When troubleshooting, you may see unexpected behaviors or receive an error message. This section provide links for identifying the cause of the problem and how to resolve it.
Behavior
JupyterHub proxy fails to start
sudospawner fails to run
What is the default behavior when none of the lists (admin, whitelist, group whitelist) are set?
JupyterHub Docker container not accessible at localhost
Errors
500 error after spawning my single-user server
How do I…?
Use a chained SSL certificate
Install JupyterHub without a network connection
I want access to the whole filesystem, but still default users to their home directory
How do I increase the number of pySpark executors on YARN?
How do I use JupyterLab’s prerelease version with JupyterHub?
How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
How do I set up rotating daily logs?
Toree integration with HDFS rack awareness script
Where do I find Docker images and Dockerfiles related to JupyterHub?
Troubleshooting commands
If you have tried to start the JupyterHub proxy and it fails to start:
check if the JupyterHub IP configuration setting is c.JupyterHub.ip = '*'; if it is, try c.JupyterHub.ip = ''
c.JupyterHub.ip = '*'
c.JupyterHub.ip = ''
Try starting with jupyterhub --ip=0.0.0.0
jupyterhub --ip=0.0.0.0
Note: If this occurs on Ubuntu/Debian, check that the you are using a recent version of node. Some versions of Ubuntu/Debian come with a version of node that is very old, and it is necessary to update node.
If the sudospawner script is not found in the path, sudospawner will not run. To avoid this, specify sudospawner’s absolute path. For example, start jupyterhub with:
jupyterhub --SudoSpawner.sudospawner_path='/absolute/path/to/sudospawner'
or add:
c.SudoSpawner.sudospawner_path = '/absolute/path/to/sudospawner'
to the config file, jupyterhub_config.py.
jupyterhub_config.py
When nothing is given for these lists, there will be no admins, and all users who can authenticate on the system (i.e. all the unix users on the server with a password) will be allowed to start a server. The whitelist lets you limit this to a particular set of users, and the admin_users lets you specify who among them may use the admin interface (not necessary, unless you need to do things like inspect other users’ servers, or modify the userlist at runtime).
Even though the command to start your Docker container exposes port 8000 (docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub), it is possible that the IP address itself is not accessible/visible. As a result when you try http://localhost:8000 in your browser, you are unable to connect even though the container is running properly. One workaround is to explicitly tell Jupyterhub to start at 0.0.0.0 which is visible to everyone. Try this command: docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub --ip 0.0.0.0 --port 8000
docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub
0.0.0.0
docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub --ip 0.0.0.0 --port 8000
You receive a 500 error when accessing the URL /user/<your_name>/.... This is often seen when your single-user server cannot verify your user cookie with the Hub.
/user/<your_name>/...
There are two likely reasons for this:
The single-user server cannot connect to the Hub’s API (networking configuration problems)
The single-user server cannot authenticate its requests (invalid token)
The main symptom is a failure to load any page served by the single-user server, met with a 500 error. This is typically the first page at /user/<your_name> after logging in or clicking “Start my server”. When a single-user notebook server receives a request, the notebook server makes an API request to the Hub to check if the cookie corresponds to the right user. This request is logged.
/user/<your_name>
If everything is working, the response logged will be similar to this:
200 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 6.10ms
You should see a similar 200 message, as above, in the Hub log when you first visit your single-user notebook server. If you don’t see this message in the log, it may mean that your single-user notebook server isn’t connecting to your Hub.
If you see 403 (forbidden) like this, it’s a token problem:
403 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 4.14ms
Check the logs of the single-user notebook server, which may have more detailed information on the cause.
If you make an API request and it is not received by the server, you likely have a network configuration issue. Often, this happens when the Hub is only listening on 127.0.0.1 (default) and the single-user servers are not on the same ‘machine’ (can be physically remote, or in a docker container or VM). The fix for this case is to make sure that c.JupyterHub.hub_ip is an address that all single-user servers can connect to, e.g.:
c.JupyterHub.hub_ip
c.JupyterHub.hub_ip = '10.0.0.1'
If you receive a 403 error, the API token for the single-user server is likely invalid. Commonly, the 403 error is caused by resetting the JupyterHub database (either removing jupyterhub.sqlite or some other action) while leaving single-user servers running. This happens most frequently when using DockerSpawner, because Docker’s default behavior is to stop/start containers which resets the JupyterHub database, rather than destroying and recreating the container every time. This means that the same API token is used by the server for its whole life, until the container is rebuilt.
The fix for this Docker case is to remove any Docker containers seeing this issue (typically all containers created before a certain point in time):
docker rm -f jupyter-name
After this, when you start your server via JupyterHub, it will build a new container. If this was the underlying cause of the issue, you should see your server again.
