Engine

Summary

The engine wraps training/evaluation/prediction with sensible defaults and optional distributed support.

Core modules

  • pyhazards.engine.trainer — Trainer class with fit, evaluate, predict, checkpoint save.

  • pyhazards.engine.distributed — strategy selection and config helpers.

  • pyhazards.engine.inference — sliding-window inference placeholder for large rasters/grids.

Typical usage

import torch
from pyhazards.engine import Trainer
from pyhazards.metrics import ClassificationMetrics
from pyhazards.models import build_model

model = build_model(name="mlp", task="classification", in_dim=16, out_dim=2)
trainer = Trainer(model=model, metrics=[ClassificationMetrics()], mixed_precision=True)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.CrossEntropyLoss()

trainer.fit(data_bundle, optimizer=optimizer, loss_fn=loss_fn, max_epochs=10)
results = trainer.evaluate(data_bundle, split="test")
preds = trainer.predict(data_bundle, split="test")

Distributed and devices

  • Trainer(strategy="auto") uses DDP when multiple GPUs are available; otherwise runs single-device.

  • mixed_precision=True enables AMP when on CUDA.

  • Device selection is handled via pyhazards.utils.hardware.auto_device by default.