Realistically, you have to run either the whole Jupyter or each notebook kernel in a container (Docker, LXC,...). Then you can set limits on the container(s), and/or restrict the number of simultaneously running kernels through the Jupyter kernel manager.
Without containers, a user can consume basically unlimited memory and CPU even from a single Python notebook kernel. -- You received this message because you are subscribed to the Google Groups "Project Jupyter" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/jupyter/917a5916-485a-46c7-bd81-264f0405e229%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
