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.

We recommend first doing these steps from a login node (nothing will be computationally intensive) but at some point you may want to shift to a compute or interactive node.

Note: We also recommend the JupyterHub project, which allows HPC administrators to offer and control the process described in this document automatically. If you find this process valuable but tedious, then you may want to ask your system administrators to support it with JupyterHub.

Install JupyterLab

We recommend using JupyterLab, and the Dask JupyterLab Extension. This will make it easy to get Dask’s dashboard through your Jupyter session.

These can be installed with the following steps:

# Install JupyterLab and NodeJS (which we'll need to integrate Dask into JLab)
conda install jupyterlab nodejs -c conda-forge -y

# Install server-side pieces of the Dask-JupyterLab extension
pip install dask_labextension

# Integrate Dask-Labextension with Jupyter (requires NodeJS)
jupyter labextension install dask-labextension

You can also use pip rather than conda, but you will have to find some other way to install NodeJS.

Dask JupyterLab Dashboard

Add A Password

For security, we recommend adding a password to your Jupyter notebook configuration.

jupyter notebook password

This is good both for security, and also means that you won’t have to copy around Jupyter tokens.

Start Jupyter

When you use Jupyter on your laptop you often just write jupyter notebook or jupyter lab. However, things are a bit different when starting a notebook server on a separate machine. As a first step, the following will work:

jupyter lab --no-browser --ip="*" --port 8888

Later, once we get SSH tunneling set up, you may want to come back and specify a specific IP address or hostname for added security.

SSH Tunneling

If your personal machine is on the same network as your cluster, then you can ignore this step.

If you are on a different network (like your home network), and have to SSH in, then it can be difficult to have your local web browser connect to the Jupyter server running on the HPC machine. If your institution doesn’t have something like JupyterHub set up, then the easiest way to accomplish this is to use SSH tunneling.

Often a command like the following works:

ssh -L 8888:login-node-hostname:8888

Where login-node-hostname and are placeholders that you need to fill in:

  • login-node-hostname is the name of the node from which you are

    running your Jupyter server, designated hostname below.

    username@hostname$ jupyter lab --no-browser --ip="*" --port 8888

    You might also run echo $HOSTNAME on that machine as well to see the host name.

  • is the address that you usually use to ssh into the cluster.

So in a real example this might look like the following:

alice@login2.summit $ jupyter lab --no-browser --ip="login2" --port 8888
alice@laptop        $ ssh -L 8888:login2:8888

Additionally, if port 8888 is busy then you may want to choose a different port, like 9999. Someone else may be using this port, particularly if they are setting up their own Jupyter server on this machine.

You can now visit http://localhost:8888 on your local browser to access the Jupyter server.

Viewing the Dask Dashboard

When you start a Dask Jobqueue cluster you also start a Dask dashboard. This dashboard is valuable to help you understand the state of your computation and cluster.

Typically, the dashboard is served on a separate port from Jupyter, and so can be used whether you choose to use Jupyter or not. If you want to open up a connection to see the dashboard you can do so with SSH Tunneling as described above. The dashboard’s default port is at 8787, and is configurable by using the scheduler_options parameter in the Dask Jobqueue cluster object. For example scheduler_options={'dashboard_address': ':12435'} would use 12435 for the web dashboard port.

However, Jupyter is also able to proxy the dashboard connection through the Jupyter server, allowing you to access the dashboard at http://localhost:8888/proxy/8787/status. This requires no additional SSH tunneling. Additionally, if you place this address into the Dask Labextension search bar (click the Dask logo icon on the left side of your Jupyter session) then you can access the plots directly within Jupyter Lab, rather than open up another tab.


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:

  • for user launched notebooks

        link: "/proxy/{port}/status"
  • for JupyterHub launched notebooks

        link: "/user/{JUPYTERHUB_USER}/proxy/{port}/status"