Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

PyTorch module trainer

Project description

PyTorch trainer

CircleCI

Are you tired of writing those same epoch and data-loader loops to train your PyTorch module ? Look no further, PyTorch trainer is a library that hides all those boring training lines of code that should be native to PyTorch.

You will also benefit from the following features:

  • Early stopping: stop training after a period of stagnation
  • Checkpointing: save model and estimator at regular intervals
  • CSV file writer to output logs
  • Several metrics are available: all default PyTorch loss functions, Accuracy, MAE
  • Progress bar from console
  • SIGINT handling: handle CTRL-C
  • Model's data type (float32, float64)

Example

Code examples can be found in the example folder.

Here is a simple example:

import torch
import pytorchtrainer as ptt


# Your usual model, optimizer, loss function and data loaders
model = MyModel()
optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)
criterion = torch.nn.MSELoss()
train_loader = MyTrainDataloader()
validation_loader = MyValidationDataloader()


# instantiate a default trainer
trainer = ptt.create_default_trainer(model, optimizer, criterion)

# optionally save a checkpoint after every 10 epochs
trainer.register_post_epoch_callback(ptt.checkpoint.SaveCheckpointCallback(save_every=10))

# optionally compute validation loss after every epoch
validation_callback = ptt.callback.ValidationCallback(validation_loader, ptt.metric.TorchLoss(criterion), validate_every=1)
trainer.register_post_epoch_callback(validation_callback)

# optionally save training and validation loss after every iteration using default save directory
trainer.register_post_iteration_callback(ptt.callback.CsvWriter(save_every=1,
                                                                extra_header=[validation_callback.state_attribute_name],
                                                                callback=lambda state: [state.get(validation_callback.state_attribute_name)]))
# run the training
trainer.train(train_loader, max_epochs=100)

Dependencies

  • python > 3.5
  • pytorch > 1.0.0 (install instructions from the official PyTorch website)

Contributing

Feel free to submit an issue or pull request. But before you do please read the contributing guidelines

Project details


Download files

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

Files for pytorchtrainer, version 0.2.1
Filename, size File type Python version Upload date Hashes
Filename, size pytorchtrainer-0.2.1-py3-none-any.whl (15.3 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size pytorchtrainer-0.2.1.tar.gz (14.0 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page