Any chance to make this a helm chart?
Kubeflow – Machine Learning Toolkit for Kubernetes
21–30 of 35 posts
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#22Earlier quoted context omitted.
Its very close to how we think about ML internally, but not what we use. Your best bet to read that is look at the TFX paper[1] which describes our internal thoughts in great detail. (Though Kubeflow is not designed to be an externalization of TFX, we're very much working in collaboration with that team) [1] http://www.kdd.org/kdd2017/papers/view/tfx-a-tensorflow-base... Disclosure: I work at Google on Kubeflow
Does google intend on open sourcing TFX ?. I only ask because we're building a lot of the same infrastructure.
We're absolutely looking at it! Please join our discussion, we'd love to talk about what you're building and if we can help and/or what you'd like us to OSS.
Disclosure: I work at Google on Kubeflow
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#23Any chance to make this a helm chart?
We're looking at a bunch of deployment packaging solutions, helm is probably one of the top ones!
Disclosure: I work at Google on Kubeflow
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#24Earlier quoted context omitted.
Thank you! We still love Google Cloud ML engine - it's perfect for those who want to run in the cloud and want a layer of abstraction. This is for people who want portable stacks and a bit more control; and/or want to use their Kubernetes deployments (particularly on-premise or multi-purpose). Does that help? Disclosure: I work at Googlr on Kubeflow
Do you foresee some kind of integration between Google Cloud ML engine and Tensorflow on k8s in the future?
We're always ready to talk roadmap - anything in particular you'd like to see integration-wise?
Disclosure: I work at Google on Kubeflow
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#25Earlier quoted context omitted.
Does google intend on open sourcing TFX ?. I only ask because we're building a lot of the same infrastructure.
We're absolutely looking at it! Please join our discussion, we'd love to talk about what you're building and if we can help and/or what you'd like us to OSS. Disclosure: I work at Google on Kubeflow
Sure, is there an issue/doc/pr to comment on ?.
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#26Hi! I’m David Aronchick, PM on Kubeflow, I’m happy to answer any questions! I was one of the early PMs on Kubernetes, and we very much want to make this a community project, so please join us in thinking about what’s next! - GH: https://GitHub.com/Google/Kubeflow - kubeflow-Discuss: https://groups.google.com/forum/m/#!forum/kubeflow-discuss NOTE: The name "flow" does not refer directly to TensorFlow; if anything it's…
Are you at kubecon this week?
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#27Hi! I’m David Aronchick, PM on Kubeflow, I’m happy to answer any questions! I was one of the early PMs on Kubernetes, and we very much want to make this a community project, so please join us in thinking about what’s next! - GH: https://GitHub.com/Google/Kubeflow - kubeflow-Discuss: https://groups.google.com/forum/m/#!forum/kubeflow-discuss NOTE: The name "flow" does not refer directly to TensorFlow; if anything it's…
Are you at kubecon this week?
I am! You can reach me at aronchick (at) Google if you'd like to meet or have questions.
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#28Any chance to make this a helm chart?
Chime in at https://github.com/google/kubeflow/issues/23 :)
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#29Earlier quoted context omitted.
Great to hear this not tied to TensorFlow! How would one use a different DL platform, say PyTorch or DyNet?
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
Interesting, 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?
Re: Kubeflow – Machine Learning Toolkit for Kubernetes
#30Earlier quoted context omitted.
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
Interesting, 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?
> ...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.