config_finetune.yaml

batch_size: 128 # batch size
epochs: 30 # total number of epochs
eval_every_n_epochs: 1 # validation frequency
fine_tune_from: pretrained_agile_60k # sub directory of pre-trained model in ./ckpt
log_every_n_steps: 5 # print training log frequency
fp16_precision: False # float precision 16 (i.e. True/False)
init_lr: 0.0005 # initial learning rate for the prediction head
init_base_lr: 0.0001 # initial learning rate for the base GNN encoder
weight_decay: 1e-6 # weight decay of Adam
gpu: cuda:0 # training GPU
task_name: lnp_hela_with_feat # name of fine-tuning benchmark, inlcuding


model:
  num_layer: 5 # number of graph conv layers
  emb_dim: 300 # embedding dimension in graph conv layers
  feat_dim: 512 # output feature dimention
  drop_ratio: 0.3 # dropout ratio
  pool: mean # readout pooling (i.e., mean/max/add)

dataset:
  num_workers: 4 # dataloader number of workers
  valid_size: 0.1 # ratio of validation data
  test_size: 0.1 # ratio of test data
  splitting: scaffold # data splitting (i.e., random/scaffold)