torchloop 0.1.0

Lightweight PyTorch utility library for training, evaluation, and TFLite export — without the framework lock-in.

torchloop

Lightweight PyTorch utility library for training, evaluation, and TFLite export — without the framework lock-in.

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The Problem

You write the same PyTorch training loop in every project. Same checkpoint logic. Same metric assembly. Same TFLite export steps. It's tedious and inconsistent.

torchloop abstracts exactly that — nothing more.

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Install

pip install torchloop

# With TFLite export support
pip install torchloop[export]

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Usage

Training

from torchloop import Trainer

trainer = Trainer(
    model,
    optimizer=torch.optim.Adam(model.parameters()),
    criterion=torch.nn.CrossEntropyLoss(),
    device="cuda",
    patience=5,           # early stopping
)

history = trainer.fit(train_loader, val_loader, epochs=30)
trainer.save("best.pt")

Evaluation

from torchloop import Evaluator

ev = Evaluator(model, device="cuda")
results = ev.report(val_loader, class_names=["No Damage", "Minor", "Major", "Destroyed"])
# prints sklearn classification report

fig = ev.confusion_matrix(val_loader)
fig.savefig("cm.png")

per_class = ev.f1_per_class(val_loader)
# {'No Damage': 0.91, 'Minor': 0.78, ...}

Export

from torchloop.exporter import Exporter

exp = Exporter(model, input_shape=(1, 3, 224, 224))
exp.to_onnx("model.onnx")
exp.to_tflite("model.tflite", quantize=True)

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Design Principles

• No lock-in: Works with any nn.Module. No subclassing required.
• Minimal surface area: Three modules. That's it.
• You own the model: torchloop wraps your loop, doesn't replace your architecture.

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Roadmap

• v0.1.0 — Trainer, Evaluator, Exporter
• v0.2.0 — LR scheduler support, mixed precision (AMP)
• v0.3.0 — W&B / MLflow logging hooks

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License

MIT