With great difficulty I have succeeded in getting OpenVINO and the Coral TPU support running on two of the DLI supplied images.
I have two based on the LTS versions.
The first is atomicpi_ubuntu_bionic18.04_lxde_standalone_220.127.116.11 modified to automatically login (suitable for rebooting by a watchdog timer) It has OpenVINO 2020.1 and the Coral libedgetpu-legacy and python3-edgetpu packages installed, node-red, mosquitto, everything to run my Security DVR AI Person Detector add-on project: https://github.com/wb666greene/AI-Person-Detector
It uses about 55% of the eMMC and uses an SD card to store detected images. The major limitation is I could not get sound to work in at all, since part of my project is audio alert messages upon
person detection using espeak-ng speech synthesizer, its missing a potentially important piece. It will run using Modidius NCS, NCS2, ot Coral TPU. I tried installing OpenVINO 2020.3, the last version that supports the original NCS but it had errors building the OpenVINO examples so I downgraded to the 2020.1 version that my project was developed with and the demos built and ran correctly. But without audio and
Condor camera usable in OpenCV, its probably not worth bothering with.
The second is atomicpi_ubuntu_focal20.04_lxqt_standalone_18.104.22.168 again modified to automatically login and ready to run my AI person detector project. Unfortunately OpenVINO 2021.1, the first to support Ubuntu 20.04 and python 3.8 doesn't support the original NCS, so its only NCS2 or TPU. Audio works and the built-in Class-D amplifier works well with espeak-ng and external speaker. It uses about 65% of the eMMC.
Unfortunately OpenVINO 2021.1 also changed the model format so my NCS models don't work, I expect to rebuild them in the new format with the
model optimizer soon. The OpenVINO classification_sample_async and security_barrier_camera_demo built and ran correctly with both CPU and NCS2. Two bad the Atom Z8350 GPU doesn't have what it takes to run the OpenVINO GPU plug-in.
If someone at DLI is interested, contact me and we can make arrangements for me to get the compressed versions of my two images to you. I can rip out my AI code or leave it in, it'll work well with any 8-channel 1080p security DVR or IP
netcams that can export rtsp streams viewable in VLC.
To really meet the AI Developer's Kit requirements you'll have to tell me how to use the
Condor camera in OpenCV. A gstreamer
pipeline that implements an rtsp server might suffice, but none of the gstreamer.sh $HOME/samples/camera examples work for me on either of DLI images.
A garden variety 1080p USB webcam works fine if you want to start with say some PyImageSearch AI tutorials.