Over at the NVIDIA blog, Pramod Ramarao writes that NVIDIA and Red Hat have worked closely to improve the user experience when installing and updating NVIDIA software on RHEL, including GPU drivers and CUDA.
NVIDIA and Red Hat are announcing a technical preview of new packages for the GPU drivers on RHEL. The goal behind this is to improve the user experience of installing and upgrading these drivers on RHEL. By providing better integration of the drivers and RHEL on a technical level, the new packages remove the need to have compilers and a full software development toolchain installed on each system running NVIDIA GPUs, and simplify the management experience.
The new NVIDIA driver packages on RHEL provide a better GPU driver installation and management experience to users on RHEL. To get started with the new packages, follow the instructions in the README. The packages currently support only RHEL 7.6 but NVIDIA is working to quickly expand support on RHEL 8.
NVIDIA and Red Hat have also increased the breadth of testing of the drivers on RHEL and are working on new features such as containerized drivers for use in Kubernetes environments such as Red Hat OpenShift and expanded support for platforms such as NVIDIA DGX and POWER based systems.
NVIDIA GPUs are transforming enterprises by accelerating enterprise computing from inference, data science to large scale AI training, to VDI. Red Hat and NVIDIA have been working together for over 10 years to accelerate Red Hat Enterprise Linux (RHEL) workloads on NVIDIA GPU enabled servers – across the datacenter, virtualized environments and the cloud. To serve these diverse enterprise use-cases on RHEL, NVIDIA provides a software stack powered by the CUDA platform (drivers, CUDA-X acceleration libraries, CUDA optimized applications and frameworks). The NVIDIA / Red Hat partnership continues to grow and there are many integration efforts across Red Hat’s and NVIDIA’s product portfolios on projects as diverse as video drivers, heterogeneous memory management (HMM), KVM support for virtual GPUs, and Kubernetes.
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