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Deeplearning framework for PyTorch

Project description


FOS is a Python framework that makes it easy to develop neural network models in PyTorch. Some of its main features include:

  • Less boilerplate code required, see also the example below.

  • Lightweight and no magic under the hood that might get in the way.

  • You can extend Fos using common OO patterns.

  • Get the insights you need into the performance of the model.


You can install FOS using pip:

pip install fos

Or alternatively from the source:

python install

Fos requires Python 3.6 or higher.


Training a model, requires just a few lines of code. First create the model, optimizer and loss function that you want to use, using normal PyTorch code:

model = resnet18()
optim = Adam(model.parameters())
loss = F.binary_cross_entropy_with_logits

Then create the FOS workout that will take care of the training and output:

workout = Workout(net, loss, optim)

And we are ready to start the training:, valid_data, epochs=5)


You can find several example Jupyter notebooks here

You can also run them on Google Colab directly:

  • Basic


  • Inputs

  • Tensorboard


If you want to help out, we appreciate all contributions. Please see the contribution guidelines for more information.

As always, PRs are welcome :)=

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