I made a fresh install of docker desktop from official docs docker desktop. Then I installed NVIDIA Container Toolkit following official docs nvidia container toolkit. When I run docker with non-root permission: docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi It…
tf-nightly version = 2.12.0-dev2023203 Python version = 3.10.6 CUDA drivers version = 525.85.12 CUDA version = 12.0 Cudnn version = 8.5.0 I am using Linux (x86_64, Ubuntu 22.04) I am coding in Visual Studio Code on a venv virtual environment…
NVIDIA starting from CUDA 11.x should in theory guarantee compatibility of their CUDA Toolkit libraries (typically shipped inside docker containers) and the driver library libcuda.so (installed on the host). This should be true at least when we stay within all…
The Karpenter default OS, Amazon Linux 2 (AL2), accepts shell scripts (bash commands). Karpenter supports using custom launch templates In the default configuration, Karpenter uses an EKS optimized version of AL2 (Amazon Linux 2) (https://karpenter.sh/v0.13.2/aws/launch-templates/) and passes the hostname of…
I have followed this GCP guide with Ubuntu 18 and 20 (have also tried Ubuntu Lite, Debian and Centos 7) but, unfortunately, after completing the lengthy install I get this: me@gpu:~$ nvidia-smi NVIDIA-SMI has failed because it couldn't communicate with…
I used to multi-gpu system in tensorflow. however, from someday, the following code used CPU only. tf.debugging.set_log_device_placement(True) strategy = tf.distribute.MirroredStrategy() Moreover, the return of physical device check function is empty tf.config.list_physical_devices('GPU') The return of nvidia-smi correctly show as following picture…
The guides I have for deploying LXC on CentOS is to install snapd's lxd https://www.cyberciti.biz/faq/set-up-use-lxd-on-centos-rhel-8-x/ SnapD is a type of service that allows installing debian/ubuntu based packages with the logic being lxd is most up to date on that platform.…