defaults.yaml

config_file: null  # This lets the user pass a config filename to load other arguments from

program:  # These are the arguments that define how the train.py script works
  seed: 1337
  output_dir: output
  data_dir: data
  log_dir: logs
  overwrite: False

experiment: # These are arugments specific to the experiment we are running
  name: ???  # this is the name given to this experiment run
  task: ???  # this is the type of task to use for this experiement (e.g. "landcoverai")
  module: # these will be passed as kwargs to the LightningModule assosciated with the task
    learning_rate: 1e-3
  datamodule: # these will be passed as kwargs to the LightningDataModule assosciated with the task
    root_dir: ${program.data_dir}
    seed: ${program.seed}
    batch_size: 32
    num_workers: 4


# The values here are taken from the defaults here https://pytorch-lightning.readthedocs.io/en/1.3.8/common/trainer.html#init
# this probably should be made into a schema, e.g. as shown https://omegaconf.readthedocs.io/en/2.0_branch/structured_config.html#merging-with-other-configs
trainer: # These are the parameters passed to the pytorch lightning Trainer object
  logger: True
  checkpoint_callback: True
  callbacks: null
  default_root_dir: null
  gradient_clip_val: 0.0
  gradient_clip_algorithm: 'norm'
  process_position: 0
  num_nodes: 1
  num_processes: 1
  gpus: null
  auto_select_gpus: False
  tpu_cores: null
  log_gpu_memory: null
  progress_bar_refresh_rate: null
  overfit_batches: 0.0
  track_grad_norm: -1
  check_val_every_n_epoch: 1
  fast_dev_run: False
  accumulate_grad_batches: 1
  max_epochs: null
  min_epochs: null
  max_steps: null
  min_steps: null
  max_time: null
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  limit_predict_batches: 1.0
  val_check_interval: 1.0
  flush_logs_every_n_steps: 100
  log_every_n_steps: 50
  accelerator: null
  sync_batchnorm: False
  precision: 32
  weights_summary: 'top'
  weights_save_path: null
  num_sanity_val_steps: 2
  resume_from_checkpoint: null
  profiler: null
  benchmark: False
  deterministic: False
  reload_dataloaders_every_epoch: False
  auto_lr_find: False
  replace_sampler_ddp: True
  terminate_on_nan: False
  auto_scale_batch_size: False
  prepare_data_per_node: True
  plugins: null
  amp_backend: 'native'
  move_metrics_to_cpu: False
  multiple_trainloader_mode: 'max_size_cycle'
  stochastic_weight_avg: False