Skip to main content

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

Project description

TorchFit

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.

Usage

# 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

Installation

After ensuring PyTorch is installed, install TorchFit with:

pip3 install torchfit

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchfit-0.1.0.tar.gz (8.6 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page