Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.
And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.
Performance, large machines and few users: connection issues
See original GitHub issueI planned to use TLJH for a classroom of about 20 concurrent users. Following http://tljh.jupyter.org/en/latest/howto/admin/resource-estimation.html I estimated 4Gb of RAM would be enough with a good margin so I installed it on a shared OVH server of type s1-4 (4 Go, 1 vCore, 100 Mbit/s).
Whatever the number of concurrent users, launching a new server sometimes fails (maybe once out of 10 times). Retrying usually works. top does not report a high CPU usage and the available swap is not used.
When the 20 users begin to use the jupyterhub, some of them (maybe 5-7 of them), after creating a notebook, have connection issues with the server. As a result, the execution of cells hangs. Restarting the server and logging out and in does not solve the issue.
Because I anticipated there could be performance issues with that configuration, I was ready to deploy a new TLJH on a slightly more powerfull OVH server (b2-7: 7 Go, 2 guaranteed vCore, 250 Mbit/s). But it did not solve the issues previously described. Eventually I had to make them develop locally.
I don’t know if this issue is a bug report of a feature request, but several things could help users in such situations:
- Even with https://tljh.jupyter.org/en/latest/troubleshooting/index.html I am not able to find any hint about the bottleneck (is it a RAM issue, a CPU issue?).
sudo journalctl -u juypyterhubdoes not report anything - Without tool to simulate the load of the whole class prior, so I had to test in live. A way to stress test a hub would be great
Issue Analytics
- State:
- Created 4 years ago
- Comments:6 (1 by maintainers)
Top Results From Across the Web
Top Related Medium Post
Top Related StackOverflow Question
Troubleshoot Live Code
Top Related Reddit Thread
Top Related Hackernoon Post
Top Related Tweet
Top Related Dev.to Post
Top Related Hashnode Post
If I understand that correctly, you have a total of 4GB of RAM on the whole machine? That’s probably too short for 20 concurrent users. (It’s 200MB each. That’s likely below the memory footprint of the jupyter notebook server each user gets to themselves.)
My connection to the server kept dropping out when i was the only person using the hub. digital blue, 4GB RAM. I was doing arithmetic in the cells.
Feels like the setting for timing out is super short. (I’m not a technical person, so if that’s a ridiculous thing to say…apologies).