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.

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 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

Tutorials and Examples

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

Features

Learning Rate Finder

learner.find_lr()

A `fit` method for Training

# Examples
learner.fit(lr, epochs)
learner.fit_onecycle(lr, epochs)
learner.fit(lr, epochs, schedulers=[scheduler])

Easy-to-Execute Testing and Predictions

# Examples
outputs = learner.predict(test_loader)
outputs, targets = learner.predict(test_loader, return_targets=True)

text = 'Shares of IBM rose today.'
predicted_label = learner.predict_example(text, preproc_fn=preprocess, labels=labels)

Gradient Accumulation

learner.fit_onecycle(lr, 1, accumulation_steps=8)

Gradient Clipping

learner.fit_onecycle(lr, 1, gradient_clip_val=1)

Mixed Precision Training

torchfit.Learner(model, train_loader, val_loader=val_loader, use_amp=True, amp_level='O2')

Multi-GPU Training and GPU Selection

To train on first two GPUs (0 and 1):

learner = torchfit.Learner(model, train_loader, val_loader=test_loader, gpus=[0,1])

To train only on the second GPU, one can do either this:

learner = torchfit.Learner(model, train_loader, val_loader=test_loader, gpus=[1])

or this...

learner = torchfit.Learner(model, train_loader, val_loader=test_loader, device='cuda:1')

Resetting Weights of Model

learner.reset_weights()

Saving/Loading Model

learner.save('/tmp/mymodel')
learner.load('/tmp/mymodel')

Installation

After ensuring PyTorch is installed, install TorchFit with:

pip3 install torchfit

amaiya/torchfit