includes model setup, training and post-processing
  • general:
    • introduced modules for model setup, training and post-processing
  • new features:
    • model setup: create model with all parameters, plot architectur
    • model class: collects model and loss, more general workflow is possible now
    • training: train model (not distributed on multiple CPUs/GPUs), monitor error and learning rate with plots and file output
    • postprocessing: evaluate trained network, create linear fit and persistence forecast, plot error metrics (not all plots are finished yet)
    • Distributor splits data into mini batches
    • create a dictionary with requested entries from data store
    • enhanced data loading from join interface
  • technical:
    • modules to run in the experiment pipeline can be found now in run_modules (before modules)
    • test teardown method for better test independence
    • updated requirements.txt
    • data store function to store data is renamed from put to set
    • new time display format for TimeTracking
    • more tests and documentation