`canari_ml train` CLI Help¶

Run the following command to get the help information for canari_ml train command:

$ canari_ml train --help

train is powered by Hydra.

== Configuration groups ==
Compose your configuration from those groups (group=option)

callbacks: default, early_stopping, model_checkpoint
common: default
hydra_config: predict, train
logger: csv, tensorboard, wandb
model: default, unet
paths: default, download, plot, postprocess, predict, preprocess, train
plot: default, ua700
postprocess: default, netcdf, plot_ua700
predict: default
preprocess: default
profiler: pytorch
train: default
trainer: default


== Config ==
Override anything in the config (foo.bar=value)

train:
  dataset: ???
  name: ???
  seed: 42
  epochs: 50
  workers: 4
  batch_size: 4
  shuffling: true
  wandb_group: unet
  wandb_project: CANARI
verbose: true
paths:
  train: outputs/${train.name}/training/
source_dataset_id: era5
model:
  model_name: unet
  network:
    _target_: canari_ml.models.models.UNet
    _partial_: true
    filter_size: 3
    n_filters_factor: 1.0
    n_output_classes: 1
  litmodule:
    _target_: canari_ml.models.lightning_modules.LitUNet
    _partial_: true
    criterion:
      _target_: canari_ml.models.losses.WeightedLoss
      loss_type: mse
    learning_rate: 0.0001
    metrics:
    - canari_ml.models.metrics.MAE
    - canari_ml.models.metrics.MSE
    - canari_ml.models.metrics.RMSE
    enable_leadtime_metrics: false
callbacks:
  model_checkpoint:
    _target_: lightning.pytorch.callbacks.ModelCheckpoint
    dirpath: ${hydra:runtime.output_dir}/checkpoints
    filename: epoch={epoch}-${callbacks.model_checkpoint.monitor}={${callbacks.model_checkpoint.monitor}:.4f}
    monitor: val_rmse
    verbose: true
    save_last: true
    save_top_k: 1
    mode: min
    auto_insert_metric_name: false
    save_weights_only: false
    every_n_train_steps: null
    train_time_interval: null
    every_n_epochs: null
    save_on_train_epoch_end: null
    enable_version_counter: false
  early_stopping:
    _target_: lightning.pytorch.callbacks.EarlyStopping
    monitor: val_rmse
    min_delta: 0.0
    patience: 10
    mode: min
    strict: true
    check_finite: true
    stopping_threshold: null
    divergence_threshold: null
    check_on_train_epoch_end: null
    log_rank_zero_only: null
logger:
  _target_: pytorch_lightning.loggers.TensorBoardLogger
  save_dir: tb_logs
  name: ${train.name}_${train.seed}
trainer:
  _target_: lightning.pytorch.Trainer
  _partial_: true
  accelerator: auto
  devices: 1
  precision: 16-mixed
  log_every_n_steps: 5
  max_epochs: ${train.epochs}
  num_sanity_val_steps: 0
  deterministic: true
  fast_dev_run: false
  logger: ${logger}
  callbacks: ${callbacks}


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canari_ml train CLI Help¶

`canari_ml train` CLI Help¶