For enterprises looking to get their GPU-accelerated AI and data science projects up and running more quickly, life just got easier.

NVIDIA and Red Hat introduced the combination of NVIDIA's GPU-accelerated computing platform and the just-announced Red Hat OpenShift 4 to speed on-premises Kubernetes deployments for AI and data science.

The result: Kubernetes management tasks that used to take an IT administrator the better part of a day can now be completed in under an hour.

More GPU acceleration, less deployment hassle

This collaboration comes at a time when enterprises are relying on AI and data science to turn their vast amounts of data into actionable intelligence.

But meaningful AI and data analytics work require accelerating the full stack of enterprise IT software with GPU computing. Every layer of software — from NVIDIA drivers to container runtimes to application frameworks — needs to be optimised.

Our CUDA parallel computing architecture and CUDA-X acceleration libraries have been embraced by a community of more than 1.2 million developers for accelerating applications across a broad set of domains — from AI to high-performance computing to VDI.

And because NVIDIA's common architecture runs on every computing device imaginable — from a laptop to the data center to the cloud — the investment in GPU-accelerated applications is easy to justify and just makes sense.

Accelerating AI and data science workloads is only the first step, however. Getting the optimised software stack deployed the right way in large-scale, GPU-accelerated data centers can be frustrating and time-consuming for IT organisations. That's where our work with Red Hat comes in.

Red Hat OpenShift is the leading enterprise-grade Kubernetes platform in the industry. Advancements in OpenShift 4 make it easier than ever to deploy Kubernetes across a cluster. Red Hat's investment in Kubernetes Operators, in particular, reduces administrative complexity by automating many routine data center management and application lifecycle management tasks.

NVIDIA has been working on its own GPU operator to automate a lot of the work IT managers previously did through shell scripts, such as installing device drivers, ensuring the proper GPU container runtimes are present on all nodes in the data center, as well as monitoring GPUs.

Thanks to our work with Red Hat, once the cluster is set up, you simply run the GPU operator to add the necessary dependencies to the worker nodes in the cluster. It's just that easy. This can make it as simple for an organisation to get its GPU-powered data center clusters up and running with OpenShift 4 as it is to spin up new cloud resources.