When troubleshooting, you may see unexpected behaviors or receive an error message. This section provides links for identifying the cause of the problem and how to resolve it.


JupyterHub proxy fails to start#

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 = ''

  • Try starting with jupyterhub --ip=

Note: If this occurs on Ubuntu/Debian, check that you are using a recent version of Node. Some versions of Ubuntu/Debian come with a very old version of Node and it is necessary to update Node.

sudospawner fails to run#

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,

What is the default behavior when none of the lists (admin, allowed, allowed groups) are set?#

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 allowed username set lets you limit this to a particular set of users, and 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 user list at runtime).

JupyterHub Docker container is not accessible at localhost#

Even though the command to start your Docker container exposes port 8000 (docker run -p 8000:8000 -d --name 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 which is visible to everyone. Try this command: docker run -p 8000:8000 -d --name jupyterhub jupyterhub --ip --port 8000

How can I kill ports from JupyterHub-managed services that have been orphaned?#

I started JupyterHub + nbgrader on the same host without containers. When I try to restart JupyterHub + nbgrader with this configuration, errors appear that the service accounts cannot start because the ports are being used.

How can I kill the processes that are using these ports?

Run the following command:

sudo kill -9 $(sudo lsof -t -i:<service_port>)

Where <service_port> is the port used by the nbgrader course service. This configuration is specified in

Why am I getting a Spawn failed error message?#

After successfully logging in to JupyterHub with a compatible authenticator, I get a ‘Spawn failed’ error message in the browser. The JupyterHub logs have jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>.

This issue occurs when the authenticator requires a local system user to exist. In these cases, you need to use a spawner that does not require an existing system user account, such as DockerSpawner or KubeSpawner.

How can I run JupyterHub with sudo but use my current environment variables and virtualenv location?#

When launching JupyterHub with sudo jupyterhub I get import errors and my environment variables don’t work.

When launching services with sudo ... the shell won’t have the same environment variables or PATHs in place. The most direct way to solve this issue is to use the full path to your python environment and add environment variables. For example:

sudo MY_ENV=abc123 \
  /home/foo/venv/bin/python3 \


Error 500 after spawning my single-user server#

You receive a 500 error while accessing the URL /user/<your_name>/.... This is often seen when your single-user server cannot verify your user cookie with the Hub.

There are two likely reasons for this:

  1. The single-user server cannot connect to the Hub’s API (networking configuration problems)

  2. 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.

If everything is working, the response logged will be similar to this:

200 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@ 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 is not connecting to your Hub.

If you see 403 (forbidden) like this, it is likely a token problem:

403 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@ 4.14ms

Check the logs of the single-user notebook server, which may have more detailed information on the cause.

Causes and resolutions#

No authorization request#

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 (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 = ''
Proxy settings (403 GET)#

When your whole JupyterHub sits behind an organization proxy (not a reverse proxy like NGINX as part of your setup and not the configurable-http-proxy) the environment variables HTTP_PROXY, HTTPS_PROXY, http_proxy, and https_proxy might be set. This confuses the JupyterHub single-user servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the single-user server thinking it has the wrong auth token. To circumvent this you should add <hub_url>,<hub_ip>,localhost, to the environment variables NO_PROXY and no_proxy.

Launching Jupyter Notebooks to run as an externally managed JupyterHub service with the jupyterhub-singleuser command returns a JUPYTERHUB_API_TOKEN error#

Services allow processes to interact with JupyterHub’s REST API. Example use-cases include:

  • Secure Testing: provide a canonical Jupyter Notebook for testing production data to reduce the number of entry points into production systems.

  • Grading Assignments: provide access to shared Jupyter Notebooks that may be used for management tasks such as grading assignments.

  • Private Dashboards: share dashboards with certain group members.

If possible, try to run the Jupyter Notebook as an externally managed service with one of the provided jupyter/docker-stacks.

Standard JupyterHub installations include a jupyterhub-singleuser command which is built from the jupyterhub.singleuser:main method. The jupyterhub-singleuser command is the default command when JupyterHub launches single-user Jupyter Notebooks. One of the goals of this command is to make sure the version of JupyterHub installed within the Jupyter Notebook coincides with the version of the JupyterHub server itself.

If you launch a Jupyter Notebook with the jupyterhub-singleuser command directly from the command line, the Jupyter Notebook won’t have access to the JUPYTERHUB_API_TOKEN and will return:

    JUPYTERHUB_API_TOKEN env is required to run jupyterhub-singleuser.
    Did you launch it manually?

If you plan on testing jupyterhub-singleuser independently from JupyterHub, then you can set the API token environment variable. For example, if you were to run the single-user Jupyter Notebook on the host, then:

export JUPYTERHUB_API_TOKEN=my_secret_token

With a docker container, pass in the environment variable with the run command:

docker run -d \
  -p 8888:8888 \
  -e JUPYTERHUB_API_TOKEN=my_secret_token \

This example demonstrates how to combine the use of the jupyterhub-singleuser environment variables when launching a Notebook as an externally managed service.

How do I…?#

Use a chained SSL certificate#

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 chained 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 file the ssl_key and ssl_cert as follows:

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.

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, In, set:

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 Enabling SSL encryption.

Install JupyterHub without a network connection#

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

I want access to the whole filesystem and still default users to their home directory#

Setting the following in 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

How do I increase the number of pySpark executors on YARN?#

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.

How do I use JupyterLab’s pre-release version with JupyterHub?#

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.

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, configure the Spawner to tell the single-user notebook servers to default to JupyterLab:

c.Spawner.default_url = '/lab'

How do I set up JupyterHub for a workshop (when users are not known ahead of time)?#

  1. Set up JupyterHub using OAuthenticator for GitHub authentication

  2. Configure the admin list to have workshop leaders listed with administrator privileges.

Users will need a GitHub account to log in and be authenticated by the Hub.

How do I set up rotating daily logs?#

You can do this with logrotate, or pipe to logger to use Syslog instead of directly to a file.

For example, with this logrotate config file:

/var/log/jupyterhub.log {

and run this daily by putting a script in /etc/cron.daily/:

logrotate /path/to/above-config

Or use syslog:

jupyterhub | logger -t jupyterhub

Toree integration with HDFS rack awareness script#

The Apache Toree kernel will have 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/ some.ip.address
ExitCodeException exitCode=1:   File "/etc/hadoop/conf/", line 63
    print rack
SyntaxError: Missing parentheses in call to 'print'

    at `org.apache.hadoop.util.Shell.runCommand(`

In order to resolve this issue, there are two potential options.

  1. Update HDFS core-site.xml, so the parameter “” points to a custom script (e.g. /etc/hadoop/conf/ Copy the original script and change the first line point to a python two installation (e.g. /usr/bin/python).

  2. In add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).

How can I view the logs for JupyterHub or the user’s Notebook servers when using the DockerSpawner?#

Use docker logs <container> where <container> is the container name defined within docker-compose.yml. For example, to view the logs of the JupyterHub container use:

docker logs hub

By default, the user’s notebook server is named jupyter-<username> where username is the user’s username within JupyterHub’s database. So if you wanted to see the logs for user foo you would use:

docker logs jupyter-foo

You can also tail logs to view them in real-time using the -f option:

docker logs -f hub

Troubleshooting commands#

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 troubleshoot
  • Kernel information

jupyter kernelspec list
  • Debug logs when running JupyterHub

jupyterhub --debug