config/base_train.yaml

# @package _global_

# specify here default configuration
# order of defaults determines the order in which configs override each other
defaults:
  - data: pdb
  - model/architecture: foldingdit_100M
  - model/sampler: euler_maruyama
  - model/processor: protein_processor
  - model: simplefold
  - callbacks: default
  - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
  - trainer: default
  - paths: default
  - extras: default
  - hydra: default
  - _self_

  # experiment configs allow for version control of specific hyperparameters
  # e.g. best hyperparameters for given model and datamodule
  - experiment: null

# set False to skip model training
train: True

# evaluate on test set, using best model weights achieved during training
# lightning chooses best weights based on the metric specified in checkpoint callback
test: False

# seed for random number generators in pytorch, numpy and python.random
seed: null