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- See all B-jet calibration meetings on Indico
- Framework experience (Prajita, July 6)
- Plans (Prajita, July 13)
- What needs to be added to JETM2 (August 17)
SDF preliminaries
An environment needs to be created to ensure all packages are available. We have explored some options for doing this.
Option 1: stealing instance built for SSI 2023. This installs most useful packages but uses python 3.6, which leads to issues with h5py.
Starting Jupyter sessions via SDF web interface
- SDF web interface > My Interactive Sessions > Services > Jupyter (starts a server via SLURM)
- Jupyter Instance > slac-ml/SSAI (using the Jupyter instance developed for SSI 2023)
Option 2: create your own conda environment. Follow the SDF docs to use ATLAS group installation of conda.
There is also a slight hiccup with permissions in the folder /sdf/group/atlas/sw/conda/pkgs, which one can sidestep by specifying their own folder for saving packages (in GPFS data space).
The TLDR is:
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export PATH="/sdf/group/atlas/sw/conda/bin:$PATH"
conda init # the previous will be added to your bashrc file
# Add the following lines to ~/.condarc file (create default file with conda config)
pkgs_dirs:
- /gpfs/slac/atlas/fs1/d/<user>/conda_envs
conda env create -f bjr_v01.yaml # for example(bjr_v01) conda install jupyter |
This env can be activated when starting a kernel in Jupyter by adding the following under Custom Conda Environment:
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export CONDA_PREFIX=/sdf/group/atlas/sw/conda
export PATH=${CONDA_PREFIX}/bin/:$PATH
source ${CONDA_PREFIX}/etc/profile.d/conda.sh
conda env list
conda activate bjr_v01 |
Producing H5 samples
We are using a custom fork of dataset-dumper, developed for producing h5 files for NN training based on FTAG derivations. The custom fork is modified to store the truth jet pT via AntiKt4TruthDressedWZJets container.
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