Part 4 - Movidius

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IoT Edge Processing with Apache NiFi and MiniFi and Multiple Deep Learning Libraries Series

For: https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68140

See Part 1: https://community.hortonworks.com/articles/215079/iot-edge-processing-with-deep-learning-on-hdf-32-a...

See Part 2: https://community.hortonworks.com/articles/215258/iot-edge-processing-with-deep-learning-on-hdf-32-a...

See Part 3: https://community.hortonworks.com/articles/215271/iot-edge-processing-with-deep-learning-on-hdf-32-a...

You will notice a bit of a travel theme this article, it's because some of the images and work was done while on various holidays in August and September.

Deep Learning

We are running TensorFlow 1.10, Apache MXNet 1.3, NCSDK 2.05 and Neural Compute Application Zoo (NC App Zoo).

Device Type 1: Plain Raspberry Pi

(Found some old Kodak slides...)

Main things to do is upgrade to Python 3.6, upgrade Raspberry PI to Stretch, upgrade libraries and a few reboots.

Install OpenCV (or upgrade) and install Apache MXNet. You want to make sure you are on the latest version of Stretch and everything is cleaned up.

Example:

sudo apt-get upgrade

sudo apt-get install build-essential tk-dev libncurses5-dev libncursesw5-dev libreadline6-dev libdb5.3-dev libgdbm-dev libsqlite3-dev libssl-dev libbz2-dev libexpat1-dev liblzma-dev zlib1g-dev

sudo apt autoremove

pip3.6 install --upgrade pip

pip3.6 install mxnet

git clone https://github.com/apache/incubator-mxnet.git --recursive

Device Type 2: Raspberry Pi Enhanced with Movidius Neural Compute Stick

I have updated the code to work with the new Movidius NCSDK 2.05.

See: https://github.com/tspannhw/StrataNYC2018/blob/master/all2.py

I also updated some variable formatting and added some additional values. Evolve that schema!

So you can see some additional data:

{"uuid": "mxnet_uuid_json_20180911021437.json", "label3": "n04081281 restaurant, eating house, eating place, eatery", "label1": "n03179701 desk", "roll": 4.0, "y": 0.0, "value5": "3.5%", "ipaddress": "192.168.1.156", "top5": "n03637318 lampshade, lamp shade", "label5": "n02788148 bannister, banister, balustrade, balusters, handrail", "host": "sensehatmovidius", "cputemp": 53, "top3pct": "6.5%", "diskfree": "5289.1 MB", "pressure": 1018.6, "cafferuntime": "111.685844ms", "label4": "n04009552 projector", "top4": "n03742115 medicine chest, medicine cabinet", "humidity": 42.5, "cputemp2": 52.62, "value2": "6.1%", "value3": "6.0%", "top2pct": "6.9%", "top1": "n02788148 bannister, banister, balustrade, balusters, handrail", "top4pct": "6.4%", "currenttime": "2018-09-11 02:14:44", "label2": "n03924679 photocopier", "top1pct": "7.3%", "top3": "n04286575 spotlight, spot", "starttime": "2018-09-11 02:14:33", "top5pct": "3.9%", "memory": 35.2, "value4": "5.0%", "top2": "n03250847 drumstick", "runtime": "11", "z": 1.0, "pitch": 360.0, "imagefilename": "/opt/demo/images/2018-09-10_2214.jpg", "tempf": 75.25, "temp": 35.14, "yaw": 86.0, "value1": "8.5%", "x": 0.0}

Apache NiFi and MiniFi Process, Proxy, Access, Filter and Transform Data Anywhere, Anytime, Any Platform

Apache NiFi and minifi Works in Moab Utah

Resources:

• https://github.com/tspannhw/StrataNYC2018
• https://www.geomesa.org/documentation/current/tutorials/geomesa-quickstart-nifi.html
• https://github.com/cinci/rpi-sense-hat-java
• https://movidius.github.io/ncsdk/install.html
• https://movidius.github.io/ncsdk/tf_modelzoo.html
• https://github.com/movidius/ncappzoo/
• https://github.com/movidius/ncappzoo/blob/ncsdk2/tensorflow/facenet/README.md
• https://github.com/movidius/ncappzoo/blob/ncsdk2/tensorflow/inception_v4/README.md
• https://medium.com/tensorflow/tensorflow-1-9-officially-supports-the-raspberry-pi-b91669b0aa0
• https://github.com/lhelontra/tensorflow-on-arm/releases/download/v1.10.0/tensorflow-1.10.0-cp35-none...
• https://github.com/movidius/ncappzoo/blob/ncsdk2/apps/image-classifier/README.md