Ray Launches Special Interest Group for Accelerators
Ray becoming a PyTorch Foundation project shifts accelerator support from a single-vendor effort to a community-governed roadmap, which should reduce fragmentation across GPU and TPU fleets. Teams running distributed training or inference on mixed hardware can follow the SIG on GitHub or Slack to influence scheduling and transport priorities early.
The Ray project, now under the PyTorch Foundation, announced a Special Interest Group (SIG) for Ray on Accelerators. Ray currently supports more than a dozen accelerator types, including NVIDIA and AMD GPUs, TPUs and Trainium. The SIG will unite hardware vendors, cloud providers and the Ray community around a vendor-neutral roadmap. Its focus areas include development and testing infrastructure on real hardware, hardware-aware scheduling that understands topology, and direct device-to-device transport via Ray Direct Transport. Participation is open through the Ray Slack channel, community meetings and GitHub.
Today, PyTorch Foundation project, Ray is announcing the Special Interest Group (SIG) for Ray on Accelerators.
Ray already supports more than a dozen accelerator types, from NVIDIA and AMD GPUs to TPUs and Trainium. The SIG brings hardware vendors, cloud providers, and the Ray community together around a vendor-neutral roadmap focused on:
· Development and testing infrastructure on real hardware · Hardware-aware scheduling that understands topology · Direct device-to-device transport with Ray Direct Transport
Join us on the Ray Slack channel, at community meetings, or on GitHub.
Learn more here: https://t.co/F4tAUbCr7v
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