use_gpu: True
seed: 10
device: 1
early_stop:
  patience: 60
federate:
  mode: standalone
  total_round_num: 70
  client_num: 10
data:
  root: data/trafficflow/PeMS03
  type: trafficflow
  splitter: trafficflowprediction
  num_nodes: 358
  lag: 12
  horizon: 12
  val_ratio: 0.2
  test_ratio: 0.2
  tod: False
  normalizer: std
  column_wise: False
  default_graph: True
  add_time_in_day: True
  add_day_in_week: True
  steps_per_day: 288
  days_per_week: 7
dataloader:
  type: trafficflow
  batch_size: 64
  drop_last: True
model:
  type: FedDGCN
  task: TrafficFlowPrediction
  dropout: 0.1
  horizon: 12
  num_nodes: 0
  input_dim: 1
  output_dim: 1
  embed_dim: 10
  rnn_units: 64
  num_layers: 1
  cheb_order: 2
  use_day: True
  use_week: True
train:
  batch_or_epoch: 'epoch'
  local_update_steps: 1
  optimizer:
    type: 'Adam'
    lr: 0.003
    weight_decay: 0.0
  batch_size: 64
  epochs: 300
  lr_init: 0.003
  weight_decay: 0
  lr_decay: False
  lr_decay_rate: 0.3
  lr_decay_step: 5,20,40,70
  early_stop: False
  early_stop_patience: 15
  grad_norm: True
  max_grad_norm: 5
  real_value: True
criterion:
  type: RMSE
trainer:
  type: trafficflowtrainer
  log_dir: ./
grad:
  grad_clip: 5.0
eval:
  metrics: ['avg_loss']