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apply_training.yaml
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apply_training.yaml
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# will update baseline training cfg (retrieved from run artifacts) with these parameters
training_cfg_upd:
Setup:
n_tau: 250
dataloader_core: "TauMLTools/Training/interface/DataLoader_main.h"
debug: False
include_mismatched: True
SetupNN:
n_batches: -1
n_batches_val: -1
validation_split: 0.
max_queue_size: 1
n_load_workers: 1
# mlflow info
path_to_mlflow: ???
experiment_id: ???
run_id: ???
# training/scaling cfg to init DataLoader class
path_to_training_cfg: ${path_to_mlflow}/${experiment_id}/${run_id}/artifacts/input_cfg/training_cfg.yaml
scaling_cfg: ${path_to_mlflow}/${experiment_id}/${run_id}/artifacts/input_cfg/ShuffleMergeSpectral_trainingSamples-2_files_0_498.json
# input path and file name
path_to_input_dir: ???
input_filename: null # without file extension
prediction_overwrite: True
# output path and file name // will store prediction file in -> artifacts/predictions/{sample_alias}/{output_filename}.h5
sample_alias: ???
output_filename: ${input_filename}_pred
# gpu setup
gpu_cfg: # for running on CPU specify "gpu_cfg: null"
gpu_mem : 7 # in Gb
gpu_index: 0
# misc.
verbose: True
checkout_train_repo: False # whether to checkout git commit used for running the training (fetched from artifacts)
#
# Options below are needed to DeepTauId input/output for CMSSW vs. local python interface
#
# "toKeep" - option to save the default DeepTauId information from initial BigTuples
toKeepID : null
# toKeepID : [
# "byDeepTau2018v2p5VSeraw",
# "byDeepTau2018v2p5VSmuraw",
# "byDeepTau2018v2p5VSjetraw",
# ]
# when save_input_names is specified - NN input tensors will be saved
# save_input_names is the naming of the saved tensors:
save_input_names : null
# save_input_names : [
# "input_tau",
# "input_inner_egamma",
# "input_inner_muon",
# "input_inner_hadrons",
# "input_outer_egamma",
# "input_outer_muon",
# "input_outer_hadrons",
# ]