NVIDIA and Red Hat Team to Accelerate Enterprise AI

NVIDIA’s accelerated computing platform and Red Hat OpenShift 4 help to speed on-premises Kubernetes deployments for AI and data science.
by Justin Boitano

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

At Red Hat Summit today, 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 requires 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 optimized.

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 optimized software stack deployed the right way in large-scale, GPU-accelerated data centers can be frustrating and time consuming for IT organizations. 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 organization to get its GPU-powered data center clusters up and running with OpenShift 4 as it is to spin up new cloud resources.

Preview and Early Access Program

At Red Hat Summit, we’re showing in our booth 1039 a preview of how easy it is to set up bare-metal GPU clusters with OpenShift and GPU operators.

Also, you won’t want to miss Red Hat Chief Technology Officer Chris Wright’s keynote on Thursday when NVIDIA Vice President of Compute Software Chris Lamb will join him on stage to demonstrate how our technologies work together and discuss our collaboration in further detail.

Red Hat and NVIDIA are inviting our joint customers in a white-glove early access program. Customers who want to learn more or participate in the early access program can sign up at https://www.openshift.com/accelerated-ai.