ocean-forecast-training

You can automate ocean forecast model training by supplying dataset and log directories to initiate an end-to-end workflow that handles dataset validation, model selection, training execution, and evaluation.

Ocean time-series training workflows support model-agnostic training for FNO2d, UNet2d, SwinTransformerV2, Crossformer, and other forecast models with guided hyperparameters and operational safeguards.

Yes, the ocean forecast training workflow handles GPU selection automatically and provides automatic parameter suggestions, allowing you to train models without manual hardware configuration.

The training workflow includes OOM protection, failure recovery workflows, and operational safety rails to prevent crashes and ensure reliable model training on large datasets.

The best way to compare ocean forecasting models is using an orchestrated training workflow that iteratively trains and evaluates multiple models with guided hyperparameter tuning and result reporting.