NTuple framework for DeepFlavour
cmsrel CMSSW_8_0_25
cd CMSSW_8_0_25/src/
cmsenv
git cms-init
git clone https://github.com/CMSDeepFlavour/DeepNTuples
# Add JetToolBox
cd DeepNTuples
git submodule init
git submodule update
# Add DeepFlavour -- To be updated once the 80X PR is done
cd -
git cms-merge-topic -u mverzett:DeepFlavour-from-CMSSW_8_0_21
mkdir RecoBTag/DeepFlavour/data/
cd RecoBTag/DeepFlavour/data/
wget http://home.fnal.gov/~verzetti//DeepFlavour/training/DeepFlavourNoSL.json
cd -
#compile
scram b -j 4
cmsrel CMSSW_8_1_0
cd CMSSW_8_1_0/src/
cmsenv
git cms-init
# Add DeepFlavour -- To be updated once the 80X PR is done
git cms-merge-topic -u cms-btv-pog:DeepFlavour-from-CMSSW_8_1_0
git clone https://github.com/CMSDeepFlavour/DeepNTuples
# Add JetToolBox
cd DeepNTuples
git submodule init
git submodule update
#compile
scram b -j 4
cmsrel CMSSW_8_4_0
cd CMSSW_8_4_0/src/
cmsenv
git cms-init
git clone https://github.com/CMSDeepFlavour/DeepNTuples
# Add JetToolBox
cd DeepNTuples
git submodule init
git submodule update
#DeepCSV is already in the release, but with different names, which will become the defaults in the close future
sed -i 's|deepFlavourJetTags|pfDeepCSVJetTags|g' DeepNtuplizer/production/DeepNtuplizer.py
#compile
scram b -j 4
cmsrel CMSSW_10_0_1
cd CMSSW_10_0_1/src/
cmsenv
git cms-init
git clone https://github.com/CMSDeepFlavour/DeepNTuples
cd DeepNTuples
git checkout 94X
# Add JetToolBox
git submodule init
git submodule update
#compile
scram b -j 4
It is important to create your grid proxy in a location that is accessible by other nodes (there is no security issue, your full credentials are still needed for access). For this purpose, redirect the grid proxy location by adding the following to your login script:
export X509_USER_PROXY=${HOME}/.gridproxy.pem
Before doing a batch submission you can test the ntuplizer locally in the production directory with:
cmsRun DeepNtuplizer.py inputFiles=/path/to/file.root
The jobs can be submitted using the following syntax
jobSub.py --file <sample file> DeepNtuplizer.py <batch directory> --outpath /path/to/output/directory/
For an example of sample files, please refer to the .cfg files already in the production directory. You first specify the number of jobs to be submitted, then the input dataset name, which should then be followed by the name of the output. Other arguments such as gluonReduction can then be specified if needed. Each argument need to be separted by at least two whitespaces.
The large job output (root files) will NOT be stored in the batch directory. The storage directory is specified by the --outpath argument. The batch directory will contain a symlink to this directory. If the outpath is not specified the ntuples are stored in the deepjet directory, where you need write permission.
The status of the jobs can be checked with
cd <batch directory>
check.py <sample subdirectories to be checked>
The check.py script provides additional options to resubmit failed jobs or to create sample lists in case a satisfying fraction of jobs ended successfully. In this case do:
check.py <sample subdirectories to be checked> --action filelist
This will create file lists that can be further processed by the DeepJet framework For resubmitting failed jobs, do:
check.py <sample subdirectories to be checked> --action resubmit
When the file lists are created, the part used for training of the ttbar and QCD samples (or in principle any other process) can be merged using the executable:
mergeSamples.py <no of jets per file> <output dir> <file lists 1> <file lists 2> <file lists 3> ...
For example:
mergeSamples.py 400000 /path/to/dir/merged ntuple_*/train_val_samples.txt
This will take a significant amount of time - likely more than the ntuple production itself. It is therefore recommended to run the command within 'screen'. In the 94X branch you can also submit via batch by doing --batch. This will create a batch directory in the folder the command is called from.
mergeSamples.py 400000 /path/to/dir/merged ntuple_*/train_val_samples.txt --batch
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