...
3. Change the path(s) in the following files
3.1) Highlighted line in "Job.slurm" file (please see below) with the correct path of 'your files'
3.2) Similarly update the paths on "Full_ML_fit_evaluation_Set2.py" file
Line numbers → 22, 31, 154
4. (This step is not necessary to check the code running, but to speed up the testing) For a quick test, you can change the "number of samples" to a small number to test (in other words "number of replicas") which is in line number 115: 'numSamples = 1000' 10' as an example. You can change this numSamples value to any number of replicas that you need.
5. Run the following commands on your terminal
$ module load anaconda/2020.11-py3.8
$ module load singularity/3.7.1
$ module load tensorflow/2.1.0-py37
$ cp $CONTAINERDIR/tensorflow-2.1.0-py37.sif /home/$USER
(make sure that you have the same module loads included in your Job.slurm file)
6. Run the following command
$ sbatch --array=0-14 2 Job.slurm
Note: Here 0-14 means the number of kinematic settings that you want to run in parallel (this is parallelization of local fits), and as a part of the output you will see Results#.csv (where # is an integer number) files which contain distributions of Compton Form Factors (CFFs) from each (individual) local fit.
7. After you submit your job:
* You can view your jobs using the web-browser (please see the following screen-shots)
* You can find commands to check the status of your job, cancel job(s), other commands related to handling jobs using .slurm file etc. using the following page
https://www.rc.virginia.edu/userinfo/rivanna/slurm/
8. At the end of your job, you will find several types of output files (please see the the following screenshot)
Results*.csv → These files contain CFFs distributions corresponding to each kinematic setting
best-netowrk*.hdf5 → These files are the 'best'/'optimum' neural-network files for each kinematic setting
result_*.out → These files contain the output while it's been running for each kinematic setting