TorchFit is a simple, easy-to-use, and minimalistic training-helper for PyTorch
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 torchfit.Learner import torchfit learner = torchfit.Learner(model, train_loader, val_loader=val_loader) # estimate LR using fastai-like Learning Rate Finder learner.find_lr() # train using 1cycle learning rate policy learner.fit_onecycle(1e-4, 3) # plot training vs. validation loss learner.plot('loss') # make predictions y_pred = learner.predict(test_loader) # save model and reload later learner.save('/tmp/mymodel') learer.load('/tmp/mymodel')
For more information see: tutorial.ipynb
After ensuring PyTorch is installed, install
pip3 install torchfit
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