![]() For purposes of demonstration, we can use the experimental NFS provisioner included in our catalog to provide persistent storage. One of the prerequisites for JupyterHub is persistent storage, so you’ll want to think about how to provide that in this cluster. At the time of publication, the stable release of Kubernetes is 1.16. Installing JupyterHub Using Rancherįirst, let’s assume we have a modern Kubernetes deployment in our Rancher environment. In this article, we’ll show how to do just that by installing JupyterHub using Rancher. Kubernetes makes migrating that workload between physical infrastructure more feasible. Often in AI/ML workloads, an engineer or researcher needs to allocate more resources. Applying computing resources to a workload is easy because of its declarative (as opposed to imperative) design and its discovery-based approach to addressing servers. Kubernetes is great at allowing us to harness the power of a large distributed computing environment. Users can work in their workspaces on shared resources, which can be managed efficiently by system administrators. Essentially, JupyterHub gives users access to computational environments and resources without burdening them with installation and maintenance tasks. ![]() It provides engineers, scientists, researchers and students the computational power of a cloud or data center while still being as easy to use as a local development environment. JupyterHub is a multi-user data exploration tool that is often key for data science and machine learning research and development. ![]() AI and ML present a perfect use case for Kubernetes, the distributed computing platform engineered at Google to run their massive workloads. They require best-in-class distributed computing environments to thrive. ![]() By their nature, AI and ML are computation hungry workloads. AI and Machine Learning are becoming critical differentiators in the technology landscape. ![]()
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