content: experiment_name = "attunet_july_2023"  # configuration name
  base_dir = '/exports/csce/eddie/eng/groups/DunnGroup/matthew/models_gelgenie'
  
  [processing]
  base_hardware = "EDDIE"   # Where the program is run [EDDIE/PC]
  device = "GPU"    # Which processor to use [GPU/CPU]
  pe = 1          # How many parallel environments (cores) have been requested alongside this job
  memory = 64     # Required memory per core in GBytes
  
  [data]
  n_channels = 1 # channels to output to model
  batch_size = 8  # Batch size for dataloader
  num_workers = 1 # parallel threads to use to speed up data processing
  
  dir_train_mask = ['/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/lsdb_gels/masks',
      '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/nathan_gels/masks',
      '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/matthew_gels/masks']
  
  dir_train_img = ['/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/lsdb_gels/images',
      '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/nathan_gels/images',
      '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/matthew_gels/images']
  
  dir_val_img = '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/neb_ladders/images'
  dir_val_mask = '/exports/csce/eddie/eng/groups/DunnGroup/matthew/gel_data/neb_ladders/masks'
  
  split_training_dataset = false
  apply_augmentations = true
  padding = true # pads all image with zeros to same size to allow for a batch size higher than 1
  
  [model]
  model_name = 'monai_attunet'   # model architecture name
  classes = 2     # Number of possible segmentation classes
  [training]
  loss = 'both'
  lr = 1e-4       # learning rate
  epochs = 400     # Number of epochs to run
  grad_scaler = true     # Use mixed precision for faster training
  load_checkpoint = false    # Load model from a .pth file (Bool/epoch ID)
  optimizer_type = 'adam' # Use adam optimizer
  save_checkpoint = true # Whether model checkpoints are saved
  checkpoint_frequency = 1 # How often checkpoints are saved (per epoch)
  model_cleanup_frequency = 20 # How often old checkpoints are deleted (in epochs)
  wandb_track = true # Whether to track training with wandb
  