Overview I have been running a similar program on Raspberry Pi devices with TensorFlow. Now that MXNet has entered Apache incubation, it has become incredibly interesting to me. With the backing of Apache and Amazon, this library cannot be ignored. So I tried in on the same Raspberry Pi 3B that I was using for TensorFlow. For this example, we are grabbing images from the standard Raspberry Pi Camera and running live image analysis on it with MXNet using the Inception pre-built model from the MXNet Model Zoo. This is the nearly the same as the TensorFlow example. What I noticed is a bit faster execution and smoother process. For accuracy, I have not run enough tests for weighing the two libraries out, but that is something I will look at doing for large number of images. Training both with my camera and images I am interested in would be very helpful. Some use cases I am thinking of are: Security Camera, Water Leak Detection, Evil Cat Sensing, Engine Vibration and self-driving model car. Raspberry Pi v3 B with PI Camera Setup Your Device For Running MXNet sudo apt-get -y install git cmake build-essential g++-4.8 c++-4.8 liblapack* libblas* libopencv* git clone https://github.com/dmlc/mxnet.git --recursive cd mxnet make cd python sudo python setup.py install 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 mv Inception_BN-0126.params Inception_BN-0000.params The primary code is Python taken from some examples from MXNet, OpenCV and PICamera. topn = inception_predict.predict_from_local_file(filename, N=5) This calls the inception_predict from MXNet example. The inception_predict code is referenced in the reference links below. Main Python Code #!/usr/bin/python # 2017 load pictures and analyze import time import sys import datetime import subprocess import sys import urllib2 import os import datetime import ftplib import traceback import math import random, string import base64 import json import paho.mqtt.client as mqtt import picamera from time import sleep from time import gmtime, strftime import inception_predict packet_size=3000 def randomword(length): return ''.join(random.choice(string.lowercase) for i in range(length)) # Create camera interface camera = picamera.PiCamera() while True: # Create unique image name uniqueid = 'mxnet_uuid_{0}_{1}'.format(randomword(3),strftime("%Y%m%d%H%M%S",gmtime())) # Take the jpg image from camera filename = '/home/pi/cap.jpg' # Capture image from RPI camera.capture(filename) # Run inception prediction on image topn = inception_predict.predict_from_local_file(filename, N=5) # CPU Temp p = subprocess.Popen(['/opt/vc/bin/vcgencmd','measure_temp'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = p.communicate() # MQTT client = mqtt.Client() client.username_pw_set("username","password") client.connect("mqttcloudprovider", 14162, 60) # CPU Temp out = out.replace('\n','') out = out.replace('temp=','') # 5 MXNET Analysis top1 = str(topn[0][1]) top1pct = str(round(topn[0][0],3) * 100) top2 = str(topn[1][1]) top2pct = str(round(topn[1][0],3) * 100) top3 = str(topn[2][1]) top3pct = str(round(topn[2][0],3) * 100) top4 = str(topn[3][1]) top4pct = str(round(topn[3][0],3) * 100) top5 = str(topn[4][1]) top5pct = str(round(topn[4][0],3) * 100) row = [ { 'uuid': uniqueid, 'top1pct': top1pct, 'top1': top1, 'top2pct': top2pct, 'top2': top2,'top3pct': top3pct, 'top3': top3,'top4pct': top4pct,'top4': top4, 'top5pct': top5pct,'top5': top5, 'cputemp': out} ] json_string = json.dumps(row) client.publish("mxnet",payload=json_string,qos=1,retain=False) client.disconnect() We grab an image from a camera, run it through MXNet, convert the results to JSON and then send the message to a cloud hosted MQTT broker. I also grab the CPU temperature to show we can add more sensors. Example JSON Sent via MQTT [{"top1pct": "54.5", "top5": "n04590129 window shade", "top4": "n03452741 grand piano, grand", "top3": "n03018349 china cabinet, china closet", "top2": "n03201208 dining table, board", "top1": "n04099969 rocking chair, rocker", "top2pct": "9.1", "top3pct": "8.0", "uuid": "mxnet_uuid_oqy_20170211203727", "top4pct": "2.8", "top5pct": "2.2", "cputemp": "75.2'C"}] Our schema is pretty consistent as above, so we can create a Hive or Phoenix table and insert into that. HDF / NiFi Flow Consume MQTT This processor will receive messages from a cloud based MQTT broker sent by a few Raspberry PIs I have setup. Extract Fields from MXNET (EvaluateJSONPath) Build a Message (UpdateAttribute) Category 1 ${top1} at ${top1pct}% Category 2 ${top2} at ${top2pct}% Category 3 ${top3} at ${top3pct}% Category 4 ${top4} at ${top4pct}% Category 5 ${top5} at ${top5pct}% UUID ${uuid} CPU Temp ${cputemp} Send Msg to Slack Channel (PutSlack) Channel is mxnet Stores Files (PutFile) We take the JSON convert it to a text message to a Slack channel. That's all it takes to ingest data from an edge device running a camera and running Deep Learning on a tiny device and then send the data asynchronously to a cloud hosted broker that can distribute to cloud and on-premise hosted Apache NiFi servers. We could also use Site-to-Site, HTTP or TCP/IP. MQTT is very lightweight, works over the Internet, has an easy Python library and works well with Apache NiFi. Reference: This sample program is critical and gave me most of the code needed to run: http://mxnet.io/tutorials/embedded/wine_detector.html http://data.mxnet.io/models/imagenet/ https://community.hortonworks.com/content/repo/77987/rpi-picamera-mqtt-nifi.html https://github.com/tspannhw/mxnet_rpi/blob/master/analyze.py https://community.hortonworks.com/content/kbentry/80339/iot-capturing-photos-and-analyzing-the-image-with.html CloudMQTT has proven to be awesome. Instant setup and a free instance for testing. This is great for getting data from my remote raspberry pis to the cloud and back into HDF 2.1 servers behind firewalls. http://cloudmqtt.com http://www.jsonpath.com/ Github Repo https://github.com/tspannhw/mxnet_rpi https://community.hortonworks.com/repos/83001/python-mxnet-raspberry-pi-example.html?shortDescriptionMaxLength=140 Pushing to Slack Channel https://nifi-se.slack.com/messages/mxnet/details/ Apache MXNet Incubation https://wiki.apache.org/incubator/MXNetProposal Awesome MXNet https://github.com/dmlc/mxnet/tree/master/example Install MXNet on Raspian http://mxnet.io/get_started/raspbian_setup.html Example Program for MXNet on Raspberry PI 3 http://mxnet.io/tutorials/embedded/wine_detector.html Raspberry Pi with MXNET http://mxnet.io/tutorials/embedded/wine_detector.html MQTT https://github.com/tspannhw/rpi-picamera-mqtt-nifi/blob/master/upload.py Real-Image with Pretrained Model http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html MXNet GTC Tutorial https://github.com/dmlc/mxnet-gtc-tutorial MXNet for Facial Identification https://github.com/tornadomeet/mxnet-face http://vis-www.cs.umass.edu/fddb/results.html http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html MXNet Models for ImageNet 1K Inception BN https://github.com/dmlc/mxnet-model-gallery/blob/master/imagenet-1k-inception-bn.md MXNet Example Image Classification https://github.com/dmlc/mxnet/tree/master/example/image-classification sudo apt-get install imagemagick identify -verbose /home/pi/cap.jpg ... 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Sentiment CoreNLP Processor [pool-1-thread-1] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator tokenize[pool-1-thread-1] INFO edu.stanford.nlp.pipeline.TokenizerAnnotator - No tokenizer type provided. Defaulting to PTBTokenizer.[pool-1-thread-1] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator ssplit[pool-1-thread-1] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator parse[pool-1-thread-1] INFO edu.stanford.nlp.parser.common.ParserGrammar - Loading parser from serialized file edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz ... done [0.4 sec].[pool-1-thread-1] INFO edu.stanford.nlp.pipeline.StanfordCoreNLP - Adding annotator sentimentFILE:Header,Header2,Header3Value,Value2,Value3Value4,Value5,Value6Attribute: {"names":"NEGATIVE"} Service Source Code JUnit Test for Processor To Add Sentiment Analysis to Your NiFi Data Flow, just add the custom processor, CoreNLPProcessor. You can downloada pre-built NAR from the github listed below. Add to your NiFi/lib directory and restart each node. The results of the run will be an attribute named sentiment: You can see how easy it is to add to your dataflows. If you would like to add more features to this processor, please fork the github below. This is not an official NiFi processor, just one I wrote in a couple of hours for my own use and for testing. There are four easy ways to add Sentiment Analysis to your Big Data pipelines: executescript of Python NLP scripts, call my custom processor, make a REST call to a Stanford CoreNLP sentiment server, make a REST call to a public sentiment as a service and send a message via Kafka (or JMS) to Spark or Storm to run other JVM sentiment analysis tools. Download a release https://github.com/tspannhw/nifi-corenlp-processor/releases/tag/v1.0 sentimentanalysiscustomprocessor.xml http://stanfordnlp.github.io/CoreNLP https://github.com/tspannhw/neural-sentiment https://github.com/tspannhw/nlp-utilities https://community.hortonworks.com/content/kbentry/81222/adding-stanford-corenlp-to-big-data-pipelines-apac.html https://community.hortonworks.com/content/repo/81187/nifi-corenlp-processor-example-processor-for-doing.html https://community.hortonworks.com/repos/79537/various-utilities-and-examples-for-working-with-va.html https://community.hortonworks.com/articles/76935/using-sentiment-analysis-and-nlp-tools-with-hdp-25.html https://community.hortonworks.com/questions/20791/sentiment-analysis-with-hdp.html https://community.hortonworks.com/articles/30213/us-presidential-election-tweet-analysis-using-hdfn.html https://community.hortonworks.com/articles/52415/processing-social-media-feeds-in-stream-with-apach.html https://community.hortonworks.com/articles/81222/adding-stanford-corenlp-to-big-data-pipelines-apac.html https://community.hortonworks.com/content/kbentry/67983/apache-hive-with-apache-hivemall.html ... View more