JupyterHub

EML’s JupyterHub is deployed for users to remotely run Jupyter Lab (with Python, R, MATLAB, and iTorch), terminal sessions, RStudio, VS Code, and even graphical desktop sessions on EML machines.

Starting Your Server¶

By default, your notebook will be spawned onto the first available standalone Linux server. For more processing power, choose the partition that will use the high-performance computing cluster: “low” for the low priority partition and the “high” for the high priority partition

You can also pass SBATCH options to your notebook and specify prologue commands that will run prior to your notebook startup.

Long Running Code¶

Interactive programs like Jupyter Lab, RStudio, or VS Code might not respond well if you launch long running jobs interactively from their UIs, and then close your device, reconnect to another network, and re-open your browser. They will often work just fine, but there can be problems. If you need to run code that you expect to take a long time to complete, it would be best to run it as a batch job on the cluster. You can submit the job from your Jupyter session or by connecting with SSH to a login node.

Stopping Your Server¶

To stop your server (and free up resources for other users), please visit the “Hub Control Panel” and choose “Stop My Server”. Note that selecting “Logout” does not free up resources for other users as it keeps your server running.

Shared Accounts¶

Some research accounts are shared among multiple people to ease the management burden of large datasets and/or code development. SSH access to such accounts is managed through SSH keys, however our JupyterHub has a different method. If you have been authorized to use a shared account, you can specify a username of `your_username@shared_username`, and then your own password, e.g. `jane_doe@big_research`. You can request access to shared accounts through the faculty who manages the account or through manager@econ.berkeley.edu.

Users of shared accounts are encourage to create independent named servers within JupyterHub, as documented below.

Named Servers¶

It is possible to start up multiple servers on the cluster, analogously to how you might start up more than one job on the cluster. This is useful if you want to provide your jupyter server with different hardware resources or cluster options, or if you are sharing a research account and want to let each user run a different jupyter server.

After you login, do not click Start on the Server Options page. Instead, visit the control panel at https://jupyter.econ.berkeley.edu/hub/home. You can get there by clicking the Home button at the top-left. Specify a name in the “Name your server” field, then click “Add New Server”. If you are using a shared account, consider naming the server after your own EML username or after your name. This helps the other account users to tell the servers apart. If you are working from your own account, consider naming your additional servers after your project or the cluster characteristics of your job. After naming and adding the server, you will then be prompted to specify spawning options.

If you have multiple named servers running, just navigate to the hub control panel by clicking the Home button at the top left, clicking File > Hub Control Panel from within Lab, or by visiting https://jupyter.econ.berkeley.edu/hub/home directly. There is no hub control panel button from within RStudio. You can access, modify, or stop the servers there. Note that your default server will be available at https://jupyter.econ.berkeley.edu/user/username while your named servers will be available at https://jupyter.econ.berkeley.edu/user/username/servername/.

Using RStudio, VS Code, Linux desktop¶

Within JupyterLab, click the application from the launcher. If you don’t see the launcher, click File > New Launcher.