- 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
- At RWTH it should be at your
- Create a dedicated environment for
PocketCoffea
, install the packages, and compile: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 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 .
For brux20 cluster at Brown, you may needconda install conda-forge::xrootd conda install conda-forge::lightgbm conda install conda-forge::tensorflow conda install pytorch::pytorch conda install setuptools==70.*
conda install conda-forge::ca-certificates
. - Checkout this repo:
git clone [email protected]:cms-rwth/VHccPoCo.git
- (If your local username is different from your CERN username) Setup your CERN username variable:
export CERN_USERNAME="YOURUSERNAME"
- Follow examples to create dataset input files. First activate voms proxy. Then:
Use
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 ../
-p 12
withbuild-datasets
to parallelizing with 12 cores, e.g. - 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
- 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
- Make some plots:
The plot parameters can be changed by editing
make-plots -inp output_VHcc_v01 -op VHccPoCo/params/plotting.yaml
VHccPoCo/params/plotting.yaml
.
-
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