The thread stated that this problem would be fixed in a future driver release. Is there now a better way to make compute mode rules permanent? (I guess I could put “nvidia-smi -g 0 -c 1” in one of our startup files. Just wondering if nvidia-smi has a standard way to do it :unsure: )
I am using a GeForce 480 card on x86_64 Red Hat Enterprise Linux Client release 5.4 (Tikanga)
Nvidia driver version 256.40
The Cuda toolkit I downloaded was cudatoolkit_3.1_linux_64_rhel5.4.run
The compute configuration isn’t persistent across reboots, so set your desired machine state in a startup file. The nvidia-smi daemon mode trick solves a different problem. The NVIDIA linux driver automatically unloads driver libraries and releases state resources after a period of inactivity when no client (be that the X server, a user space application, or nvidia-smi itself) is connected to the driver. The unloading causes a loss of driver state including compute mode settings.
On our cluster, all GPU nodes run a start up script which sets up the necessary device entries in dev, starts nvidia-smi in daemon mode and the calls nvidia-smi to configure each gpus compute state. I don’t believe there is yet a way to avoid it, but it is a trivial solution which we find “just works”.
The compute configuration isn’t persistent across reboots, so set your desired machine state in a startup file. The nvidia-smi daemon mode trick solves a different problem. The NVIDIA linux driver automatically unloads driver libraries and releases state resources after a period of inactivity when no client (be that the X server, a user space application, or nvidia-smi itself) is connected to the driver. The unloading causes a loss of driver state including compute mode settings.
On our cluster, all GPU nodes run a start up script which sets up the necessary device entries in dev, starts nvidia-smi in daemon mode and the calls nvidia-smi to configure each gpus compute state. I don’t believe there is yet a way to avoid it, but it is a trivial solution which we find “just works”.