Jupyter Launcher

The Jupyter launcher provides a simple interface to start Jupyter Notebook servers on the DTU HPC cluster.

Getting started

To start the launcher, go to Tools and select Jupyter launcher. You need to be logged in to access the launcher, as it will submit a job to DTU HPC to start the server.

You'll see the interface, with the basic options that are needed for the server:

• CPUs Specify the number of CPUs to request.
• GPU If selected, the job will be routed to one of the GPU queues.
• Memory Set the maximum amount of RAM required. Please be realistic and conservative with this value.
• Wall time Define the maximum run time for the server.
• Root directory Determine the starting directory for the server, which affects the files you can access. By default, it will start in your home directory at DTU, but you can enter any accessible path here.
• Conda environment If selected, the server will start with one of the standard Conda environments. Choose qim3d if unsure. Leave this blank to avoid starting an environment, but ensure that Jupyter is installed in your base environment.

Once you've selected your options, click Launch Jupyter Server to submit the job to the cluster. This process typically takes around 30 seconds, provided the queue isn't too busy. If it takes longer than one minute, try refreshing the page and submitting the request again. If the issue persists, please contact us for assistance.

When the server is ready, a button labeled Open Jupyter Server will appear:

Click this button to open the Jupyter interface in a new tab. If you need a server with different configurations, you can use the Re-launch Jupyter Server option. However, note that only one server can be running at a time.

Extra options

The interface is made of a few tabs:

• Basic config
• Advanced config
• About the launcher
• Logs

The tab Advanced config will allow you to set some parameters for more specific cases.

• Hostname This is the address used for SSH connections and job submissions. Change this if you need to connect to a specific server.

• Job name Provide a name for the submitted job. Modify this if you need a more descriptive or specific name.

• Source path Enter a path here that will be sourced before starting the Jupyter server. This is useful for activating a custom environment. For instance, to use the default qim3d Conda environment, you might use a path like "/dtu/3d-imaging-center/QIM/conda/miniconda3/bin/activate". Note that this field is ignored if a Conda environment is selected in the "Basic Config" tab.

• Conda environment Specify the name of a Conda environment to activate. This activation will occur after the path from the previous field is sourced. For example, entering "qim3d" here is equivalent to running "conda activate qim3d" in the terminal.

• Reset SSH tunnels By default, the SSH tunnel between the cluster node and the platform is reset each time a new server is launched. If you experience connection issues, try unchecking this box to see if it resolves the problem.

Logs

The final tab provides detailed logs that are updated throughout the process. Here, you'll find additional information about the job script, requested resources, and the node where the server is running. If you're experiencing connection issues, check the logs for useful debugging information that might help resolve the problem.