[Tossrock]

Unprofitable for ETH mining maybe, but it seems like a natural fit to rent time on it to deep learning people with slow training models. Although that could still be unprofitable after the cost of electricity, I guess it's a question of market size/demand. A lot of deep learning is already at big infrastructure players anyway who wouldn't need the service, leaving academics / smaller companies. But maybe some people would find a reliable, scalable GPU cluster valuable.

[samnco]

Ahah, good point. Really the ETH stuff was "because I can". But in the same charts repository you will find a Tensorflow chart. My previous series of blogs [0] was about exactly that. A nice addition as well for compute intensive workloads is the use of LXD [1]

Another use case is in media for transcoding. It is not a trivial job to orchestrate transcoding at scale, and Kubernetes with or without GPUs is an excellent solution for that as it is trivial to setup a completely automated job queue.

Also another interesting field will eventually be HPC but there are some constraints about compute that K8s does not tick scheduling wise at this point in time. There is a pluggable scheduler in the works I think, and this will eventually help. Also the LXD example is a nice optimization but it would not replace the scheduler in any way.

[0]: https://medium.com/intuitionmachine/gpus-kubernetes-for-deep...

[1]: https://hackernoon.com/job-concurrency-in-kubernetes-lxd-and...