ray-core

To run Python functions in parallel across multiple machines, you can use Ray to distribute tasks over multiple CPUs and GPUs. This approach enables parallel execution by managing tasks and shared object stores across a cluster.

The best way to maintain stateful workers for distributed machine learning is by using Ray actors. Actors allow you to maintain stateful workers like model servers or parameter servers throughout the distributed computing lifecycle.

Yes, you can share large model weights between parallel tasks using Ray's shared object store. The shared object store allows you to share large data like embeddings or model weights between tasks efficiently.

To allocate specific GPUs on the same node for distributed computing, you can create a placement group using Ray. This allows you to schedule tasks and actors requiring specific hardware resources like 2 GPUs on a single machine.

Yes, Ray supports parallel data processing and large-scale simulations. It is designed for building distributed systems and services, allowing you to parallelize CPU or GPU-bound Python work across cores or machines.