Data In Motion: AI

Showing posts with label AI. Show all posts

The Rise of the Mega Edge (FLaNK)

At one point edge devices were cheap, low energy and low powered. They may have some old WiFi and a single core CPU running pretty slow. Now power, memory, GPUs, custom processors and substantial power has come to the edge.

Sitting on my desk is the NVidia Xaver NX which is the massively powerful machine that can easily be used for edge computing while sporting 8GB of fast RAM, a 384 NVIDIA CUDA® cores and 48 Tensor cores GPU, a 6 core 64-bit ARM CPU and is fast. This edge device would make a great workstation and is now something that can be affordably deployed in trucks, plants, sensors and other Edge and IoT applications.

https://www.datainmotion.dev/2020/06/unboxing-most-amazing-edge-ai-device.html

Next that titan device is the inexpensive hobby device, the Raspberry Pi 4 that now sports 8 GB of LPDDR4 RAM, 4 core 64-bit ARM CPU and is speedy! It can also be augmented with a Google Coral TPU or Intel Movidius 2 Neural Compute Stick.

https://dzone.com/articles/efm-series-using-minifi-agents-on-raspberry-pi-4-w

https://www.datainmotion.dev/2020/02/edgeai-google-coral-with-coral.html

These boxes come with fast networking, bluetooth and the modern hardware running in small edge devices that can now deployed en masse. Enabling edge computing, fast data capture, smart processing and integration with servers and cloud services. By adding Apache NiFi's subproject MiNiFi C++ and Java agents we can easily integrate these powerful devices into a Streaming Data Pipeline. We can now build very powerful flows from edge to cloud with Apache NiFi, Apache Flink, Apache Kafka (FLaNK) and Apache NiFi - MiNiFi. I can run AI, Deep Learning, Machine Learning including Apache MXNet, DJL, H2O, TensorFlow, Apache OpenNLP and more at any and all parts of my data pipeline. I can push models to my edge device that now has a powerful GPU/TPU and adequate CPU, networking and RAM to do more than simple classification. The NVIDIA Jetson Xavier NX will run multiple real-time inference streams at 60 fps on multiple cameras.

I can run live SQL against these events at every segment of the data pipeline and combine with machine learning, alert checks and flow programming. It's now easy to build and deploy applications from edge to cloud.

I'll be posting some examples in my next article showing some simple examples.

By next year, 12 or 16 GB of RAM may be a common edge device RAM, perhaps 2 CPUs with 8 cores, multiple GPUs and large fast SSD storage. My edge swarm may be running much of my computing power as my flows running elastically on public and private cloud scale up and down based on demand in real-time.

Unboxing the Most Amazing Edge AI Device Part 1 of 3 - NVIDIA Jetson Xavier NX

Unboxing the Most Amazing Edge AI Device

Fast, Intuitive, Powerful and Easy.

Part 1 of 3

NVIDIA Jetson Xavier NX

This is the first of a series on articles on using the Jetson Xavier NX Developer kit for EdgeAI applications. This will include running various TensorFlow, Pytorch, MXNet and other frameworks. I will also show how to use this amazing device with Apache projects including the FLaNK Stack of Apache Flink, Apache Kafka, Apache NiFi, Apache MXNet and Apache NiFi - MiNiFi.

These are not words that one would usually use to define AI, Deep Learning, IoT or Edge Devices. They are now. There is a new tool for making what was incredibly slow and difficult to something that you can easily get your hands on and develop with. Supporting running multiple models simultaneously in containers with fast frame rates is not something I thought you could affordably run in robots and IoT devices. Now it is and this will drive some amazingly smart robots, drones, self-driving machines and applications that are not yet in prototypes.

Out of the box, this machine is sleek, light weight and ready to go. And now with built-in fast WiFi, yet another great upgrade! I added a 256GB SSD Hard drive and it took seconds and a few quick Linux commands. It's running Ubuntu 18.04 LTS which supports all the deep learning and python libraries you need and runs well. It has a powerful fan already attached and judging by the fast spinning when I was running benchmarks it probably needs it.

It was super easy to get working, just plugged in a USB mouse and keyboard and HDMI monitor.

I ran the benchmarks and was massively impressed with the FPS that can be processed. This machine has some serious power. Basically, this device you are going to locate at the edge in a robot, drone, car or other edge point could be your desktop machine.

I ran a few graphics demos and tests to validate everything once my keyboard, mouse and HDMI monitor were connected. The abilities are awesome. I can see why NVIDIA GPUs are amazing for gaming.

