The SLAC JupyterLab

How to launch JupyterLab at SLAC

Once you login to the main portal, click "My Interactive Sessions" from the top menu bar. Then choose "Jupyter". You will need to make a few choices:

In "Jupyter Instance" box, choose "atlas/20210403". You can choose Jupyter Instances for other experiments but there is no guarantee that those instances will work for you. You can also bring your own Jupyter environment (under Custom)
Check the "Use JupyterLab instead of Jupyter Notebook?" box.
In the accoutn box, type "atlas:usatlas".
In the "Partition" box, choose "rome" for CPU only session, or "ampere" if you need GPUs
Choose hours, # CPUs, memory, # GPUs and GPU type, then click "launch". Note that your Jupyter work runs as a SLURM job. So choose only what you need to ensure speedy launching of your job.

Run your own Jupyter environment

The ATLAS instance we built may not satisfy your need. If you have your own Jupyter environment that is accessible from SLAC (on SLAC disk or in CVMFS), you may be able to run it on SLAC's Jupyter infrastracture. To do so:

Following the same steps above to launch Jupyter at SLAC.
Instead of choosing the "atlas-jupyter..." instance, you choose "Custom Singularity Image" or "Custom Conda Environment".
The "Commands to initate Jupyter" box will be pre-filled with commands to prepare your instance. You can edit/paste whatever Shell script to the box to prepare launching your Jupyter environment.
A backend launching script will "source" your Shell script. It expects that after "sourcing", there is a command (or Shell function) called "jupyter" for it to run. It will then run one of the following commands, jupyter notebook or jupyter lab (depend on whether you choose Jupyter Notebook or Jupyter Lab) to launch your Jupyter environment.
Below are example scripts to prepare for launching Jupyter from a Singularity container:

export SINGULARITY_IMAGE=/sdf/data/atlas/u/$(id -un)/.../my_singularity_image.sif
function jupyter() { singularity exec --nv -B /sdf,/cvmfs ${SINGULARITY_IMAGE} jupyter $@ }

or from a Conda environment (assuming Anaconda 3 is installed at ~/anaconda3, and jupyterlab is installed via Conda):

source ~/anaconda3/etc/profile.d/conda.sh
conda activate

Kernels and extensions in the ATLAS Jupyter environment

The Jupyter environment provides several kernels and extensions. This includes:

python2 with pyroot and uproot. By default, AnalysisBase,21.2.111 is loaded before the pyroot2 kernel is launched. To overwrite this, create a file $HOME/notebooks/.user_setups in your home directory (even if your home directory is in AFS)
ROOT C++. The ATLAS environment is set before the kernel is launched. The overwrite method is the same as the above.
python3 with pyroot, uproot3/awkward and Dask.
python3 with pyroot, uproot3/awkward and RAPIDS.AI packages (cuPy, cuDF, cuML, Dask/Dask-CUDA, etc.). Choose "# of GPUs" (at least 1) and "GPU type" before launching Jupyter.
python3 with pyroot, uproot3/awkward and TensorFlow(GPU) and Keras. Choose "# of GPUs" (at least 1) and "GPU type" before launching Jupyter.
Terminal console for simple interactive use, e.g. file managements. It also include python2.7/python3, gcc/g++, gdb, make, cmake3, xrootd-clients, openssh-client, curl, vi, SLURM clients, etc.
Markdown document editor and previewer. You can edit and preview in two tabs simultaneously.

Extend ATLAS JupyterLab Functionalities

Python's pip module allows users to add packages to the JupyterLab environment as they need. For example, one can use PYCUDA and DASK distributed scheduling with SLURM. Check out this doc on how to do that.

Getting help

Please use the following e-mail addresses to get help. The division below is not strict. Questions will be routed to appropriate staff members. 1. Use atlas-us-slac-acf@cern.ch for ATLAS specific questions and requestions, including ATLAS software related issues. 2. Use unix-admin@slac.stanford.edu for all other questions