We have deployed JupyterHub at https://jupyter.stat.berkeley.edu so that you may remotely run the following on SCF machines through your browser:
- Jupyter notebooks (including Python, R, Julia, and MATLAB notebooks).
- VS Code
- Linux desktops
By default, your server will be spawned onto the first available standalone Linux machine, however you may optionally start your server on a cluster node in case you need access to more processing power. You can also pass SBATCH options to your server, and specify prologue commands that will run prior to your server startup. Please let us know if you have any difficulties or if you need us to add features to it.
Stopping Your Server
To stop your server (and free up resources for other users), please visit the control panel by selecting File > Hub Control Panel from within Jupyter Lab. Then click "Stop My Server". Note that selecting "Logout" does not free up resources for other users as it keeps your server running.
Some non-personal accounts are shared amongst multiple people to ease the management burden of large datasets and/or code development. While SSH access to such accounts is managed through SSH keys, our JupyterHub has a different method. If you have been authorized to use a shared account, when you login 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 firstname.lastname@example.org.
Users of shared accounts are encouraged to create independent named servers within JupyterHub, as documented below.
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.stat.berkeley.edu/hub/home. You can get there by clicking the Home button at the top-left. Specify an arbitrary name in the "Name your server" field, then click "Add New 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.stat.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.stat.berkeley.edu/user/username while your named servers will be available at https://jupyter.stat.berkeley.edu/user/username/servername/.
All Python 3 and R packages installed on the system, as well as packages installed by users within their home directories, should be available.
Using a Custom Conda Environment
If you would like your jupyter notebook to leverage your own conda environment, you must prepare a custom Jupyter kernel:
# Switch to your conda environment source activate /path/to/your/custom/conda/env # Install the ipykernel package for python conda install ipykernel # Create a new jupyter kernel python -m ipykernel install --user --name=mycondakernel
Once you have created the kernel, you can access it from the JupyterLab launcher. You can also prepare an R-based kernel:
# Use the conda-forge channel conda config --add channels conda-forge conda config --set channel_priority strict # Activate your environment source activate ~/.conda/envs/myr # Install a faster package resolver conda install mamba # Install R and the IRkernel mamba install r r-irkernel # Create a new jupyter kernel named "My R" r -e "IRkernel::installspec(displayname='My R')"