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HGCalML

Requirements

  • DeepJetCore 3.X (https://github.com/DL4Jets/DeepJetCore)
  • DeepJetCore 3.X container (or latest version in general)

For CERN, a script to start the latest container in interactive mode can be found here:

/eos/home-j/jkiesele/singularity/run_deepjetcore3.sh

Setup

git clone  --recurse-submodules  https://github.com/cms-pepr/HGCalML
cd HGCalML
source env.sh #every time
./setup.sh #just once

When developing custom CUDA kernels

The kernels are located in modules/compiled The naming scheme is obvious and must be followed. Compile with make.

Converting the data from ntuples

convertFromSource.py -i <text file listing all training input files> -o <output dir> -c TrainData_window_onlytruth

This data structure removes all noise and not correctly assigned truth showers until we have a better handle on the truth. Once we do, we can use TrainData_window which does not remove noise

Standard training and inference

Go to the Train folder and then use the following command to start training. The file has code for running plots and more. That can be adapted according to needs.

cd Train

python3 june_format_example_nf_pca_double_coords.py /mnt/ceph/users/sqasim/Datasets/hgcal_kenneth_test_april_20_prop/dataCollection.djcdc /mnt/ceph/users/sqasim/trainings/training_folder

After training the model for a while, navigate to scripts directory and do the inference. Please note that this is different from the standard DeepJetCore procedure.

predict_hgcal.py /mnt/ceph/users/sqasim/trainings/training_folder/KERAS_check_model_last_save/ /mnt/ceph/users/sqasim/Datasets/hgcal_kenneth_test_april_20_prop/dataCollection.djcdc /mnt/ceph/users/sqasim/Datasets/hgcal_kenneth_test_april_20_prop/test_files.txt /mnt/ceph/users/sqasim/trainings/training_folder/inference