GridIn: tools for running over grid

Please note:

• This guide suppose that the framework is already installed. See https://github.com/cp3-llbb/Framework for detailed instructions
• This guide also installs our local database SAMADhi
• You probably want to have the access to this database: ask around!
• This guide also installs Datasets, our repo to list the datasets to be ran on
• The utilities in the scripts folders are copied to CMSSW/bin during the scram b, so if these utilities have been modified you need to rebuild in order to have them in your PATH

First time setup

source /nfs/soft/grid/ui_sl6/setup/grid-env.sh
source /cvmfs/cms.cern.ch/cmsset_default.sh
source /cvmfs/cms.cern.ch/crab3/crab.sh

cd <path_to_CMSSW>
cmsenv

cd ${CMSSW_BASE}/src
git clone -o upstream [email protected]:cp3-llbb/GridIn.git cp3_llbb/GridIn
git clone -o upstream [email protected]:cp3-llbb/SAMADhi.git cp3_llbb/SAMADhi
git clone -o upstream [email protected]:cp3-llbb/Datasets.git cp3_llbb/Datasets

scram b -j 4
cd ${CMSSW_BASE}/src/cp3_llbb/GridIn
source first_setup.sh

How-to

The script you'll be working with is runOnGrid.py, from the scripts folder. During the first build, this script is copied by CMSSW into the global scripts directory, which is inside PATH; you can thus access it from anywhere in the source tree

In order to run on the grid, you need 3 things:

• First, an analyzer for the framework
• A configuration file for this analyzer
• A set of JSON files describing the datasets you want to run on

The first two points must be handled by you. For the last point, a set of JSON files for the commonly used datasets are already included (see inside test/datasets). The structure of the JSON file is described below

You can now run on the grid. Go to the test folder, and run

runOnGrid.py -c <Your_Configuration_File> --mc datasets/mc_TT.json datasets/mc_DY.json <datasets/...>

<Your_Configuration_File> must be substituted by the name of the configuration file, including the .py extension. You should now have a new file inside the working directory, named crab_TTJets_TuneCUETP8M1_amcatnloFXFX_25ns.py. This file is a configuration file for crab3. A file is created automatically for each dataset specified when running runOnGrid.py.

Note: By default, runOnGrid.py does not submit any jobs to the grid, it only creates the necessary files for crab. If you want to automatically submit the jobs, you can add the --submit flag when running runOnGrid.py (does not seems to work for the moment due to a crab bug).

To manually launch the jobs, use the crab submit <crab_python_file>. All the submitted tasks are stored inside the tasks folder.

Book-keeping

If the job has completed successfully, you can run

runPostCrab.py <myCrabConfigFile.py>

This will gather the needed information (number of events, code version, source dataset, ...) and insert the sample (and possibly the parent dataset if missing) in the database

JSON file format

Each dataset is stored inside a JSON file, containing at least the dataset pretty name, its path as well as the number of units per job. The meaning of units depends on the type of dataset: for data, a unit is a luminosity section. For MC, a unit is a file.

An example of JSON file is given below:

{
  "/TTJets_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISpring15DR74-Asympt25ns_MCRUN2_74_V9-v1/MINIAODSIM": {
    "name": "TTJets_TuneCUETP8M1_amcatnloFXFX_25ns",
    "units_per_job": 15
  }
}

It can contains any number of datasets, but by convention, only datasets belonging into the same group should be into the same file (for example, it's fine to have one file for exclusive DY datasets, but not one file for all the different TT samples). The root node must be a dictionary, where the key is the dataset path, and values are:

• name: The pretty name of the dataset. This name is used to format the task name and the output path
• units_per_job: For MC, the number of files processed by each job. For data, the number of luminosity section processed by each job.

For a data JSON file, an additional value is mandatory:

• run_range: must be an array with two entries, like [1, 30], defining the range of validity of the dataset

An optional value, but highly recommended is:

• certified_lumi_file: the path (filename or url) of the golden JSON file containing certified luminosity section. If not present, a default file will be used, presumably outdated by the time you'll run.