Data Works MD December 2019 - https://www.meetup.com/DataWorks/events/265823739/ Video is available at https://www.youtube.com/watch?v=EFHUdKTDRZM Robotics and Machine Learning: Working with NVIDIA Jetson Kits Interested in machine learning and AI? Do you want to learn more about high performance GPU programming and how it applies to Deep Learning? Patty Delafuente will introduce you to the Nvidia Jetson developer kits, discuss their applications, how to get started, and provide a live demonstration of NVIDIA® Jetson Nano™, an easy to use deep learning and robotics platform. Patty Delafuente Patty Delafuente, is a lead Data Scientist in the Advanced Data Analytics Lab at the Social Security Administration. She teaches evening classes for the University of Maryland Baltimore County’s graduate level Data Science Program. Patty is a member of the ‘MS in Analytics Advisory Board’ for Texas A&M University. Patty holds a Master of Science in Analytics from Texas A&M University along with a Bachelor and Masters in Information Systems and holds numerous certifications. In 2017, she was awarded the Texas A&M Margaret Sheather Memorial Award in Analytics for her Capstone Project, “Using Decision Trees to Analyze Patterns in Disability Fraud.” She has over twenty years of database engineering, business intelligence, and analytics experience. Her interests include machine learning, text mining, and using GPUs to improve the performance of analyzing and processing big data. She is a certified Nvidia Instructor in the ‘Fundamentals of Deep Learning for Computer Vision’ and ‘Accelerated Computing with Python’. Patty can be reached LinkedIn at https://www.linkedin.com/in/pattydelafuente319/

1. Working with the Nvidia Jetson Developer Kits
Patty Delafuente

2.  Senior Director Data Science, NewWave Telecom & Technologies
 Adjunct Faculty, Graduate Data Science Program, UMBC
 MS Analytic Advisory Board, Texas A&M University
 Nvidia Certified Instructor and University Ambassador (Computer Vision,
Parallel GPU Computing Python, RAPIDS)
 Contact me at:
 Linked in: https://www.linkedin.com/in/pattydelafuente319/
 Email: Patricia.Delafuente@NewWave.io or pstanton@umbc.edu

3.  Introduction
 Robotics and Machine Learning
 GPUs
 Nvidia
 Jetson Developer Kits

4.  Artificial Intelligence- using machines to emulate human or biological
performance
 Robotics and Machine learning are categories of Artificial Intelligence
 Robot as defined by https://robots.ieee.org/learn/: An autonomous machine capable of
sensing its environment, carrying out computations to make decisions, and performing
actions in the real world.
 Machine learning enables machines to infer an outcome from statistical models trained
on past data. It may encompass methods of diagnostics, predictive and prescriptive
analytics. Deep learning is a form of machine learning that leverages deep neural
networks.

7.  Machine Learning requires labeled data to
train models that can be used for inference
 https://www.cigionline.org/articles/ghost-
workers-underpinning-worlds-artificial-
intelligence-systems?fbclid=IwAR1GBO8v-
X9xLna8BAZZXd5FDHfBQp_P9oMs5NiYA7
7piGKxnqw-ugt13V0

8. DNN GPU BIG DATA

9.  Deep Neural Networks
 Convolutional Neural Network
 https://pytorch.org/docs/stable/tor
chvision/models.html
 Transfer Learning - Andrew Ng
explanation:
https://www.youtube.com/watch?
v=yofjFQddwHE

11.  Can be used to accelerate the machine learning aspects
 Data is typically passed from CPU to GPU
 Many, smaller simpler cores compared to CPU
 GPU Optimized for throughput
 CPU optimized to complete a computer operation as fast as possible

12. 12
CPU
Optimized for
Serial Tasks
GPU Accelerator
Optimized for
Parallel Tasks
CPU Strengths
• Very large main memory
• Very fast clock speeds
• Latency optimized via large caches
• Small number of threads can run
very quickly
CPU Weaknesses
• Relatively low memory bandwidth
• Cache misses very costly
• Low performance/watt

