Using Conda Environments
Using Widgets
Requesting a GPU
Do Not Run Jupyter on the Login Nodes
Base Conda Environment
Custom Conda Environment
Another Approach
Running as a batch job
#!/bin/bash #PBS -m abe #PBS -M YourEmail@griffithuni.edu.au #PBS -N myJupyterJob ###If not using gpu,you should not request gpuq2. You can use workq or something similar #PBS -q gpuq2 ### Number of nodes:Number of CPUs:Number of threads per node. ###If not using gpu,you should not request ngpus #PBS -l select=1:ncpus=1:ngpus=1:mem=10gb,walltime=10:00:00 # The job's working directory cd $PBS_O_WORKDIR module load anaconda3/2021.11 #activate your env #source activate protein source /usr/local/bin/s3proxy.sh unset PYTHONPATH jupyter-lab --no-browser --port=8889 --ip=0.0.0.0
After running the above script and the job has started on the compute node, you run the following command on your local machine to start port forwarding.
For n060 gpu node
ssh -CNL 8889:localhost:8889 s123456@n060.rcs.griffith.edu.au
For node gpu node n061:
ssh -N -f -L 8889:n061:8889 -J s123456@gc-prd-hpclogin1.rcs.griffith.edu.au s123456@n061
Note that we selected the Linux port 8889 in the above command to connect to the notebook. If you don't specify the port, it will default to port 8888 but sometimes this port can be already in use either on the remote machine or the local one
(i.e., your laptop). If the port you selected is unavailable, you will get an error message, in which case you should just pick another one. It is best to keep it greater than 1024.
Consider starting with 8888 and increment by 1 if it fails, e.g., try 8888, 8889, 8890 and so on. If you are running on a different port then substitute your port number for 8889.
Lastly, open a web browser on your laptop/desktop and copy and paste the URL from the previous output: