Configuration reference¶
This page describes the composed training schema first. Historical full YAML keys are retained in a separate compatibility section.
Composition root¶
| Key/group | Purpose |
|---|---|
seed | global Lightning and worker seed |
data=<choice> | instantiate a data module from configs/data/ |
model=<choice> | instantiate a model from configs/model/ |
trainer=<choice> | Lightning runtime and checkpoint settings |
logging=<choice> | tracking adapters |
experiment=<choice> | optional focused overrides |
_target_ is owned locally by each data/model choice and is passed to Hydra's instantiation mechanism.
Modular data¶
| Key | Purpose |
|---|---|
_target_ | data-module constructor/factory |
root, stats_file | exported dataset and training statistics |
train_split, val_split, test_split | split directory names |
target_size, center_crop_size | output and optional final crop shape |
random_flips | paired physics-aware train augmentation |
include_test_in_train | explicitly merge test into train; modular default is false |
require_era5 | reject rows without context |
include_pmw, include_ibtracs | request optional companions/metadata |
normalization, target_normalization | condition and target transforms |
robust_clip, max_era5_time_gap_hours | robust range and context freshness |
loader.batch_size, num_workers | per-process loader size/workers |
loader.pin_memory, persistent_workers | loader memory/lifetime behavior |
sampling.intensity_balanced.enabled | use intensity-aware train sampling |
Dataset implementations may add focused keys, but their resulting capabilities must be represented by DataSpec.
Modular deterministic-residual model¶
| Key | Purpose |
|---|---|
_target_ | ERA5ResidualRegressor constructor |
condition_channels, base_channels, channel_mults | data width and U-Net sizing |
huber_delta_ms, off_swath_anchor_weight | physical residual loss |
high_wind_*, peak_* | intensity weighting and robust peak objective |
radial_profile_*, exceedance_area_* | optional structural objectives |
prediction_min_ms, prediction_max_ms | physical output bounds |
psnr_data_range_ms | physical PSNR range |
lr, weight_decay | AdamW settings |
lr_scheduler_* | ReduceLROnPlateau settings and monitor |
validation_reconstruction_batches, log_reconstruction_images | validation media coverage |
Modular diffusion model¶
Both standalone and residual diffusion use descriptive flat constructor keys.
| Key | Purpose |
|---|---|
_target_ | model constructor or model-specific factory |
condition_channels / base_condition_channels | prepared/base condition width |
generated_channels | target/residual output width |
num_timesteps, schedule | forward process |
model_dim, model_dim_mults, model_channels, model_out_dim | U-Net sizing |
sampling_method, sampling_timesteps, sampling_eta | reverse sampler |
guidance_scale, condition_dropout_probability | classifier-free guidance |
clip_sample | clip the clean estimate during reverse sampling |
ema_decay, ema_update_after_step, ema_use_for_eval | EMA behavior |
min_snr_gamma | optional epsilon-prediction Min-SNR cap |
lr, lr_scheduler_* | optimizer and scheduler |
validation_seed, validation_ensemble_size, validation_ensemble_batches | stable validation members |
validation_reconstruction_batches, log_reconstruction_images | reconstruction/media coverage |
Residual diffusion additionally accepts:
| Key | Purpose |
|---|---|
baseline_source | era5 or deterministic |
baseline_checkpoint_path | frozen Stage 1 checkpoint; environment-backed choice uses GEO2WF_BASELINE_CKPT |
residual_transform, residual_soft_scale_ms, residual_clip_ms | signed transform |
prediction_min_ms, prediction_max_ms | recomposed output bounds |
*_loss_weight and related thresholds/kernels | optional gradient, spectral, low-frequency, smoothness, peak, radial, exceedance, multiscale, annular objectives |
sparse_target_fill, unobserved_loss_weight | weak off-swath supervision |
probabilistic_score_* | ensemble checkpoint-score composition |
Inspect the selected file in configs/model/ for authoritative defaults.
trainer¶
| Key | Purpose |
|---|---|
max_epochs | epoch limit |
accelerator, devices, strategy | hardware and distributed execution |
precision, float32_matmul_precision | numerical mode |
deterministic | Lightning deterministic-algorithm request |
log_every_n_steps | step logging interval |
enable_checkpointing, default_root_dir | artifacts and run parent |
limit_train_batches, limit_val_batches | bounded loops |
checkpoint.monitor, mode | selection metric and direction |
checkpoint.save_top_k, save_last, filename | retention/naming policy |
logging¶
| Key | Purpose |
|---|---|
wandb.enabled | construct W&B unless disabled by environment |
wandb.project, wandb.name | run destination and display name |
wandb.log_model | W&B checkpoint logging policy |
CSV metrics and run manifests are always configured independently of W&B.
Legacy full-YAML reference¶
Legacy configs remain accepted through --config. Their top-level sections are:
| Section | Translation |
|---|---|
export | maintained exporter defaults |
data | adapted to PairedDataModule.from_config |
model.type | compatibility model factory only |
model.unet, model.sampling, model.residual | translated into model constructor arguments |
optimization | translated into optimizer, scheduler, EMA, and objective arguments |
validation | translated into model validation/sampling settings |
trainer, logging | consumed by the shared training runtime |
These files may also contain PMW keys such as pmw_as_condition, max_pmw_time_gap_hours, and pmw_include_time_offset. They preserve historical experiments but are not templates for new grouped configs. A full YAML file cannot be combined with Hydra overrides.
Export configuration¶
Export currently retains its established argparse/full-YAML interface. Relevant keys include source/manifest/output paths, channel set, splits, grid size and resolution, closest-match limits, PMW/IBTrACS/ERA5 inclusion and freshness, crop center/shift/padding, and a per-split limit. Explicit command flags take precedence. See Export GEO–SAR.