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The steps would basically be: - Containerize the DL platform - Create a k8s manifest (similar to our CRD if necessary) - Create a service endpoint - integra
by TheIronYuppie 9y ago
The steps would basically be:
- Containerize the DL platform
- Create a k8s manifest (similar to our CRD if necessary)
- Create a service endpoint
- integrate all that into the JH deployment
This is less hard than it sounds, but we'd love help! We only started with TF because that's what we know.
Disclosure: I work at Google on Kubeflow
- minerals 9y agoInteresting, though I don't see how it is better than a plain docker image over kubernetes? Not much of a hassle now too. And how is it different from what DL4J is already doing with Zeppelin and supporting both Keras, TF, MXNet and PyTorch on the way?
- bonesss 9y ago> ...how [is it] better than a plain docker image over kubernetes? Scalability for people with existing on-premise (or cloud based), kubernetes workflows, especially once it comes to training or heavy crunching. That's not to say that Docker Machine/Swarm/Compose couldn't handle the same, but it's an extra step for kubernetes users and pushes people onto a slightly different toolchain than minikube->K8s.
- TheIronYuppie 9y agoCorrect! Many folks have more complicated deployments in the cloud, and we're trying to align (as close as humanly possible) your on-prem stack with your cloud stack, to minimize the pain in migration. If you have a single container, and a simple pipeline, this may be a bit more than you need. We've just found that there are normally 5 or more services/systems that people wire together to create an ML stack, and that's what we're trying to solve for/simplify. Disclosure: I work at Google on Kubeflow