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TorchFit is a simple, easy-to-use, and minimalistic training-helper for PyTorch

Project description


TorchFit is a bare-bones, minimalistic training-helper for PyTorch that exposes an easy-to-use fit method in the style of fastai and Keras.

TorchFit is intended to be minimally-invasive with a tiny footprint and as little bloat as possible. It is well-suited to those that are new to training models in PyTorch. For more complex training scenarios (e.g., training GANs, multi-node GPU training), PyTorch Lightning is highly recommended.


# normal PyTorch stuff
train_loader = create_your_training_data_loader()
val_loader = create_your_validation_data_loader()
test_loader = create_your_test_data_loader()
model = create_your_pytorch_model()

# wrap model and data in Learner
import torchfit
learner = torchfit.Learner(model, train_loader, val_loader=val_loader)

# estimate LR using Learning Rate Finder

# train using 1cycle learning rate policy
learner.fit_onecycle(1e-4, 3)

# plot training vs. validation loss

# make predictions as easy as in Keras
y_pred = learner.predict(test_loader)

# save model and reload later'/tmp/mymodel')

For more information, see the Tutorial Notebook.


After ensuring PyTorch is installed, install TorchFit with:

pip3 install torchfit

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