Interactive Use

Dask-jobqueue is most often used for interactive processing using tools like IPython or Jupyter notebooks. This page provides instructions on how to launch an interactive Jupyter notebook server and Dask dashboard on your HPC system.

Using Jupyter

It is convenient to run a Jupyter notebook server on the HPC for use with dask-jobqueue. You may already have a Jupyterhub instance available on your system, which can be used as is. Otherwise, documentation for starting your own Jupyter notebook server is available at Pangeo documentation.

Once Jupyter is installed and configured, using a Jupyter notebook is done by:

  • Starting a Jupyter notebook server on the HPC (it is often good practice to run/submit this as a job to an interactive queue, see Pangeo docs for more details).

$ jupyter notebook --no-browser --ip=`hostname` --port=8888
  • Reading the output of the command above to get the ip or hostname of your notebook, and use SSH tunneling on your local machine to access the notebook. This must only be done in the probable case where you don’t have direct access to the notebook URL from your computer browser.

$ ssh -N -L 8888:x.x.x.x:8888 [email protected]_domain

Now you can go to http://localhost:8888 on your browser to access the notebook server.

Viewing the Dask Dashboard

Whether or not you are using dask-jobqueue in Jupyter, IPython or other tools, at one point you will want to have access to Dask dashboard. Once you’ve started a cluster and connected a client to it using commands described in Example), inspecting client object will give you the Dashboard URL, for example The Dask Dashboard may be accessible by clicking the link displayed, otherwise, you’ll have to use SSH tunneling:

# General syntax
$ ssh -fN [email protected] -L port-number:localhost:port-number
# As applied to this example:
$ ssh -fN [email protected] -L 8787:localhost:8787

Now, you can go to http://localhost:8787 on your browser to view the dashboard. Note that you can do SSH tunneling for both Jupyter and Dashboard in one command.

A good example of using Jupyter along with dask-jobqueue and the Dashboard is available below:

Dask Dashboard with Jupyter

If you are using dask-jobqueue within Jupyter, one user friendly solution to see the Dashboard is to use nbserverproxy. As the dashboard HTTP end point is launched inside the same node as Jupyter, this is a great solution for viewing it without having to do SSH tunneling. You just need to install nbserverproxy in the Python environment you use for launching the notebook, and activate it as indicated in the docs:

pip install nbserverproxy
jupyter serverextension enable --py nbserverproxy

Then, once started, the dashboard will be accessible from your notebook URL by adding the path /proxy/8787/status, replacing 8787 by any other port you use or the dashboard is bind to if needed. Sor for example:

  • http://localhost:8888/proxy/8787/status with the example above

  • if using JupyterHub

Note that if using Jupyterhub, the service admin should deploy nbserverproxy on the environment used for starting singleuser notebook, but each user may have to activate the nbserverproxy extension.

Finally, you may want to update the dashboard link that is displayed in the notebook, shown from Cluster and Client objects. In order to do this, edit dask config file, either ~/.config/dask/jobqueue.yaml or ~/.config/dask/distributed.yaml, and add the following: "/proxy/{port}/status" # for user launched notebook "/user/{JUPYTERHUB_USER}/proxy/{port}/status" # for jupyterhub launched notebook