Author: David Finster
Last Updated: Tue, Oct 18, 2022There's no need to install any drivers on a Vultr Cloud GPU. Licensed NVIDIA drivers and the CUDA Toolkit are preinstalled by cloud-init when you deploy our standard images.
Please check these troubleshooting FAQs before opening a support ticket if you have driver or license problems with a Cloud GPU server.
See if the drivers loaded:
$ sudo lsmod | grep nvidia2modprobe nvidia
The two most common reasons they may not load are:
The installer did not run
A kernel update ran after the driver installation finished.
To find out if the installer ran during deployment, test if this file exists:
$ sudo ls /opt/nvidia/drivers/linux_nvidia_client.run
If the file does not exist, please open a support ticket.
If /opt/nvidia/drivers/linux_nvidia_client.run exists and the drivers aren't loaded, then a kernel update is possibly blocking the drivers. Try re-running the installer to resolve the conflict. After this, the NVIDIA drivers should correctly recognize the new kernel and load.
$ sudo bash /opt/nvidia/drivers/linux_nvidia_client.run --ui=none --no-questions
The Cloud GPU will not run if the license file is missing or corrupt. Normally, cloud-init uses Vultr's vendor-data to install the license file during deployment. If your Cloud GPU isn't working properly, please verify the license file exists:
$ sudo ls /etc/nvidia/gridd.conf
If the file is missing, or you believe it is corrupt, please open a support ticket.
If you want to use an operating system that isn't offered by Vultr, please install cloud-init in your custom OS. It will automatically install the drivers and libraries using Vultr's vendor-data.
PyTorch supports several different installation methods, which you'll find documented on their website. We recommend using Anaconda to install PyTorch in most cases. To get started with PyTorch, you can deploy our ready-to-run Anaconda and Miniconda Marketplace apps.
Vultr Talon Cloud GPUs are compatible with CUDO version 11.3 or later.