Some certificate providers, i.e. Entrust, may provide you with a chained certificate that contains multiple files. If you are using a chained certificate you will need to concatenate the individual files by appending the chain cert and root cert to your host cert:
cat your_host.crt chain.crt root.crt > your_host-chained.crt
You would then set in your jupyterhub_config.py file the ssl_key and ssl_cert as follows:
ssl_key
ssl_cert
c.JupyterHub.ssl_cert = your_host-chained.crt c.JupyterHub.ssl_key = your_host.key
Your certificate provider gives you the following files: example_host.crt, Entrust_L1Kroot.txt and Entrust_Root.txt.
example_host.crt
Entrust_L1Kroot.txt
Entrust_Root.txt
Concatenate the files appending the chain cert and root cert to your host cert:
cat example_host.crt Entrust_L1Kroot.txt Entrust_Root.txt > example_host-chained.crt
You would then use the example_host-chained.crt as the value for JupyterHub’s ssl_cert. You may pass this value as a command line option when starting JupyterHub or more conveniently set the ssl_cert variable in JupyterHub’s configuration file, jupyterhub_config.py. In jupyterhub_config.py, set:
example_host-chained.crt
c.JupyterHub.ssl_cert = /path/to/example_host-chained.crt c.JupyterHub.ssl_key = /path/to/example_host.key
where ssl_cert is example-chained.crt and ssl_key to your private key.
Then restart JupyterHub.
See also JupyterHub SSL encryption.
Both conda and pip can be used without a network connection. You can make your own repository (directory) of conda packages and/or wheels, and then install from there instead of the internet.
For instance, you can install JupyterHub with pip and configurable-http-proxy with npmbox:
python3 -m pip wheel jupyterhub npmbox configurable-http-proxy
Setting the following in jupyterhub_config.py will configure access to the entire filesystem and set the default to the user’s home directory.
c.Spawner.notebook_dir = '/' c.Spawner.default_url = '/home/%U' # %U will be replaced with the username
From the command line, pySpark executors can be configured using a command similar to this one:
pyspark --total-executor-cores 2 --executor-memory 1G
Cloudera documentation for configuring spark on YARN applications provides additional information. The pySpark configuration documentation is also helpful for programmatic configuration examples.
While JupyterLab is still under active development, we have had users ask about how to try out JupyterLab with JupyterHub.
You need to install and enable the JupyterLab extension system-wide, then you can change the default URL to /lab.
/lab
For instance:
python3 -m pip install jupyterlab jupyter serverextension enable --py jupyterlab --sys-prefix
The important thing is that jupyterlab is installed and enabled in the single-user notebook server environment. For system users, this means system-wide, as indicated above. For Docker containers, it means inside the single-user docker image, etc.
In jupyterhub_config.py, configure the Spawner to tell the single-user notebook servers to default to JupyterLab:
c.Spawner.default_url = '/lab'
Set up JupyterHub using OAuthenticator for GitHub authentication
Configure whitelist to be an empty list in jupyterhub_config.py
Configure admin list to have workshop leaders be listed with administrator privileges.
Users will need a GitHub account to login and be authenticated by the Hub.
You can do this with logrotate, or pipe to logger to use syslog instead of directly to a file.
logger
For example, with this logrotate config file:
/var/log/jupyterhub.log { copytruncate daily }
and run this daily by putting a script in /etc/cron.daily/:
/etc/cron.daily/
logrotate /path/to/above-config
Or use syslog:
jupyterhub | logger -t jupyterhub
The following commands provide additional detail about installed packages, versions, and system information that may be helpful when troubleshooting a JupyterHub deployment. The commands are:
System and deployment information
jupyter troubleshooting
Kernel information
jupyter kernelspec list
Debug logs when running JupyterHub
jupyterhub --debug
The Apache Toree kernel will an issue, when running with JupyterHub, if the standard HDFS rack awareness script is used. This will materialize in the logs as a repeated WARN:
16/11/29 16:24:20 WARN ScriptBasedMapping: Exception running /etc/hadoop/conf/topology_script.py some.ip.address ExitCodeException exitCode=1: File "/etc/hadoop/conf/topology_script.py", line 63 print rack ^ SyntaxError: Missing parentheses in call to 'print' at `org.apache.hadoop.util.Shell.runCommand(Shell.java:576)`
In order to resolve this issue, there are two potential options.
Update HDFS core-site.xml, so the parameter “net.topology.script.file.name” points to a custom script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point to a python two installation (e.g. /usr/bin/python).
In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).
Docker images can be found at the JupyterHub organization on DockerHub. The Docker image jupyterhub/singleuser provides an example single user notebook server for use with DockerSpawner.
Additional single user notebook server images can be found at the Jupyter organization on DockerHub and information about each image at the jupyter/docker-stacks repo.