Release Date: July 24, 2019
Kubewise is a simple multi-platform desktop client for Kubernetes®. In the same way the
kubectl command requires only a valid kubeconfig file to run commands against a Kubernetes cluster, Kubewise requires you just to configure one or more valid kubeconfig files to interact with the corresponding Kubernetes clusters.
- Support for multiple kubeconfig files.
- UI-driven interaction with the most frequently used Kubernetes entities.
- One-click terminal with the proper KUBECONFIG env variable set.
- Generation of custom kubeconfig files for a given namespace.
- Highlight sustaniability and security-related data.
Kubernetes® is a registered trademark of The Linux Foundation in the United States and other countries, and is used pursuant to a license from The Linux Foundation.
Disclaimer: This Fling is not connected in any way with The Linux Foundation nor The Cloud Native Computing Foundation (CNCF).
- Any modern macOS, Windows or Linux (Debian-based) OS.
kubectlv1.14.0+ installed to access Kubernetes v1.14.0+ clusters.
Instructions are easy as 1 2 3:
- Download the proper deliverable for your platform and install it accordingly.
- Run the Kubewise application and select your first kubeconfig file.
[ Features ]
- Terminal command UI - users can now override the default command to open a new terminal window of their choice.
- About Info UI - displays the version of currently installed kubectl
[ Bug fixes ]
- Fixed an issue where Windows users cannot add a kubeconfig file
- Fixed an issue where Linux users cannot list resources due to snap security policies
- Switching to YAML format in the Inspect resource view loaded all resources of the same type
- Surround path params of kubectl commands with double quotes
[ Misc ]
- Save settings file pretty printed
- Allign 'trash' icons in kubeconfig dropdown
- Show loading spinner on application startup
Federated Machine Learning on Kubernetes
FATE is an opensource project hosted by Linux Foundation to provide a federated learning framework. FATE has been used to increase the performance of predictions in credit reporting, insurance and other financial areas, as well as surveillance and visual detection projects. It helps organizations to comply with strict privacy regulations and laws such as GRDP and CCPA.