Scaling

The platform operator provisions the Kubernetes cluster with the Jupyter Contents, Environments and Pools.

Based on the user traffic, the operator has to size the Kubernetes Nodepools and Jupyterpools size to balance availability and costs.

The size of the Nodepools and Jupyterpools can be updated based on the expected traffic. This is how you achieve scalability.

The API services can be scaled up to serve in parallel more traffic in needed, ensuring parrallel scalability.

Read the available benchmarks to get more information on the various up- and down- scaling cases.

Scaling Up

Scaling-up is achieved by giving more nodes to the related Nodepools, e.g. jupyter-cpu-medium, jupyter-cuda-medium... .

Scaling-down is achieved by giving less nodes to the related Nodepools, e.g. jupyter-cpu-medium, jupyter-cuda-medium...

Benchmarks are being worked out and will be published on this page as soon as available.

The benchmarks focus on the balance between Kernel availability and costs.