LinkedIn to Present Ray-Based Elastic PyTorch Training Stack
Running RL training workloads on heterogeneous clusters is one of the harder production problems, and a large-scale deployment built on Ray rather than bespoke orchestration signals where the tooling ecosystem is heading. Engineers building distributed training pipelines can compare LinkedIn's approach with their own stack at the conference. The session doubles as a preview of what Ray-PyTorch integration looks like in production.