The specifications for the edge device are very impressive. The 8GB of RAM makes this feel like a powerful desktop and not a low powered edge device.

I ran the benchmarks and they were smoking fast. I can see using this as a workstation as the FPS were nice as you can see below.

In part 2, I am going to show how to run some edge AI workloads at tremendous speed and stream the results and images to your cloud or big data environments using Apache open source frameworks including Apache Flink, Apache NiFi - MiNiFi and Apache Kafka.

In part 3, We will push the processing capabilities and amp up the workloads and test all the impressive features of this new killer edge device.

There is so many great tutorials and learning materials available for the NVIDIA Xavier NX. I have found that all my work for Jetson Nano has been working here, only faster. So this is great, I'll have a few interesting demos and run throughs and a video in the follow up articles.

I added a standard USB hub and a Logitech C270 USB Web Camera which worked perfectly. I will use that in the follow up articles and some edge applications.

Tutorials and Guides

References:

sudo /usr/sbin/nvpmodel -q

I highly recommend all AI, Deep Learning, IoT, IIoT, Edge and streaming developers obtain one or more of these developer kits.

This is a powerful machine in a small box. From edge applications to robotics to smart devices to anything that needs powerful processing at the edge, this is your device. A fast CPU, fast GPU and all the interfaces you need. This should be part of any project. Joining my NVIDIA Jetson Nano you now have some great affordable options for Edge AI applications. It is amazing to test drive the performance of this device. I will also be showing this at my online meetups, so join me or watch the video on Youtube later.

===

Jetson Xavier NX Developer Kit features:

Power: 10W (Max efficiency) | 15W (Max performance)

NVIDIA Volta architecture with 384 NVIDIA CUDA® cores and 48 Tensor cores

6-core NVIDIA Carmel ARM®v8.2 64-bit CPU 6 MB L2 + 4 MB L3

2x NVDLA Engines

8 GB 128-bit LPDDR4x @ 51.2GB/s

2x 4K @ 30 | 6x 1080p @ 60 | 14x 1080p @ 30 (H.265/H.264)

Gigabit Ethernet, M.2 Key E (WiFi/BT included), M.2 Key M (NVMe)

HDMI

4x USB 3.1, USB 2.0 Micro-B

2x 4K @ 60 ! If you lower the resolution, it scales up the numbers.

The Jetson Xavier NX Developer Kit is now available for $399 US at NVIDIA.com and from channel partners worldwide. I would recommend acquiring some ASAP before current supplies wane and you may have to wait.

ODPI's OpenDS4All - Open Source Data Science Content To Teach the World

OpenDS4All

Start learning now:

https://github.com/odpi/OpenDS4All/tree/master/opends4all-resources/opends4all-data-wrangling-and-integration

ODPI has officially announced this recently and it looks great.

https://www.odpi.org/news/2020/02/28/odpi-announces-the-opends4all-project

There is a ton of amazing materials including slides, notes, documentation, homework, exercises and Jupyter notebooks covering Data Wrangling, Data Science, the Basics and Apache Spark.

This“starter set” of training materials can help you build a Data Science program for yourself, your company, your university or your non-profit. I am going to bring some of these to my meetups and hopefully can help give back with new materials, updates and suggestions.

These are college level materials developed by the University of Pennsylvania and open source via the ODPI with IBM leading. The code and slides look great. I can see these helping to enable the world adding another million desperately needed Data Scientists, Data Engineers and Data Science Enabled professionals.

I have been running some of this via Cloudera Machine Learning in my CDP cluster in AWS and it works great. This is really well made. I am hoping to create a module on Streaming Data Science to contribute.

https://www.meetup.com/futureofdata-princeton/

EdgeAI: Google Coral with Coral Environmental Sensors and TPU With NiFi and MiNiFi (Updated EFM)

EdgeAI: Google Coral with Coral Environmental Sensors and TPU With NiFi and MiNiFi

Building MiNiFi IoT Apps with the new Cloudera EFM

It is very easy to build a drag and drop EdgeAI application with EFM and then push to all your MiNiFi agents.

Cloudera Edge Management CEM-1.1.1
Download the newest CEM today!

https://www.cloudera.com/downloads/cdf/cem.html

https://docs.cloudera.com/cem/1.1.1/release-notes/topics/cem-whats-new.html

NiFi Flow Receiving From MiNiFi Java Agent

In a cluster in my CDP-DC Cluster I consume Kafka messages sent from my remote NiFi gateway to publish alerts to Kafka and push records to Apache HBase and Apache Kudu. We filter our data with Streaming SQL.

