# 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.WandbLogger
      init_args:
        save_dir: logs.autoregressive.wandb
        project: "thermal-conduction-unsteady"
        name: "standard"
  callbacks:
    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
      init_args:
        dirpath: logs.autoregressive.wandb/standard/checkpoints
        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: 1000
  min_epochs: null
  max_steps: -1
  min_steps: null
  overfit_batches: 0.0
  log_every_n_steps: null
  accumulate_grad_batches: 1
  default_root_dir: null

model: 
  class_path: ThermalSolver.autoregressive_module.GraphSolver
  init_args:
    model_class_path: ThermalSolver.model.diffusion_net.DiffusionNet
    model_init_args: 
      input_dim: 1
      hidden_dim: 8
      output_dim: 1
      n_layers: 8
    unrolling_steps: 5

data: 
  class_path: ThermalSolver.graph_datamodule_unsteady.GraphDataModule
  init_args:
    hf_repo: "SISSAmathLab/thermal-conduction-unsteady"
    split_name: "100_samples_easy_refined"
    batch_size: 32
    train_size: 0.7
    val_size: 0.2
    test_size: 0.1
    build_radial_graph: false
    remove_boundary_edges: true
    start_unrolling_steps: 5
optimizer: null
lr_scheduler: null