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Computational jobs on the AI cluster are managed with a SLURM job manager. We provide an in-depth tutorial on how to use SLURM <placeholder>, but some basic examples that are immediately applicable on the AI cluster will be discussed in this section.

Important notice:

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1. load python module

   Code Block
   language bash
   # list existing modules with  module avail # load suitable python module (miniconda in this example) module load miniconda3-4.10.3-gcc-12.2.0-hgiin2a

2. create new environment

   Code Block
   language bash
   conda create -n myvenv python=3.10 # and follow prompts...

3. once this new environment is created, activate it with

   Code Block
   language bash
   # initialize conda      conda init bash # or "conda init csh" if csh is used      # list existing environments      conda env list      # activate newly installed env from previous step      conda activate myvenv      # if you need to deactivate      conda deactivate myvenv

4. After all this is done, conda will automatically load ~base~ environment upon every login to the cluster. To prevent this, run

   Code Block
   language bash
   conda config --set auto_activate_base false

5. Use this env to install jupyter (and other required packages)

   Code Block
   language bash
   conda install jupyter

working with pip

1. load python module

   Code Block
   language bash
   # list existing modules with  module avail # load suitable python module (miniconda in this example) module load miniconda3-4.10.3-gcc-12.2.0-hgiin2a

2. Create new environment and activate it

   Code Block
   language bash
   # create venv python -m venv ~/myvenv # and activate it source ~/myvenv/bin/activate

3. Install jupyter into this new virtual environments:

   Code Block
   language bash
   pip install jupyter

Submit a slurm job

Once conda is installed, submit a slurm SLURM job. Prepare this slurm SLURM batch script similar to this (use your own SBATCH arguments, like job name and the amount of resources you need, this is only an example):

#!/bin/bash

#SBATCH --job-name=<myJobName> # give your job a name
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --time=00:30:00 # set this time according to your need
#SBATCH --mem=3GB # how much RAM will your notebook consume?
#SBATCH --gres=gpu:1 # if you need to use a GPU
#SBATCH -p ai-gpu # specify partition

module purge
module load miniconda3-4.10.3-gcc-12.2.0-hgiin2a
# if using conda
conda activate myvenv
# if using pip
# source ~/myvev/bin/activate

# set log file
LOG="/home/${USER}/jupyterjob_${SLURM_JOB_ID}.txt"

# gerenerate random port
PORT=`shuf -i 10000-50000 -n 1`

# print useful info
cat << EOF > ${LOG}
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~ Slurm Job $SLURM_JOB_ID
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Hello from the Jupyter job!

In order to connect to this jupyter session
setup a tunnel on your local workstation with:
     --->  ssh -t ${USER}@ai-login01.med.cornell.edu -L ${PORT}:localhost:${PORT} ssh ${HOSTNAME} -L ${PORT}:localhost:${PORT}
(copy above command and paste it to your terminal).

Depending on your ssh configuration, you may be
prompted for your password. Once you are logged in,
leave the terminal running and don't close it until
you are finished with your Jupyter session.

Further down look for a line similar to
     ---> http://127.0.0.1:10439/?token=xxxxyyyxxxyyy
Copy this line and paste in your browser
EOF

# start jupyter
jupyter-notebook --no-browser --ip=0.0.0.0 --port=${PORT} 2>&1 | tee -a ${LOG}

Save this file somewhere in your file space on the login node and submit a batch slurm job with

sbatch script_name.txt 

Set up connection to the jupyter servers

Slurm job will generate a text file ~/jupyterjob_<JOBID>.txt. Follow instructions in this file to connect to the jupyter session. Two steps that need to be take are:

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