credit.trainers.utils

Functions

cleanup()
cycle(dl)
accum_log(log, new_logs)
inject_flat_var_keys(→ None)	Inject Gen1-compatible variable keys into conf["data"] for metrics/loss.
inject_postblock_info(→ None)	Inject post-block indices into conf["postblock"] for proper initialization of Gen 1 postblocks.
load_dataset(...)	Build a MultiSourceDataset for train or validation.
load_dataloader(→ torch.utils.data.DataLoader)	Build a DataLoader with DistributedMultiStepBatchSampler.
load_model_states_and_optimizer(conf, model, device)	Load model weights, optimizer, scheduler, and gradient scaler.

Module Contents

credit.trainers.utils.cleanup()

credit.trainers.utils.cycle(dl)

credit.trainers.utils.accum_log(log, new_logs)

credit.trainers.utils.inject_flat_var_keys(conf: dict) → None

Inject Gen1-compatible variable keys into conf[“data”] for metrics/loss.

LatWeightedMetrics and VariableTotalLoss2D expect flat lists at: - conf["data"]["variables"] - conf["data"]["surface_variables"] - conf["data"]["diagnostic_variables"]

These are derived from the nested Gen2 source config.

credit.trainers.utils.inject_postblock_info(conf: dict) → None

Inject post-block indices into conf[“postblock”] for proper initialization of Gen 1 postblocks.

credit.trainers.utils.load_dataset(conf: dict, is_train: bool) → credit.datasets.multi_source.MultiSourceDataset

Build a MultiSourceDataset for train or validation.

credit.trainers.utils.load_dataloader(conf: dict, dataset: credit.datasets.multi_source.MultiSourceDataset, rank: int, world_size: int, is_train: bool) → torch.utils.data.DataLoader

Build a DataLoader with DistributedMultiStepBatchSampler.

credit.trainers.utils.load_model_states_and_optimizer(conf, model, device)

Load model weights, optimizer, scheduler, and gradient scaler.