tcztzy / bsub_jupyter_lab Goto Github PK

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

    Or copy and paste one of these URLs:
        http://node-10-4-1:53074/?token=ea9bba78eb0840154b45acfc90dc9395e66c8d6fbcb2d4be

.libPaths(/lustre/path_to_installed_libraries)
library(your_library_name)

# set local/personal libs directory, e.g.:
R_version = paste0(as.character(R.version['major']),
       '.', as.character(R.version['minor']))
local_libs = paste0('~/personal_rlibs_R_',R_version) # can be changed to any new dir in your home dir or project area

# create directory if doesn't exist already:
dir.create(local_libs, showWarnings = FALSE)
# tell R to use this libs directory
.libPaths(local_libs)
# Install BiocManager if not done already:
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager", lib=local_libs, repos='http://cran.us.r-project.org')
BiocManager::install(lib=local_libs)

# now R is ready to install packages in local_libs
# example install of a package in local_libs directory via Bioconductor:
BiocManager::install('purrr', lib=local_libs)

# example install of a package in local_libs directory without Bioconductor:
install.packages("anocva", lib=local_libs, repos='http://cran.us.r-project.org')

# test that libraries are there and loaded correctly
library(purrr)
library(anocva)
# check that local_libs directory indeed has `purrr` and `anocva` dirs:
list.files(local_libs)

# in future sessions, don't forget to run .libPaths(local_libs) so that R can load these libraries.

    import sys
    sys.path.append('/lustre/path_to_new_pip_libraries')
    import pandas

ssh -L2222:farm5-login.internal.sanger.ac.uk:22 [email protected] 

rsync -rvz -e 'ssh -p 2222' --progress local_file_to_copy your_username@localhost:/lustre/path_where_you_want_the_files_to_go/

conda create --prefix /lustre/path_to_your_new_env/jupyterlab_env -c conda-forge jupyterlab

 
# full setup of your own Jupiter lab + R kernel environment:
 
# choose a path you have access to (/lustre/somewhere or preferably /software/somewhere if you can):
export INSTALL_DIR=/lustre/scratch118/humgen/hgi/users/mercury/test
 
# first, clone jupyter repo in INSTALL_DIR (this is important later on to setup the new R kernel config):
cd $INSTALL_DIR
git clone https://github.com/wtsi-hgi/bsub_jupyter_lab.git
 
# now, install your own jupyterlab conda environment, also in INSTALL_DIR:
conda create --prefix $INSTALL_DIR/jupyterlab_env -c conda-forge jupyterlab
 
# check that environment can be loaded ok:
conda activate $INSTALL_DIR/jupyterlab_env
 
#  now, you need to add your own R jupyter kernel in that new conda env:
# first, add R to the conda env:
conda install -c conda-forge r-base # this should instal R version 4 (let me know if you wanted a different version here).
# next add R kernel and tell jupyter lab configuration about it:
# check which R will be used by default
which R # in my case it will say “/lustre/scratch118/humgen/hgi/users/mercury/test/jupyterlab_env/bin/R” which is good because I want it to be in from my INSTALL_DIR
# go into R
R
# check that R default library path is in INSTALL_DIR
.libPaths() # in my case it will say [1] "/lustre/scratch118/humgen/hgi/users/mercury/test/jupyterlab_env/lib/R/library"
# now install the IRkernel package in that default location
install.packages('IRkernel')
IRkernel::installspec(displayname = 'Rperso')  # to register the kernel in the current R installation
# That command will install the R kernel in your home dir
# in my case, it said [InstallKernelSpec] Installed kernelspec ir in /nfs/users/nfs_m/mercury/.local/share/jupyter/kernels/ir
 
# now exit R, and copy that kernel config from your home directory to the bsub_jupyter_lab directory you just cloned:
cp -r ~/.local/share/jupyter/kernels/ir $INSTALL_DIR/bsub_jupyter_lab/kernels/
 
# check that you can use your new conda env via jupyter notebook, and that it sees your personal R kernel:
cd bsub_jupyter_lab
./bsub_jupyter_lab.sh -g hgi -c 4 -m 50000 -q normal -e $INSTALL_DIR/jupyterlab_env
# notice I used the -e option, and please replace -g with our own farm5 LSF group (not ‘hgi’).
# open your browser to the jupyter lab URL, and check that you see R kernel 'Rperso'
 
# You should see the “Rperso” kernel available in the list of kernels, and R command .libPaths() should point to the R library path in your new conda env.
 
# now you can install any other library in your environment!
# also, if you need rstudio shortcuts, you will need: jupyter labextension install @techrah/text-shortcuts

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