# lightning.pytorch==2.0.7
seed_everything: 1019
trainer:
  accelerator: auto
  strategy: auto
  devices: 4
  num_nodes: 1
  precision: 16
  logger: logger.yaml
  callbacks: null
  fast_dev_run: false
  max_epochs: 30
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: null
  limit_val_batches: 10
  limit_test_batches: null
  limit_predict_batches: null
  overfit_batches: 0.0
  val_check_interval: 1.0
  check_val_every_n_epoch: 1
  num_sanity_val_steps: null
  log_every_n_steps: 10
  enable_checkpointing: null
  enable_progress_bar: null
  enable_model_summary: null
  accumulate_grad_batches: 1
  gradient_clip_val: 0.1
  gradient_clip_algorithm: norm
  deterministic: true
  benchmark: null
  inference_mode: true
  use_distributed_sampler: true
  profiler: null
  detect_anomaly: false
  barebones: false
  plugins: null
  sync_batchnorm: false
  reload_dataloaders_every_n_epochs: 0
  default_root_dir: "./logs/"
early_stop:
  monitor: "valid/FFPP/auc"
  min_delta: 0.0
  patience: 10
  verbose: false
  mode: max
  strict: true
  check_finite: true
  stopping_threshold: null
  divergence_threshold: null
  check_on_train_epoch_end: null
  log_rank_zero_only: false
checkpoint:
  dirpath: null
  filename: null
  monitor: "valid/FFPP/auc"
  verbose: false
  save_last: true
  save_top_k: 1
  save_weights_only: false
  mode: max
  auto_insert_metric_name: true
  every_n_train_steps: null
  train_time_interval: null
  every_n_epochs: null
  save_on_train_epoch_end: null
lr_monitor:
  logging_interval: epoch
  log_momentum: false
progress_bar:
  refresh_rate: 1
  leave: false
  theme:
    description: '#8250E5'
    progress_bar: '#7FFF00'
    progress_bar_finished: '#7FFF00'
    progress_bar_pulse: '#7FFF00'
    batch_progress: '#5398FE'
    time: grey54
    processing_speed: grey70
    metrics: white
  console_kwargs: null
optimizer:
  lr: 0.0001
  betas:
  - 0.9
  - 0.999
  eps: 1.0e-08
  weight_decay: 0.001
  amsgrad: false
  maximize: false
  foreach: null
  differentiable: false
lr_scheduler:
  start_factor: 1.0
  end_factor: 1.0
  total_iters: 1
  last_epoch: -1
  verbose: false
data: data.yaml