Quick Start

This guide will help you get started with PyHazards quickly using the hazard-first API.

Basic Usage

Toy Example (tabular classification)

import torch
from pyhazards.datasets import DataBundle, DataSplit, Dataset, FeatureSpec, LabelSpec
from pyhazards.models import build_model
from pyhazards.engine import Trainer
from pyhazards.metrics import ClassificationMetrics

class ToyHazard(Dataset):
    def _load(self):
        x = torch.randn(500, 16)
        y = torch.randint(0, 2, (500,))
        splits = {
            "train": DataSplit(x[:350], y[:350]),
            "val": DataSplit(x[350:425], y[350:425]),
            "test": DataSplit(x[425:], y[425:]),
        }
        return DataBundle(
            splits=splits,
            feature_spec=FeatureSpec(input_dim=16, description="toy features"),
            label_spec=LabelSpec(num_targets=2, task_type="classification"),
        )

data = ToyHazard().load()
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, optimizer=optimizer, loss_fn=loss_fn, max_epochs=5)
results = trainer.evaluate(data, split="test")
print(results)

GPU Support

PyHazards automatically detects CUDA availability. To explicitly set the device:

Using Environment Variable:

export PYHAZARDS_DEVICE=cuda:0

Using Python API:

from pyhazards.utils import set_device

# Set to use CUDA device 0
set_device("cuda:0")

# Or use CPU
set_device("cpu")

Next Steps

For more detailed documentation, please refer to: