For the past couple of weeks, I have been investigating some issues reported by the community when using ESXi with the popular Google Coral Edge TPU for accelerating machine learning (ML) inferencing. Fortunately, with the help from one of our engineers, Songtao, we were able to find a solution! You can find the complete write-up HERE and it also works with the latest ESXi 8.0 Update 1 release.
I was actually surprised at how popular the combination of the Google TPU and ESXi was from the community, which I guess should not come as a surprise, especially with the capabilities of ESXi coupled with all the interests in AI/ML these days.
Another popular use case of the Google TPU, which I had recently learned about is for real-time AI object detection using the Frigate NVR (Network Video Recorder) software, which is a commonly deployed solution that enable various home security and automation capabilities.
In fact, during a conversation with my buddy Alan Renouf, who is a Product Manager focused on running modern Edge workloads and is also a Frigate user, I discovered that the Frigate stack, which encompasses inferencing, video decoding, and the integration of cameras and sensors, closely resembles the components that you would find in many Edge deployments with simliar set of use cases.
Funny enough, I ended up leveraging a lot of my existing work with running ESXi on Intel NUCs and iGPU passthrough, while learning about and setting up Frigate! This was definitely an interesting project to explore and as shared, I now have a complete working setup with the full setup and write-up below.
JFYI - I have already submitted a PR 6576 to update the Frigate ESXi documentation as it is severely out of date and help folks quickly find the latest setup instructions.
Earlier this week I had no idea what Frigate NVR was ...
Today, full setup w/ESXi on Intel NUC (this thing is amazing, SO many use cases) 🥳
✅ Passthrough Google Coral USB TPU (inferencing)
✅ Passthrough Intel iGPU (vid
decoding)
✅ RTSP enabled camera #AlwaysBeLearning pic.twitter.com/Qghj7qwOFp— William Lam (@lamw.bsky.social | @*protected email*) (@lamw) May 18, 2023