kubeflow
Support MLX on Kubernetes with Kubeflow
MLX is a new ML framework specifically designed to run on Apple silicon: https://github.com/ml-explore/mlx
It has some differences compare to PyTorch with mps backend: https://github.com/ml-explore/mlx/issues/12#issuecomment-1843956313
It would be nice to integrate MLX in Kubeflow ecosystem for distributed capabilities, and provide a way to run MLX models on Kubernetes.
For example, we can leverage Kubeflow Training Operator for MLX Model Training and Fine-Tuning, and Kubeflow Katib for HyperParameter optimization. Since Kind cluster supports ARM arch, we should explore if we can use M-series GPUs for MLX model training with Kind in the future.
In addition to that, I saw examples how folks run Kubernetes on multi-VMs with MacOS machines and kubeadm.
That might be useful when a single machine can't handle very large ML model.
cc @kubeflow/wg-training-leads @awni
In addition to that, I saw examples how folks run Kubernetes on multi-VMs with MacOS machines and kubeadm. That might be useful when a single machine can't handle very large ML model.
Interesting. Does MLX support multi-node training?
Not yet. We are working on it. Probably makes sense to follow up on this once we have some basic support there.
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Once we implemented the Kubeflow Training V2 APIs we can make MLX work with MPI using mpirun command 🎉
It looks like distributed communication with MLX uses MPI: https://ml-explore.github.io/mlx/build/html/usage/distributed.html#getting-started
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.