Last night the object detection project, which is using a Raspberry Pi model 3b+ and an Intel / Movidius Neural Compute Stick 2 (NCS2), finally started working as expected. The video feed comes from a Pi cam v2.1. Kudos for Adrian at Pyimagesearch for his very helpful guide on this topic!
After a fresh install of Ubuntu server 20.04 on a Raspberry Pi (3b+ in this case) you may find that the default login (user: “ubuntu” password: “ubuntu”) won’t get you logged in.
After waiting for a few minutes while googling this the Rpi output some cloud-init and SSH-key messages. When trying again the default ubuntu/ubuntu login worked just fine.
So, it turns out that even though the login screen is shown and it looks like the OS has fully booted, it won’t be possible to log in until the cloud-init etc. have finished (which may take 5min or so)!
After having received several inquiries from people about how to get started with EdgeX Foundry I’ve decided to write a hands-on tutorial. Hopefully this will make it easier for newcomers to setup a system, configure data ingestion, data export & many other things.
Incidentally, getting started with EdgeX Foundry is also an excellent way to learn how to practically leverage new concepts and technologies in IT. In addition to learning about open source IoT solutions, the guide also covers topics like:
Docker
Building, running and monitoring containers
Grouping containers with docker-compose
Microservices
REST APIs
MQTT
Python scripting
Tools like Postman, cURL, etc.
The guide started as a blog post but ended up being way too long. Now it’s in PDF format and clocks in at 48 pages. Hopefully not too long for those looking to get started 🙂
After finding an unfortunate lizard which had fallen into our garden sink I 3D printed a staircase to allow any future visitors to escape on their own. This is a modified (resized and broadened) version of the staircase here: https://www.thingiverse.com/thing:3147067
In early 2020 a new feature was added to the PowerEdge 14G servers called “Telemetry Streaming’. This feature makes it possible to send a continuous stream of telemetry containing in-depth information about the state of the server and its various components including, but not limited to, the following:
CPU, Memory and Fans
FPGA and GPU
PCIe slots
Airflow inside server
Power usage information
Since the level and depth of information collected with this method FAR exceeds what has been previously possible using IPMI or other tools, this feature can help in several areas. For example:
Power ML algorithms for Anomaly Detection
Provide detailed inventory, usage and status information