Just to qualify that "math execution" part, the beauty of Ray is that you get threadpool-like features to speed up arbitrary python code. So not just paralleli

Just to qualify that "math execution" part, the beauty of Ray is that you get threadpool-like features to speed up arbitrary python code. So not just parallelism, but state/variable sharing for relatively small data. So this is great for some optimizers and definitely RL (where your "math" is some really complicated simulation / loss logic), but Ray wouldn't make much sense for BLAS stuff. Am I missing something here? Ray shows expertise in multi-machine that's lacking in stuff like Jax, Tensorflow, and PyTorch. Horovod nailed down a lot of the performance issues for SGD in particular, but is missing the sort of rapid deployment / distribution stuff in Ray. If only they could all work together ...