start jupyter lab (python and R) server on LSF farm5 node via bsub.
ssh on farm5, clone repo and run start script with arguments:
! This requires conda to be activated, see instructions below if not done already !
git clone https://github.com/wtsi-hgi/bsub_jupyter_lab.git && cd bsub_jupyter_lab
./bsub_jupyter_lab.sh -g hgi -c 4 -m 50000 -q normal
-g
is your LSF group-c
is the number of CPUs requested-m
is the memory requested (50000 means 50G)-q
is the LSF queue
The address:port and token of the server will be given in file jupyter_lab.log
(created in current directory).
That is, wait until you see a line like the following in jupyter_lab.log
, and paste address in your browser:
Or copy and paste one of these URLs:
http://node-10-4-1:53074/?token=ea9bba78eb0840154b45acfc90dc9395e66c8d6fbcb2d4be
If you are working remotely, you need to be connected via VPN or use SSH tunneling through the web proxy to access the node from your web browser. With the latter, you need to forward the port of the host that is running Jupyter Lab on to a port on your local machine. For the example above, open a new terminal session and do
ssh -L 53074:node-10-4-1.internal.sanger.ac.uk:53074 your_sanger_username@ssh.sanger.ac.uk
(note the addition of internal.sanger.ac.uk
), which forwards port 53074 on node-10-4-1 to port 53074 on your localhost, jumping through the SSH gateway. Then you can point your browser to address 127.0.0.1
:
http://127.0.0.1:53074/?token=ea9bba78eb0840154b45acfc90dc9395e66c8d6fbcb2d4be
2 R versions are currently available in the notebook: R 3.6.1 and R 4.0.0 .
You can add your own installed libraries (must be compatible with either R 3.6.1 or R 4.0.0) in an R notebook:
.libPaths(/lustre/path_to_installed_libraries)
library(your_library_name)
In Jupyter, you can open a terminal and try install packages with a --target
directory, e.g.
pip install pandas --target /lustre/path_to_new_pip_libraries
and then in a python notebook:
import sys
sys.path.append('/lustre/path_to_new_pip_libraries')
import pandas
If that doesn’t work for your package because of conda conflicts, contact HGI: you could clone the complete jupyter conda environment and reference the new one in the start script, so that you can alter all conda/pip packages yourself.
ssh farm5-login
gn5@farm5-head2:~$ /software/hgi/installs/anaconda3/bin/conda init bash
<now you must log out and into farm5 again>
<check that conda activate works:>
gn5@farm5-head2:~$ conda activate hgi_base