We can use SQL to route, create aggregates like averages, chose a subset of fields and limit data returned. Using the power of Apache Calcite, Streaming SQL in NiFi is a game changer against Record Data Types including CSV, XML, Avro, Parquet, JSON and Grokable text. Read and write different formats and convert when your SQL is done. Or just to SELECT * FROM FLOWFILE to get everything.

We can see this flow from Atlas as we trace the data lineage and provenance from Kafka topic.

We can search Atlas for Kafka Topics.

From coral Kafka topic to NiFi to Kudu.

Details on Coral Kafka Topic

Examining the Hive Metastore Data on the Coral Kudu Table

NiFi Flow Details in Atlas

Details on Alerts Topic

Statistics from Atlas

See: https://www.datainmotion.dev/2020/02/connecting-apache-nifi-to-apache-atlas.html

Example Web Camera Image

Example JSON Record

[{"cputemp":59,"id":"20200221190718_2632409e-f635-48e7-9f32-aa1333f3b8f9","temperature":"39.44","memory":91.1,"score_1":"0.29","starttime":"02/21/2020 14:07:13","label_1":"hair spray","tempf":"102.34","diskusage":"50373.5 MB","message":"Success","ambient_light":"329.92","host":"coralenv","cpu":34.1,"macaddress":"b8:27:eb:99:64:6b","pressure":"102.76","score_2":"0.14","ip":"127.0.1.1","te":"5.10","systemtime":"02/21/2020 14:07:18","label_2":"syringe","humidity":"10.21"}]

Querying Kudu results in Hue

Pushing Alerts to Slack from NiFi

I am running on Apache NiFi 1.11.1 and wanted to point out a new feature. Download flow: Will download the highlighted flow/pgroup as JSON.

Looking at NiFi counters to monitor progress:

We can see how easy it is to ingest IoT sensor data and run AI algorithms on Coral TPUs.

Shell (coralrun.sh)

#!/bin/bash

DATE=$(date +"%Y-%m-%d_%H%M%S")

fswebcam -q -r 1280x720 /opt/demo/images/$DATE.jpg

python3 -W ignore /opt/demo/test.py --image /opt/demo/images/$DATE.jpg 2>/dev/null

Kudu Table DDL

https://github.com/tspannhw/table-ddl

Python 3 (test.py)

import time

import sys

import subprocess

import os

import base64

import uuid

import datetime

import traceback

import base64

import json

from time import gmtime, strftime

import math

import random, string

import time

import psutil

import uuid

from getmac import get_mac_address

from coral.enviro.board import EnviroBoard

from luma.core.render import canvas

from PIL import Image, ImageDraw, ImageFont

import os

import argparse

from edgetpu.classification.engine import ClassificationEngine

# Importing socket library

import socket

start = time.time()

starttf = datetime.datetime.now().strftime('%m/%d/%Y %H:%M:%S')

def ReadLabelFile(file_path):

with open(file_path, 'r') as f:

lines = f.readlines()

ret = {}

for line in lines:

pair = line.strip().split(maxsplit=1)

ret[int(pair[0])] = pair[1].strip()

return ret

# Google Example Code

def update_display(display, msg):

with canvas(display) as draw:

draw.text((0, 0), msg, fill='white')

def getCPUtemperature():

res = os.popen('vcgencmd measure_temp').readline()

return(res.replace("temp=","").replace("'C\n",""))

# Get MAC address of a local interfaces

def psutil_iface(iface):

# type: (str) -> Optional[str]

import psutil

nics = psutil.net_if_addrs()

if iface in nics:

nic = nics[iface]

for i in nic:

if i.family == psutil.AF_LINK:

return i.address

# /opt/demo/examples-camera/all_models

row = { }

try:

#i = 1

#while i == 1:

parser = argparse.ArgumentParser()

parser.add_argument('--image', help='File path of the image to be recognized.', required=True)

args = parser.parse_args()

# Prepare labels.

labels = ReadLabelFile('/opt/demo/examples-camera/all_models/imagenet_labels.txt')

# Initialize engine.

engine = ClassificationEngine('/opt/demo/examples-camera/all_models/inception_v4_299_quant_edgetpu.tflite')

