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Showing posts with the label AI

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-seri

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 machin

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 anoth

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, Parq

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