OpenStack, the open-source infrastructure project that aims to give enterprises the equivalent of AWS for the private clouds, today announced the launch of its 17th release, dubbed “Queens.” After all of those releases, you’d think that there isn’t all that much new that the OpenStack community could add to the project, but just as the large public clouds keep adding new services, so is OpenStack.

“People want to get more out of their cloud,” OpenStack Foundation COO Mark Collier told me. Those users want to run both their legacy workloads and new workloads on the platform, but what those new workloads look like is changing. “For us, what we are seeing in terms of new workloads is a lot of demand for machine learning. That’s a very hot space and people see value in it very quickly.”

It’s probably no surprise, then, that one of the marquee new features in the Queens release is built-in support for vGPUs, that is, the ability to attach GPUs to virtual machines.

As Collier and OpenStack Executive Director Jonathan Bryce noted, until now, most users would opt for running bare-metal servers with GPUs for this, but that comes with its own administrative overhead for setting up these machines. Now, users can simply boot up a virtual machine with a vGPU and start running their scientific and machine learning workloads.

In addition to support for vGPUs, OpenStack is also adding support for other hardware and software acceleration resources (think FPGAs, CryptoCards, etc.) thanks to the new Cyborg project, which can make these resources available as standalone machines or as part of the…

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