List of things to convert:

• neural nets
• augmentation
• data prep
• multichannel
• features?
• data_reading?
• scoring?

Instructions from proposal document:

1. Copy experiment xxx.cfg files to corresponding directory in recipes
2. Move [global] section to a yaml file (e.g. params.yaml), rename to constants:
3. Move each element of [functions] section to the yaml file

• Convert all = to :
• Remove final [\endtag]

4. Split functions: into saveables: and functions:
5. For most models (especially ones with replicate parts), move all model code to a model.py file. Define a new subclass of torch.nn.Module that takes all key model parameters (e.g. number of layers, etc.) and use these parameters to build the model.
6. Move all code in cfg hierarchy computation sections to an ‘experiment.py` python file
7. At top of experiment.py, instantiate an Experiment object and pass:

• Params file object
• Command line parameters (i.e. sys.argv[1:])

9. When execute_computations would be called, instead:

• Create a dataloader if necessary
• Add a loop to code if necessary