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- login to open ondemand at https://s3df.slac.stanford.edu/ondemand
- click on the top menu bar and choose interactive apps → Jupyter
- Select Custom for Jupyter Image
- Select Conda Environment
- Make sure the conda env you pick has jupyterlab installed.
- Customize this text that is shown:
Code Block export CONDA_PREFIX=<path-to-miniconda3> export PATH=${CONDA_PREFIX}/bin/:$PATH source ${CONDA_PREFIX}/etc/profile.d/conda.sh conda env list conda activate <your-environment-name>- Set the path to the conda env setup (
CONDA_PREFIX)to run the jupyter notebook. ( conda info | grep "active env location" can show the path to the current active conda env setup (probably miniforge3). You would need to ssh into iana on s3df, activate a conda env that you made with jupyterlab and then run the command to find its path. ) An example output is/sdf/home/s/sanjeev/miniforge3/
(an example of setting up a conda env for the first time for jupyter lab is here in the lume impact jupyter example) - change <environment> to be the desired conda env path, for example
/sdf/home/s/sanjeev/miniforge3/envs/bmadI have a conda env called bmad. Another explanation is here. - Click use JupyterLab instead of Jupyter Notebook
For run on cluster type select: Batch; s3df; account: FACET; Partition Milano *make sure your account is part of the FACET repo or use ad:beamphysics if your part of that.
- Select number of hours and cores (max cores is 120)
- Configure rest of settings
- Launch and connect
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