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PocketCoffea VHcc setup

  1. Install Miniconda or Micromamba

    • At RWTH it should be at your /net/scratch_cms3a/<username> area
    • At lxplus CERN, it should be in your /eos/user/u/username area

  2. Create a dedicated environment for PocketCoffea, install the packages, and compile:

    conda create -n PocketCoffea python=3.10 -c conda-forge
conda activate PocketCoffea
# install PocketCoffea
git clone [email protected]:PocketCoffea/PocketCoffea.git
cd PocketCoffea
pip install -e .

    Follow their installation instructions for other options. Afterwards install additional packages needed for BDT/DNN training and evaluation. Keep using conda, since using pip might alter the environment, leading to conflicts.

    conda install conda-forge::xrootd
conda install conda-forge::lightgbm
conda install conda-forge::tensorflow
conda install pytorch::pytorch
conda install conda-forge:alive-progress
conda install conda-forge:optuna
conda install conda-forge:imblearn

    For brux20 cluster at Brown, you may need conda install conda-forge::ca-certificates.

  3. Checkout this repo:

    git clone [email protected]:cms-rwth/VHccPoCo.git

  4. (If your local username is different from your CERN username) Setup your CERN username variable:

    export CERN_USERNAME="YOURUSERNAME"

  5. Follow examples to create dataset input files. First activate voms proxy. Then:

    cd VHccPoCo
mkdir datasets
build-datasets --cfg samples_Run3.json -o -ws T2_DE_RWTH -ws T2_DE_DESY -ws T1_DE_KIT_Disk -ws T2_CH_CERN -ir
cd ../

    Use -p 12 with build-datasets to parallelizing with 12 cores.

  6. Run with the futures executor (test before large submission):

    runner --cfg VHccPoCo/cfg_VHcc_ZLL.py -o output_VHcc_Test --executor futures -s 10 -lf 1 -lc 1

  7. Run on condor with Parsl executor (only if the previous step was successeful):

    runner --cfg VHccPoCo/cfg_VHcc_ZLL.py -o output_VHcc_v01 --executor parsl-condor@RWTH -s 60

    Note: use dask@lxplus executor if running at CERN.

  8. Make some plots:

    make-plots -inp output_VHcc_v01 -op VHccPoCo/params/plotting.yaml

    The plot parameters can be changed by editing VHccPoCo/params/plotting.yaml.

  9. Produce shapes for limit setting with scripts/convertToRoot.py script.