MiniKF 20220112.0.0

January 14, 2022

NEW! Now with Kubeflow 1.4!

Create a PyTorch distributed job from inside your notebook using Kale and Kubeflow. Distributed training made easier than ever.

Create an AutoML workflow with the click of a button. Start from a dataset, define a task, then discover, train, and optimize a model from inside your notebook automagically.

New MiniKF release including:

  • Kubeflow 1.4
  • Rok 1.4-rc8-11-g47325593f

New Features

  • Upgrade Rok to the latest stable version.
  • Upgrade Kubeflow to version 1.4.
  • Upgrade Kubernetes to version 1.19.15.
  • Upgrade Minikube to version 1.23.2.
  • Upgrade Linux Kernel to version 5.4.151.
  • Updated Kubeflow UIs for an enhanced data science experience.
  • Brand new MiniKF dashboard.
  • Ability to expose Kubernetes metadata, resources, and spec in the Kale SDK. Users are now able to set limits, requests, labels, annotations, or use the nodeSelector using the Kale SDK.
  • Allow setting environment variables in Kale step using the Kale SDK.
  • Allow users to configure the size of the Kale marshal volume.
  • Allow users to define a container and entrypoint for a pipeline step, built using Kale.
  • Integrate Kale with the PyTorch distributed training operator to enable users to run distributed PyTorch training jobs.
  • Extend Kale to support running conditionals with the outputs of the pipeline steps.
  • Enable users to make predictions using an existing KF Serving Inference Service via the Kale API.
  • Support a default automatic snapshot policy for notebooks so data scientists don’t accidentally lose work.
  • Ability to view notebook servers across all namespaces in the Notebooks UI.
  • Monitor the last activity of the notebook servers.
  • Configurable way to stop idle notebook servers automatically.
  • Enable users to mount an existing volume to a notebook server.
  • Automatic log gathering process.
  • Ability to present a notebook programmatically.
  • Fully automated process for snapshotting all notebooks in a Kubeflow cluster and publishing them to Rok Registry.
  • Fully automated process for restoring all notebooks of a Rok bucket.
  • Support for ReadWriteMany (RWX) volumes.
  • Protect critical Rok data from EBS failure or user error.
  • Support Istio authorization to EKF resources based on groups inherited from the identity provider.
  • Introduce an option to disable the auto-profile creation so that users don’t have their own namespace, but only be members of a shared namespace.
  • Enable admins to apply a skeleton of Kubernetes resources for every user namespace in an automated manner.
  • Extend the official K8s autoscaler to support scale-in when using local volumes.

You can find more information about bugfixes and improvements here:

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