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I've found that Kubernetes mostly solves this problem. I say mostly because for AI/ML workloads that require GPUs, we still rely on running things on bare metal
by sbrother 6y ago
I've found that Kubernetes mostly solves this problem. I say mostly because for AI/ML workloads that require GPUs, we still rely on running things on bare metal locally, and deploying with GKE's magic annotations and Deep Learning images. But for anything else, I haven't had an issue going all in on k8s at the beginning, even with very small teams.
- erulabs 6y agoYep! My startup is https://kubesail.com https://kubesail.com, so I agree :) As for ML on Kube, I agree, there have been and still are some rough edges. The kernel drivers alone make a lot of out-of-the-box Kubernetes solutions unusable. That said, we've had a lot of success helping people move entirely onto kube - the mental gain alone from ditching the bash scripts or ansible playbooks (etC) alone is pretty freeing.