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.
# 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 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 as easy as in Keras y_pred = learner.predict(test_loader) # save model and reload later learner.save('/tmp/mymodel') learer.load('/tmp/mymodel')
TorchFit Training Loop
For more information, see the the following notebooks:
- Quickstart with MNIST: quickstart notebook to get you up and running
- Tutorial Notebook: tutorial notebook using the same model and data employed in the PyTorch text classification tutorial
After ensuring PyTorch is installed, install
pip3 install torchfit
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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