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I assume this is similar to Ray?
by valzam 11mo ago
I assume this is similar to Ray?
- lairv 11mo agoI'm also curious what's the use case of this over Ray. Tighter integration with PyTorch/tensors abstractions?
- porridgeraisin 11mo agoThat. Also, it has RDMA. Last I checked, Ray did not support RDMA. There are probably other differences as well, but the lack of RDMA immediately splits the world into things you can do with ray and things you cannot do with ray
- zacmps 11mo agoNot currently, but it is being worked on https://github.com/ray-project/ray/issues/53976 https://github.com/ray-project/ray/issues/53976.
- disattention 11mo agoI had the same thought, especially because of their recent collaboration. https://pytorch.org/blog/pytorch-foundation-welcomes-ray-to-deliver-a-unified-open-source-ai-compute-stack/ https://pytorch.org/blog/pytorch-foundation-welcomes-ray-to-...
- unnah 11mo agoThere's also Dask, which can do distributed pandas and numpy operations etc. However it was originally developed for traditional HPC systems and has only limited support for GPU computing. https://www.dask.org/ https://www.dask.org/
- cwp 11mo agoThe code example is very similar to Ray. Monarch: class Example(Actor): @endpoint def say_hello(self, txt): return f"hello {txt}" procs = this_host().spawn_procs({"gpus": 8}) actors = procs.spawn("actors", Example) hello_future = actors.say_hello.call("world") hello_future.get() Ray: @ray.remote(num_gpus=1) class Example: def say_hello(self, txt): return f"hello {txt}" actors = [Example.remote() for _ in range(8)] hello_object_refs = [a.say_hello.remote("world") for a in actors] ray.get(hello_object_refs)