Using Raspberry Pi 3B+ with Apache NiFi MiNiFi and Google Coral Accelerator and Pimoroni Inky Phat

Using Raspberry Pi 3B+ with Apache NiFi MiNiFi and Google Coral Accelerator and Pimoroni Inky Phat

First we need to unbox our new goodies. The Inky Phat is an awesome E-Ink display with low power usage that stays displayed after shutdown!

Next I added a new Google Coral Edge TPU ML Accelerator USB Coprocessor to a new Raspberry Pi 3B+. This was so easy to integrate and get up and running.

Let's unbox this beautiful device (but be careful when it runs it can get really hot and there is a warning in the instructions). So I run this on top of an aluminum case and with a big fan on it.

Pimoroni Inky Phat

It is pretty easy to set this up and it provides a robust Python library to write to our E-Ink display. You can see an example screen here.

https://github.com/pimoroni/inky

Pimoroni Inky pHAT ePaper eInk Display in Red

https://shop.pimoroni.com/products/inky-phat

https://github.com/pimoroni/inky

https://pillow.readthedocs.io/en/stable/reference/ImageDraw.html

https://learn.pimoroni.com/tutorial/sandyj/getting-started-with-inky-phat

Install Some Python Libraries and Debian Install for Inky PHAT and Coral

pip3 install font_fredoka_one
pip3 install geocoder
pip3 install fswebcam
sudo apt-get install fe
pip3 install psutil
pip3 install font_hanken_grotesk
pip3 install font_intuitive
wget http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz

These libraries are for the Inky, it needs fonts to write. The last TAR is for the Edge device and is a fast install documented well by Google.

Download Apache NiFi - MiNiFi Java Agent

https://nifi.apache.org/minifi/download.html

Next up, the most important piece. You will need to have JDK 8 installed on your device if you are using the Java agent. You can also use the MiniFi C++ Agent but that may require building it for your OS/Platform. That has some interesting Python running abilities.

Google Coral Documentation - Google Edge TPU

• Google Edge TPU ML accelerator coprocessor
• USB 3.0 Type-C socket
• Supports Debian Linux on host CPU
• ASIC designed by Google that provides high performance ML inferencing for TensorFlow Lite models

• https://coral.withgoogle.com/tutorials/edgetpu-retrain-classification-ondevice/
• https://coral.withgoogle.com/tutorials/edgetpu-api/
• http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api_reference.zip
• https://coral.withgoogle.com/web-compiler/
• https://coral.withgoogle.com/tutorials/edgetpu-models-intro/
• https://coral.withgoogle.com/tutorials/accelerator/
• https://coral.withgoogle.com/tutorials/edgetpu-api/
• https://coral.withgoogle.com/models/
• https://coral.withgoogle.com/tutorials/accelerator-datasheet/

Using Pretrained Tensorflow Lite Model:

Inception V4 (ImageNet)

Recognizes 1,000 types of objects

Dataset: ImageNet

Input size: 299x299

Let's run a flow:

Results (Once an hour we update our E-Ink Display with Date, IP, Run Time, Label 1)

Source Code

https://github.com/tspannhw/nifi-minifi-coral

References

• https://medium.freecodecamp.org/building-an-iiot-system-using-apache-nifi-mqtt-and-raspberry-pi-ce1d6ed565bc
• https://community.hortonworks.com/articles/85984/using-minifi-to-read-data-from-a-sense-hat-on-a-ra.html
• https://community.hortonworks.com/articles/107379/minifi-for-image-capture-and-ingestion-from-raspbe.html
• https://community.hortonworks.com/articles/107379/minifi-for-image-capture-and-ingestion-from-raspbe.html
• https://community.hortonworks.com/articles/32605/running-nifi-on-raspberry-pi-best-practices.html
• https://www.tensorflow.org/lite/convert/cmdline_examples
• https://www.tensorflow.org/lite/guide/get_started
• https://pillow.readthedocs.io/en/stable/reference/ImageDraw.html
• https://coral.withgoogle.com/tutorials/edgetpu-faq/