LPC

This is possible, but with a little less user support. You will need to use the following command to open up your ssh connection:

ssh -L localhost:8888:localhost:8888 <username>@cmslpc-sl7.fnal.gov

Replace <username> with your LPC username. Then cd to the directory of your choice where you will clone the repository as before:

git clone https://github.com/FNALLPC/machine-learning-das

In order to open Jupyter with all the appopriate libraries, you will need to have either installed a conda environment with Jupyter in your nobackup area (where you have more space) or have a CMSSW environment setup. To install and activate a conda environment you can do:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O $HOME/nobackup/miniconda3.sh
bash $HOME/nobackup/miniconda3.sh -b -f -u -p $HOME/nobackup/miniconda3
source $HOME/nobackup/miniconda3/etc/profile.d/conda.sh
conda env create -f environment.yml
conda activate machine-learning-das

Once you have a conda environment, open Jupyter (which is actually the Jupyter that gets installed as part of the conda environment):

jupyter notebook --no-browser --port=8888 --ip 127.0.0.1

If everything worked, the last line of the output should be a url of the form:

http://127.0.0.1:8888/?token=<long string of numbers and letters>

Copy this url into your browser. You may now perform the rest of the exercise like normal, except you will have to change the kernel to the default python3 one (it will have all the necessary libararies because you are using the Jupyter version that is part of your conda installation).

In the future, when you need access to Jupyter and want to run this exercise, you can do source $HOME/nobackup/miniconda3/etc/profile.d/conda.sh; conda activate machine-learning-das