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Newer versions of Ansible don't work with RHEL 8

Red Hat Enterprise Linux 8 is supported until 2029, and that distribution includes Python 3.6 for system python. Ansible's long been stuck between a rock and a hard place supporting certain modules (especially packaging modules like dnf/yum on RHEL and its derivatives, because the Python bindings for the packaging modules are stuck supporting system Python.

Users are getting errors like:

/bin/sh: /usr/bin/python3: No such file or directory
The module failed to execute correctly, you probably need to set the interpreter.\nSee stdout/stderr for the exact error.

...or...

SyntaxError: future feature annotations is not defined

As ansible-core evolves, they don't want to support old insecure versions of Python forever—Python 3.6 was out of security support back in 2021!.

55 TOPS Raspberry Pi AI PC - 4 TPUs, 2 NPUs

I'm in full-on procrastination mode with Open Sauce coming up in 10 days and a project I haven't started on for it, so I decided to try building the stable AI PC with all the AI accelerator chips I own:

  • Hailo-8 (26 TOPS)
  • Hailo-8L (13 TOPS)
  • 2x Coral Dual Edge TPU (8+8 = 16 TOPS)
  • 2x Coral Edge TPU (4+4 = 8 TOPS)

After my first faltering attempt in my testing of Raspberry Pi's new AI Kit, I decided to try building it again, but with a more 'proper' PCIe setup, with external 12V power to the PCIe devices, courtesy of an uPCIty Lite PCIe HAT for the Pi 5.

Raspberry Pi 55 TOPS AI Board

I'm... not sure it's that much less janky, but at least I had one board with a bunch of M.2 cards instead of many precariously stacked on top of each other!

Testing Raspberry Pi's AI Kit - 13 TOPS for $70

Raspberry Pi today launched the AI Kit, a $70 addon which straps a Hailo-8L on top of a Raspberry Pi 5, using the recently-launched M.2 HAT (the Hailo-8L is of the M.2 M-key variety, and comes preinstalled).

Raspberry Pi AI Kit

The Hailo-8L's claim to fame is 3-4 TOPS/W efficiency, which, along with the Pi's 3-4W idle power consumption, puts it alongside Nvidia's edge devices like the Jetson Orin in terms of TOPS/$ and TOPS/W for price and efficiency.

Google's Coral TPU has been a popular choice for a machine learning/AI accelerator for the Pi for years now, but Google seems to have left the project on life support, after the Coral hardware was scalped for a couple years about as badly as the Raspberry Pi itself!

Saying a lot while saying nothing at all about Ansible AWX

A few days ago, the post Upcoming Changes to the AWX Project came across my feed. An innocuous title, but sometimes community-impacting changes are buried in posts like this. So, as an interested Ansible user, I read through the post.

In 1,610 words, almost nothing of substance was written.

A lot about how it's not 2014 anymore, so 2014-era architecture doesn't suit AWX. Then a big bold disclaimer at the bottom:

Before we conclude, we should be clear about what will not happen.

  • We are not changing the Ansible project
  • We are not adjusting our OSS license structure

Ultimately, we need to make some changes to the way our systems work and our projects are structured. Not a rewrite but a refactoring and restructuring of how some of the core components connect and communicate with each other.

Can the Raspberry Pi 5 handle 4K?

Apple TV and Raspberry Pi 5 connected to LG OLED TV

In the past, I've booted LibreELEC on the Raspberry Pi Compute Module 4 in my "This is not a TV" Sharp NEC display.

According to LibreELEC's Pi 5 blog post, the new BCM2712 SoC decodes 4K and 1080p content just fine in H.264, and supports HEVC 4K60 hardware decoding.

And they've tested AV1, VC1, and VP9 at 1080p with no issue, though 4K in non-native formats does encounter frame dropping.

I wanted to put the Pi through some testing of my own, now that the Pi 5's been out for months, and LibreELEC version 12 is stable.

Testing object detection (yolo, mobilenet, etc.) with picamera2 on Pi 5

Besides the Pi 5 being approximately 2.5x faster for general compute, the addition of other blocks of the Arm architecture in the Pi 5's upgrade to A76 cores promises to speed up other tasks, too.

Jeff Geerling person object detection on Pi 5

On the Pi 4, popular image processing models for object detection, pose detection, etc. would top out at 2-5 fps using the built-in CPU. Accessories like the Google Coral TPU speed things up considerably (and are eminently useful in builds like my Frigate NVR), but a Coral adds on $60 to the cost of your Pi project.

With the Pi 5, if I can double or triple inference speed—even at the expense of maxing out CPU usage—it could be worth it for some things.