If you encounter slow nvidia-smi before the information is shown. For my 8 x A40 Cards, it took about 26 seconds to initialise. The reason for slow initialization might be due to the driver persistence issue. For more background on the issue, do take a look at Nvidia Driver Persistence. According to the article, The NVIDIA GPU driver has historically followed Unix design philosophies by only initializing software and hardware state when the user has configured the system to do so. Traditionally, this configuration was done via the X Server and the GPUs were only initialized when the X Server ..read more

I was having issues with the Applications like NetKET to detect and enable MPI. Diagnosis I have installed OpenMPI and enabled CUDA during the configuration. CUDA Libraries including nvidia-smi has been installed without issue. But running, nvidia-smi topo –matrix, I am not able to see NVLink similar to In fact, when I run NetKet on CUDA with MPI, the error that was generated was mpirun noticed that process rank 0 with PID 0 on node gpu1 exited on signal 11 (Segmentation fault)." Solution This forum entry provided some enlightenment. https://forums.developer.nvidia.com/t/cuda-initializati ..read more

There is a very good article written by Microway on this utility. Take a look at nvidia-smi: Control Your GPUs What is nvidia-smi? nvidia-smi is a command line utility, based on top of the NVIDIA Management Library (NVML), intended to aid in the management and monitoring of NVIDIA GPU devices. Installation Do take a look at NVIDIA CUDA Installation Guide for Linux for more information Query GPU Status $ nvidia-smi -L Query overall GPU usage with 1-second update intervals $ nvidia-smi dmon Query System/GPU Topology and NVLink $ nvidia-smi topo --matrix $ nvidia-smi nvlink --status Q ..read more

For more information, take a look at The NVIDIA® Data Center GPU Manager (DCGM) . According to the Information, The NVIDIA® Data Center GPU Manager (DCGM) simplifies administration of NVIDIA Datacenter (previously “Tesla”) GPUs in cluster and datacenter environments. At its heart, DCGM is an intelligent, lightweight user space library/agent that performs a variety of functions on each host system: GPU behavior monitoring GPU configuration management GPU policy oversight GPU health and diagnostics GPU accounting and process statistics NVSwitch configuration and monitoring This functional ..read more

Installation Guide You can take a look at Nvidia CUDA Installation Guide for more information Step 1: Get the Nvidia CUDA Repo You can find the Repo from the Nvidia Download Sites. It should be named cuda_rhel8.repo. Copy it and use it as a template with a j2 extension. [cuda-rhel8-x86_64] name=cuda-rhel8-x86_64 baseurl=https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64 enabled=1 gpgcheck=1 gpgkey=https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/D42D0685.pub Step 2: Use Ansible to Generate the repo from Templates. The Ansible Script should look like th ..read more

If you have installed the CUDA Drivers and CUDA SDK using the NVIDIA CUDA Installation Guide for Linux. Look for Section 3.3.3 for RHEL 8 / Rocky 9 If after following instruction, you are still facing issues, you may want to consider the following 1- Blacklist nouveau.conf $ vim /etc/modprobe.d/blacklist-nouveau.conf blacklist nouveau options nouveau modeset=0 2- Remove Nvidia driver installation # dnf module remove --all nvidia-driver 3- Remove CUDA-Related Installation sudo dnf remove "cuda*" "*cublas*" "*cufft*" "*cufile*" "*curand*" \ "*cusolver*" "*cusparse*" "*gds-tools*" "*npp ..read more

Traditional methods for performing data reductions are very costly in terms of latency and CPU cycles. The NVIDIA Quantum InfiniBand switch with NVIDIA SHARP technology addresses complex operations such as data reduction in a simplified, efficient way. By reducing data within the switch network, NVIDIA Quantum switches perform the reduction in a fraction of the time of traditional methods ..read more

I follow the blog Installing Nvidia Drivers on Rocky Linux 8.5. But I encountered an error that I have not encountered before Error: Problem 1: package nvidia-kmod-common-3:515.48.07-1.el8.noarch requires nvidia-kmod = 3:515.48.07, but none of the providers can be installed - cannot install the best candidate for the job - package kmod-nvidia-latest-dkms-3:515.48.07-1.el8.x86_64 is filtered out by modular filtering - nothing provides dkms needed by kmod-nvidia-latest-dkms-3:515.48.07-1.el8.x86_64 - package kmod-nvidia-open-dkms-3:515.48.07-1.el8.x86_64 is filtered out by modular ..read more

In this meetup you’ll learn how synthetic data is transforming AI development efforts: Learn how to use NVIDIA’s Omniverse Replicator to quickly create synthetic data and how it can integrate with NVIDIA TAO training tools. Hear from Sky Engine AI, an NVIDIA synthetic data partner, sharing how you can leverage 3rd party synthetic data services. Get your questions answered in a live Q&A session with our team of experts. Register here and select one of the following sessions: Americas, Europe, Middle East: Wednesday May 18 – 8am PT | 4PM CET  Asia-Pacific: Thursday May 19 – 11am SST ..read more

Nvidia Corporation has announced the EOL Notice #LCR-000906 – MELLANOX PCN INFORMATION: PCN Number: LCR-000906 – MELLANOX PCN Description: EOL notice for Mellanox ConnectX-5 VPI host channel adapters and Switch-IB 2 based EDR InfiniBand Switches Publish Date: Sun May 08 00:00:00 GMT 2022 Type: FYI ..read more