Best to run in a clean conda env

# get analysis code and install as package
git clone git@github.com:andrzejnovak/boostedhiggs.git
cd boostedhiggs
pip install -e . 
cd ..

# get runner code
git clone git@github.com:andrzejnovak/nanocc.git
cd nanocc

# initiate proxy e.g
# voms-proxy-init --voms cms:/cms/dcms --valid 168:00  --vomses ~/.grid-security/vomses/

Test run

Use --executor iterative for single process to debug, --executor futures for local multiprocessing.

python runner.py --id test17 --json metadata/v2x17.json --year 2017 --limit 1 --chunk 5000 --max 2 --executor futures -j 5 

Test scale out

Scale-out will be dependent on the cluster setup. If the cluster is sufficiently permissive the below might run right away, otherwise some editing of runner.py and HighThroughputExecutor when using --executor parsl will be necessary. Analogously for --executor dask

python runner.py --id test17 --json metadata/v2x17.json --year 2017 --limit 1 --chunk 5000 --max 2 --executor parsl

Full scale

Removing test limiters...

python runner.py --id test17 --json metadata/v2x17.json --year 2017 --executor parsl