# Run inference.

img = Image.open(args.image)

scores = {}

kCount = 1

# Iterate Inference Results

for result in engine.ClassifyWithImage(img, top_k=5):

scores['label_' + str(kCount)] = labels[result[0]]

scores['score_' + str(kCount)] = "{:.2f}".format(result[1])

kCount = kCount + 1

enviro = EnviroBoard()

host_name = socket.gethostname()

host_ip = socket.gethostbyname(host_name)

cpuTemp=int(float(getCPUtemperature()))

uuid2 = '{0}_{1}'.format(strftime("%Y%m%d%H%M%S",gmtime()),uuid.uuid4())

usage = psutil.disk_usage("/")

end = time.time()

row.update(scores)

row['host'] = os.uname()[1]

row['ip'] = host_ip

row['macaddress'] = psutil_iface('wlan0')

row['cputemp'] = round(cpuTemp,2)

row['te'] = "{0:.2f}".format((end-start))

row['starttime'] = starttf

row['systemtime'] = datetime.datetime.now().strftime('%m/%d/%Y %H:%M:%S')

row['cpu'] = psutil.cpu_percent(interval=1)

row['diskusage'] = "{:.1f} MB".format(float(usage.free) / 1024 / 1024)

row['memory'] = psutil.virtual_memory().percent

row['id'] = str(uuid2)

row['message'] = "Success"

row['temperature'] = '{0:.2f}'.format(enviro.temperature)

row['humidity'] = '{0:.2f}'.format(enviro.humidity)

row['tempf'] = '{0:.2f}'.format((enviro.temperature * 1.8) + 32)

row['ambient_light'] = '{0}'.format(enviro.ambient_light)

row['pressure'] = '{0:.2f}'.format(enviro.pressure)

msg = 'Temp: {0}'.format(row['temperature'])

msg += 'IP: {0}'.format(row['ip'])

update_display(enviro.display, msg)

# i = 2

except:

row['message'] = "Error"

print(json.dumps(row))

Source Code:

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

Sensors / Devices / Hardware:

Humdity-HDC2010 humidity sensor
Light-OPT3002 ambient light sensor
Barometric-BMP280 barometric pressure sensor
PS3 Eye Camera and Microphone USB
Raspberry Pi 3B+
Google Coral Environmental Sensor Board
Google Coral USB Accelerator TPU

References:

Easy Deep Learning in Apache NiFi with DJL

Custom Processor for Deep Learning

Happy Mm.. FLaNK Day!

I have been experimenting with the awesome new Apache 2.0 licensed Java Deep Learning Library, DJL. In NiFi I was trying to figure out a quick use case and demo. So I use my Web Camera processor to grab a still shot from my Powerbook webcam and send it to the processor. The results are sent to slack.

Since it's the holidays I think of my favorite holiday movies: The Matrix and Blade Runner. So I thought a Voight-Kampf test would be fun. Since I don't have a Deep Learning QA piece built yet, let's start by seeing if you look human. We'll call them 'person'. I am testing to see if I am a replicant. Sometimes hard to tell. Let's see if DJL thinks I am human.

See: http://nautil.us/blog/-the-science-behind-blade-runners-voight_kampff-test

Okay, so it least it thinks I am a person. The classification of a Christmas tree is vaguely accurate.

It did not identify my giant french bread.

Building A New Custom Processor for Deep Learning

The hardest part of was a good NiFi Integration test. The DJL team provide some great examples and it's really easy to plug into their models.

ZooModel<BufferedImage, DetectedObjects> model =
MxModelZoo.SSD.loadModel(criteria, new ProgressBar())
Predictor<BufferedImage, DetectedObjects> predictor = model.newPredictor()
DetectedObjects detection = predictor.predict(img);

All the source is in github and references the below DJL sites and repos.

Using a New Custom Processor as part of a Real-time Holiday Flow

We first add the DeepLearningProcessor to our canvas.

An example flow:

GetWebCameraProcessor: grab an image from an attached webcamera
UpdateAttribute: Add media type for image
DeepLearningProcessor: Run our DJL deep learning model from a zoo
PutSlack: Put DJL results in a text window in slack
PostSlack: Send our DJL altered image to slack
Funnel: Send all failures to Valhalla

If we example the provenance we can see how long it took to run and some other interesting attributes.

We place the results of our image analysis in attributes while we return a new image that has a bounding box on the found object(s).

We now a full classification workflow for real-time deep learning analysis on images, could be used for Santa watching, Security, Memes and other important business purposes.

The initial release is available here: https://github.com/tspannhw/nifi-djl-processor/releases/tag/v1.0
Using library and example code from the Deep Java Library (https://djl.ai/).