Google releases Kubeflow

After announcing Kubeflow in the beginning of December, Google has now released the machine learning framework for the Kubernetes orchestrator. The Kubeflow project can be found in a new open source Github repo that contains:

• JupyterHub to create & manage interactive Jupyter notebooks
• A Tensorflow Custom Resource (CRD) that can be configured to use CPUs or GPUs, and adjusted to the size of a cluster with a single setting
• A TF Serving container

Kubeflow requires ksonnet, a framework for writing, sharing, and deploying Kubernetes application manifests.

Google are collaboring with others on Kubwflow, among others with Red Hat, Canonical, Weaveworks, Container Solutions and many others. CoreOS is already seeing the promise of Kubeflow:

“The Kubeflow project was a needed advancement to make it significantly easier to set up and productionize machine learning workloads on Kubernetes, and we anticipate that it will greatly expand the opportunity for even more enterprises to embrace the platform. We look forward to working with the project members in providing tight integration of Kubeflow with Tectonic, the enterprise Kubernetes platform.” -- Reza Shafii, VP of product, CoreOS

If you’d like to try out Kubeflow right now right in your browser, you can find a Kubeflow installation on Katacoda.