Running DeepReg Remotely (on the Cluster)

This tutorial gives an example of how to run DeepReg remotely (e.g. on a cluster). Our example is specific to the UCL cluster, which has the operating system CentOS 7 (similar to Ubuntu), with job scheduler Sun Grid Engine (SGE). More information on the specific configuration at UCL is available here.

Installing the Environment

Below is the script to install the environment for DeepReg in the cluster. If you want to switch to the current working branch. Call git branch origin/<branch_name> after downloading DeepReg.

git clone<personal_acount_id>/DeepReg.git
module load default/python/3.8.5

cd <DeepReg_Dir>

export PATH=/share/apps/anaconda3-5/bin:$PATH
conda env create -f environment.yml   #set up environment
source /share/apps/source_files/cuda/cuda-10.1.source    # set up cuda for GPU
source activate deepreg   # activate conda env

export CONDA_PIP="/home/<cs_account_id>/.conda/envs/deepreg/bin/pip"
$CONDA_PIP install -e .

module is a command to find packages and set up them in your own storge. You can get more information by module help. Another way to install packages is

export PATH=/share/apps/<module_name>/bin:$PATH

You can find any available packages in cluster nodes by

ls /share/apps/ | grep '<package_name>*'

Tip: For now, all the packages are stored in share/apps/. If the path does not exist, try module load default/python/3.8.5. Then, call $PATH to find the new location of packages.

Example Script

Below is the submission script for running quick start example here. Change <DeepReg_dir> in the script to the remote DeepReg repo location and save the below code in a <your_name>.qsub. Submit the job with qsub <your_name>.qsub and check the status of the job with qstat, the saved stdout and stderr is in home/<cs_account_id>/logs/.

#$ -S /bin/bash   # bash for job
#$ -l gpu=true   # use gpu
#$ -l tmem=10G   # virtual mem used
#$ -l h_rt=36:0:0   # max job runtime hour:min:sec
#$ -N DeepReg_tst   # job name
#$ -wd home/<cs_account_id>/logs   # output, error log dir.
#Please call `mkdir logs` before using the script.


cd ../<DeepReg_dir>
export PATH=/share/apps/anaconda3-5/bin:$PATH
source activate deepreg   # activate conda env
export PATH=/share/apps/cuda-10.1/bin:/share/apps/gcc-8.3/bin:$PATH   # path for cuda, gcc
export LD_LIBRARY_PATH=/share/apps/cuda-10.1/lib64:/share/apps/gcc-8.3/lib64:$LD_LIBRARY_PATH   # path for cuda, gcc

deepreg_train \
--gpu \
--config_path config/unpaired_labeled_ddf.yaml \
--log_dir test

You also can directly access one of four cluster nodes reserved for development purposes, by the command below. You can then run your code via the command line. More information on the specific configuration at UCL is available here.

qrsh -l tmem=14G,h_vmem=14G

Contact and Version

Please contact, for information about the cluster. The information here is likely to change as UCL updates the software on the cluster.