Skip to main content

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:

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.




Popular posts from this blog

Migrating Apache Flume Flows to Apache NiFi: Kafka Source to HDFS / Kudu / File / Hive

Migrating Apache Flume Flows to Apache NiFi: Kafka Source to HDFS / Kudu / File / HiveArticle 7 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_9.html Article 6 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_35.html
Article 5 - 
Article 4 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_8.html Article 3 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_7.html Article 2 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache.html Article 1https://www.datainmotion.dev/2019/08/migrating-apache-flume-flows-to-apache.html Source Code:  https://github.com/tspannhw/flume-to-nifi
This is one possible simple, fast replacement for "Flafka".



Consume / Publish Kafka And Store to Files, HDFS, Hive 3.1, Kudu

Consume Kafka Flow 

 Merge Records And Store As AVRO or ORC
Consume Kafka, Update Records via Machine Learning Models In CDSW And Store to Kudu

Sour…

Exploring Apache NiFi 1.10: Stateless Engine and Parameters

Exploring Apache NiFi 1.10:   Stateless Engine and Parameters Apache NiFi is now available in 1.10!
https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12316020&version=12344993

You can now use JDK 8 or JDK 11!   I am running in JDK 11, seems a bit faster.

A huge feature is the addition of Parameters!   And you can use these to pass parameters to Apache NiFi Stateless!

A few lesser Processors have been moved from the main download, see here for migration hints:
https://cwiki.apache.org/confluence/display/NIFI/Migration+Guidance

Release Notes:   https://cwiki.apache.org/confluence/display/NIFI/Release+Notes#ReleaseNotes-Version1.10.0

Example Source Code:https://github.com/tspannhw/stateless-examples

More New Features:

ParquetReader/Writer (See:  https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_7.html)Prometheus Reporting Task.   Expect more Prometheus stuff coming.Experimental Encrypted content repository.   People asked me for this one before.Par…

Ingesting Drone Data From DJII Ryze Tello Drones Part 1 - Setup and Practice

Ingesting Drone Data From DJII Ryze Tello Drones Part 1 - Setup and Practice In Part 1, we will setup our drone, our communication environment, capture the data and do initial analysis. We will eventually grab live video stream for object detection, real-time flight control and real-time data ingest of photos, videos and sensor readings. We will have Apache NiFi react to live situations facing the drone and have it issue flight commands via UDP. In this initial section, we will control the drone with Python which can be triggered by NiFi. Apache NiFi will ingest log data that is stored as CSV files on a NiFi node connected to the drone's WiFi. This will eventually move to a dedicated embedded device running MiniFi. This is a small personal drone with less than 13 minutes of flight time per battery. This is not a commercial drone, but gives you an idea of the what you can do with drones. Drone Live Communications for Sensor Readings and Drone Control You must connect to the drone…