config.yaml

# Training configurations for gRNAde_drop3d@0.75_maxlen@500.h5

# Misc configurations
device:
  value: 'gpu'
  desc: Device to run on (cpu/cuda/xpu)
gpu:
  value: 0
  desc: GPU ID
seed:
  value: 0
  desc: Random seed for reproducibility
save:
  value: True
  desc: Whether to save current and best model checkpoint

# Data configurations
data_path:
  value: "./data/"
  desc: Data directory (preprocessed and raw)
radius:
  value: 4.5
  desc: Radius for determining local neighborhoods in Angstrom (currently not used)
top_k:
  value: 32
  desc: Number of k-nearest neighbors in 3D and sequence space
num_rbf:
  value: 32
  desc: Number of radial basis functions to featurise distances
num_posenc:
  value: 32
  desc: Number of positional encodings to featurise edges
max_num_conformers:
  value: 1
  desc: Maximum number of conformations sampled per sequence
noise_scale:
  value: 0.1
  desc: Std of gaussian noise added to node coordinates during training
drop_prob_3d:
  value: 0.75
  desc: Dropout probability of 3D coordinates during training
random_order:
  value: True
  desc: Whether to train with random permutation or sequential order
max_nodes_batch:
  value: 3000
  desc: Maximum number of nodes in batch
max_nodes_sample:
  value: 500
  desc: Maximum number of nodes in batches with single samples (ie. maximum RNA length)

# Splitting configurations
split:
  value: 'das'
  desc: Type of data split (das/structsim_v2)

# Model configurations
model:
  value: 'gRNAde'
  desc: Model architecture
node_in_dim:
  value: [15, 4]  # (num_bb_atoms x 5, 2 + (num_bb_atoms - 1))
  desc: Input dimensions for node features (scalar channels, vector channels)
node_h_dim:
  value: [128, 16]
  desc: Hidden dimensions for node features (scalar channels, vector channels)
edge_in_dim:
  value: [132, 3]  # (num_bb_atoms x num_edge_type + num_rbf + num_posenc, num_bb_atoms)
  desc: Input dimensions for edge features (scalar channels, vector channels)
edge_h_dim:
  value: [64, 4]
  desc: Hidden dimensions for edge features (scalar channels, vector channels)
num_layers:
  value: 4
  desc: Number of layers for encoder/decoder
drop_rate:
  value: 0.5
  desc: Dropout rate
out_dim:
  value: 4
  desc: Output dimension (4 bases for RNA)

# Training configurations
epochs:
  value: 100
  desc: Number of training epochs
lr:
  value: 0.0001
  desc: Learning rate
label_smoothing:
  value: 0.05
  desc: Label smoothing for cross entropy loss
batch_size:
  value: 8
  desc: Batch size for dataloaders (currently not used)
num_workers:
  value: 16
  desc: Number of workers for dataloaders
val_every:
  value: 10
  desc: Interval of training epochs after which validation is performed

# Evaluation configurations
model_path:
  value: ''
  desc: Path to model checkpoint for evaluation or reloading
evaluate:
  value: False
  desc: Whether to run evaluation (or training)
n_samples:
  value: 16
  desc: Number of samples for evaluating recovery
temperature:
  value: 0.1
  desc: Sampling temperature for evaluating recovery