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Troubleshooting

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

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)?

Troubleshooting commands

Behavior

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=0.0.0.0

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, jupyterhub_config.py.

Errors

500 error after spawning my single-user server

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.

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)

Symptoms

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/jupyter-hub-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/jupyter-hub-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.

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 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 = '10.0.0.1'
403 GET /hub/api/authorizations/cookie

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.

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 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:

c.JupyterHub.ssl_cert = your_host-chained.crt
c.JupyterHub.ssl_key = your_host.key

Example

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, jupyterhub_config.py. In jupyterhub_config.py, 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 JupyterHub 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:

pip wheel jupyterhub
npmbox configurable-http-proxy

I want access to the whole filesystem, but still default users to their home directory

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

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 prerelease 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:

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'

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 whitelist to be an empty list in jupyterhub_config.py
  3. 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.

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 troubleshooting
  • Kernel information
jupyter kernelspec list
  • Debug logs when running JupyterHub
jupyterhub --debug

Toree integration with HDFS rack awareness script

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.

  1. 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).
  2. In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).