NVIDIA CV-CUDA: A long-running CV-CUDA Python process consumes resources without bound, ending in denial of service

Impact

A long-running CV-CUDA Python process consumes resources without bound, ending in denial of service and data loss. On a shared inference node this means one tenant's preprocessing job can starve the box.

Who can reach it

Local, low privileges - a user able to submit work through the CV-CUDA Python API.

What to do

Update CV-CUDA per bulletin 5560 and rebuild affected images. Cost: package update and job restart; no driver or firmware change. Consider cgroup memory limits on preprocessing containers as a standing control.

References