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Jupyterlab kubernetes
Jupyterlab kubernetes









jupyterlab kubernetes

It is considered a best practice for different developers to work in different namespaces. In fact, the lack of ability to clone a volume to a different namespace is often a showstopper. When they make this migration, it is absolutely necessary for them to be able to take their clone-based processes with them so that they don’t lose all of the lifecycle efficiencies that they have achieved over the years. For many years, development teams, including NetApp’s own internal development team, have been using NetApp FlexClone ® technology to reduce build-test processes from multiple days to just a few hours or even minutes.ĭevelopment teams and data science teams are increasingly interested in moving their build-test and model training environments to Kubernetes in order to drive efficiency and avoid vendor lock-in. Accelerating development with FlexCloneįor developers and data scientists who work with large repositories, built artifacts, and datasets, the ability to clone workspace volumes almost instantaneously can greatly accelerate the development lifecycle. This is a major limitation when the volume that you want to clone contains something like a developer or data scientist workspace. For example, because of the architecture of Kubernetes itself, you can’t clone a volume to a different namespace by using a Kubernetes CSI driver.

#JUPYTERLAB KUBERNETES DRIVERS#

However, there are things that CSI drivers can’t do.

jupyterlab kubernetes

In fact, we were a pioneer in this area with the NetApp® Astra™ Trident provisioner, the initial version of which was released way back in 2016. They bring persistent storage into the cloud-native world by greatly simplifying provisioning, snapshots, and clones. Kubernetes Container Storage Interface drivers are great.











Jupyterlab kubernetes