13. 13
CPU
Optimized for
Serial Tasks
GPU Accelerator
Optimized for
Parallel Tasks
GPU Strengths
• High bandwidth main memory
• Significantly more compute
resources
• Latency tolerant via parallelism
• High throughput
• High performance/watt
GPU Weaknesses
• Relatively low memory capacity
• Low per-thread performance
© NVIDIA 2013

14.  History: https://www.nvidia.com/en-us/
 Survivor of the computer graphics chip war 1990s- early 2000
 Enabled the use graphic processing units for complex computations and analytics
 https://elinux.org/Jetson

15.  Speedup = 1
(1- P) + P/ N
P = % of execution time from code that can be enhanced
N = number of CUDA cores
 E.G. if 60% of time executed by code that can be parallelized, then running it a tx2
with 256 cores has a speedup of
1
(1-.60) + .60/256 = 2.5 times speed improvement

16.  Jetsons are small AI computers engineered to provide advanced
machine learning capability in autonomous systems
 GPU enables advanced functionality while reducing power
consumption and costs

18.  Advanced AI and autonomous vehicle
 Built using Nvidia Jetson TX2 Developer and Jet Robot Kit
 Need host Ubuntu machine to setup and program
 Complex Programming Framework: Robot Operating System (ROS), Nvidia GPU
CUDA, Arduino Sketches, Python, C++
 Heavy build requirements- soldering, wiring
 Hardware costs $2k not including host machine

19.  Jet Toolkit https://developer.nvidia.com/downloads/jet-toolkit-zip
 Bill of Materials
https://docs.google.com/spreadsheets/d/1jGn7AG5NivTjxPEppJEdIUVxhm5nB17T
3ZtRTCYyuQg/edit#gid=0
 Jetson TX2 eLinux Reference: https://elinux.org/Jetson_TX2

20.  Architecture and software similar to Jet Robot Kit
 https://www.turtlebot.com/turtlebot2/
 Substitute TX2 for Netbook/PC
 Ubuntu / ROS

21. Jetson
TX1/TK1
Arduino
Mega
H-Bridge
Shield
Left
Motor
Right
Motor
Camera
Accel/Gyro
(GY-521)
USB
USB
I2C
Sonar
Module
Sonar
Module
Sonar
Module
Encoder
readings

22. Jetson
TX1/TK1
Arduino
Mega
H-Bridge
Shield
USB
3S (11.1V)
5000mAh
Battery
Red
Power
Switch
Fus
e

23.  Heavy build requirements
 Not for novices
 Complex software setup and configuration
 Several hours to flash with operating system and CUDA tools
 Chore to ensure versions of OS, ROS, and CUDA are compatible
 Not all bug fixes are documented, lot of trial and error
 “apt” overwrites “libglx.so” during updates to prevent: sudo apt-mark hold xserver-xorg-
core
https://elinux.org/Jetson_TK1#An_important_step_before_connecting_the_Jetson_to_Int
ernet
 Incompatibility between library & executable versions:
https://elinux.org/Jetson_TX2/TX2_Issue

26.  Get started
 https://console.aws.amazon.com/deepracer/home?region=us-east-1#getStarted
 Build, train, and test model in a virtual environment
 Requires cloud account and cloud fees may apply
 Purchase DeepRacer: https://www.amazon.com/dp/B07JMHRKQG

27.  Simple robotic car
 Arduino sketches (C++) uploaded to Arduino card through USB port from
Windows or Ubuntu machine
 Simple to build and program
 Follows explicit functions to travel distances and avoid obstacles
 Limited machine learning
 Cost $50 to $99 not including host machine

