Thanks @WayneWWW for the reply. That documentation is very helpful.
I have some (possibly dumb) followup thoughts and questions. I’m not a low-level Linux expert, and the OTA process in the documentation sounds a bit daunting and high risk. I don’t totally understand everything that is getting upgraded in the process, so please excuse any ignorance.
One simple question: Is there any way to do a less invasive upgrade with the only goal of being able to use newer CUDA / cuDNN (and thereby TensoRT)? That is our chief goal in performing OTA updates.
We utilize TensorRT and CUDA, and our app runs inside a docker container. Our base L4T OS (with tons of stuff uninstalled) + CUDA / cuDNN / TensorRT + our container produces about a 13GB compressed
system.img file and uses about 9GB of disk on a running TX2.
If I understand correctly, the OTA process does not require you to download a full filesystem image to perform the update and you can instead only update the relevant portions (namely the bootloader / core parts of the OS / etc) of the filesystem and retain files in userspace.
However, since there is a direct dependency chain from the GPU driver → CUDA → TensorRT → our app, is there any way we can we update the GPU driver / CUDA without simultaneously requiring a new docker image built against the newer TensorRT / CUDA?
I’m not sure if you have any recommended strategies for navigating these waters, but any insights would be appreciated.