To create the environment you can do:

conda create -f environment.yml

To do a test run do:

conda activate skim-test-env
python skim.py -d GJets -t # Run on a small subset

In test mode, the default is to run over 3 files per 2 datasets with 2 steps of 50 events per file. These parameters can be modified to scale up.

To run on the full GJets datasets (~500GB) with DaskVine do:

python skim.py -d GJets -dv

This will by default cache the preprocessing step.

If wanted you can run over all the data (~2.5TB) instead by passing --do_all instead of -d dataset_tag.

To package the environment do:

conda activate skim-test-env
poncho_package_create $CONDA_PREFIX skim-test-env.tar.gz

Start the factory with:

vine_factory -T condor -C factory.json --python-env skim-test-env.tar.gz --scratch-dir=/tmp/vine-factory-$uid

To run interactively with one worker do:

vine_worker -d all --cores 4 --memory 36000 -M triphoton-manager

This will output the logs to the /tmp directory of the machine the worker is running on.

To make the main plots do:

vine_graph_log -T png <PERFORMANCE_LOG_PATH>

To make the disk accumulation plots do:

python vine_plot_compose.py <TRANSACTION_LOGP_PATH> --worker-cache --sublabels --out <OUTPUT_FILENAME>