# lightning.pytorch==2.5.5
seed_everything: 1999
trainer:
  accelerator: gpu
  strategy: auto
  devices: 1
  num_nodes: 1
  precision: null
  logger:
      - class_path: lightning.pytorch.loggers.TensorBoardLogger
        init_args:
          save_dir: lightning_logs
          name: "pointnet"
          version: null
  callbacks:
    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
      init_args:
        monitor: val/loss
        mode: min
        save_top_k: 1
        filename: best-checkpoint
    - class_path: lightning.pytorch.callbacks.EarlyStopping
      init_args:
        monitor: val/loss
        mode: min
        patience: 10
        verbose: false
  max_epochs: 200
  min_epochs: null
  max_steps: -1
  min_steps: null
  overfit_batches: 0.0
  log_every_n_steps: null
  inference_mode: true
  default_root_dir: null
  accumulate_grad_batches: 2
  gradient_clip_val: 1.0
model: 
  class_path: ThermalSolver.point_module.PointSolver
  init_args:
    model_class_path: ThermalSolver.model.point_net.PointNet
    model_init_args:
      input_dim: 4
      output_dim: 1
data: 
  class_path: ThermalSolver.point_datamodule.PointDataModule
  init_args:
    hf_repo: "SISSAmathLab/thermal-conduction"
    split_name: "2000"
    batch_size: 10
    train_size: 0.8
    test_size: 0.1
    test_size: 0.1
optimizer: null
lr_scheduler: null
# ckpt_path: lightning_logs/pointnet/version_0/checkpoints/best-checkpoint.ckpt