28.  https://elinux.org/Jetson_Nano
 Purchase from Nvidia for $99 https://www.nvidia.com/en-us/autonomous-
machines/embedded-systems/jetson-nano/
 Need to purchase Power Supply (5V 2A Micro-USB or 4A DC barrel jack adapter),
MicroSD card (recommend 32 GB minimum), Camera (Raspberry Pi Module v2 or
Logitech CS270), optional 2 pin jumper
 All in one robot kits with or without Nano $110-259: https://www.nvidia.com/en-
us/autonomous-machines/embedded-systems/jetbot-ai-robot-kit/
 Free Nvidia DLI course “Getting Started with AI on Jetson Nano”

30.  Compatible with Raspberry PI and Adafruit modules
 Simpler setup
 Download Nano image from:
 Non-jetbot with camera:
https://developer.download.nvidia.com/training/nano/dlinano_v1-0-0_image_20GB.zip or
 Jetbot image:
https://drive.google.com/open?id=1GF2D814hkViwluZ5SgNKW56cQu_5Ekt5
 Write existing image to MicroSD using Etcher or similar software
 Configured for Jupyter Labs, after wi-fi set connect with web browser
http://<jetbot_ip_address>:8888

31.  Build instructions and starter labs https://github.com/NVIDIA-AI-IOT/jetbot/wiki
 Take a shortcut and by solderless Jetbot Kit with printed 3d panels:
https://www.amazon.com/Solderless-Printed-Controller-Switches-
Polarized/dp/B07TB4KFCZ
 Supports headless connectivity once Wi-Fi is enabled
 Example code to get started https://github.com/NVIDIA-AI-
IOT/jetbot/wiki/Examples

32.  https://elinux.org/Jetson_AGX_Xavier
 $699
 Requires Linux host pc to flash OS

33. Jetson Kits Specifications Projects
Jetson TX2 $399
https://developer.nvidia.com/e
mbedded/jetson-tx2-
developer-kit
256 GPU Core NVIDIA Pascal™
2 Denver 64-bit CPUs + Quad-Core A57
Complex
8 GB L128 bit DDR4 Memory
32 GB eMMC 5.1 Flash Storage
Jet Robot Kit
https://developer.nvidi
a.com/embedded/com
munity/quick-start-
platforms
Nano $99
https://developer.nvidia.com/e
mbedded/jetson-nano-
developer-kit
128 GPU Core Nvidia Maxwell™
Quad Core A57 1.43 GHZ
4 GB 64-bit LPDDR4 25.6 GB/s
MicroSD storage
https://www.nvidia.co
m/en-us/autonomous-
machines/embedded-
systems/jetbot-ai-
robot-kit/
AGX Xavier $699
https://developer.nvidia.com/e
mbedded/jetson-agx-xavier-
developer-kit
384/512-core NVIDIA Volta™ GPU with 48/64
Tensor Cores
8-core ARM v8.2 64-bit CPU, 8MB L2 + 4MB
L3
16GB 256-Bit LPDDR4x | 137GB/s
32GB eMMC 5.1
https://elinux.org/Jetso
n_AGX_Xavier
Xavier NX $399 Preorder
https://developer.nvidia.com/e
mbedded/jetson-xavier-nx
384-core NVIDIA Volta™ GPU with 48 Tensor
Cores
6-core NVIDIA Carmel ARM®v8.2 64-bit
CPU6MB L2 + 4MB L3
8 GB 128-bit LPDDR4x

34.  Code and reference materials available at:
https://drive.google.com/drive/folders/1imlL1Mx6g_JDOlUR-
N3ZbmFOs5044yk8?usp=sharing
 Resources:
 Jetson Developer Forum: https://devtalk.nvidia.com/default/board/139/embedded-
systems/1
 JetsonHacks: https://www.jetsonhacks.com/
 Linux Jetson: https://elinux.org/Jetson
 Nvidia Jetson Github: https://github.com/NVIDIA-AI-IOT/
 Jetbot AI for Makers: https://www.youtube.com/watch?v=zOCSRzDUI-Y