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    "info": {
        "author": "Benoit Martin",
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        "description": "# PyTorch trainer\n\n[![CircleCI](https://circleci.com/gh/benoitmartin88/pytorchtrainer/tree/master.svg?style=svg)](https://circleci.com/gh/benoitmartin88/pytorchtrainer/tree/master)\n\nAre you tired of writing those same epoch and data-loader loops to train your PyTorch module ?\nLook no further, PyTorch trainer is a library that hides all those boring training lines of code that should be native to PyTorch. \n\nYou will also benefit from the following features:\n\n- Early stopping: stop training after a period of stagnation\n- Checkpointing: save model and estimator at regular intervals\n- CSV file writer to output logs\n- Several metrics are available: all default PyTorch loss functions, Accuracy, MAE\n- Progress bar from console\n- SIGINT handling: handle CTRL-C\n- Model's data type (`float32`, `float64`) \n\n\n## Example\n\nCode examples can be found in the [example folder](https://github.com/benoitmartin88/pytorchtrainer/tree/master/examples).\n\nHere is a simple example:\n\n``` python\n\nimport torch\nimport pytorchtrainer as ptt\n\n\n# Your usual model, optimizer, loss function and data loaders\nmodel = MyModel()\noptimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)\ncriterion = torch.nn.MSELoss()\ntrain_loader = MyTrainDataloader()\nvalidation_loader = MyValidationDataloader()\n\n\n# instantiate a default trainer\ntrainer = ptt.create_default_trainer(model, optimizer, criterion)\n\n# optionally save a checkpoint after every 10 epochs\ntrainer.register_post_epoch_callback(ptt.checkpoint.SaveCheckpointCallback(save_every=10))\n\n# optionally compute validation loss after every epoch\nvalidation_callback = ptt.callback.ValidationCallback(validation_loader, ptt.metric.TorchLoss(criterion), validate_every=1)\ntrainer.register_post_epoch_callback(validation_callback)\n\n# optionally save training and validation loss after every iteration using default save directory\ntrainer.register_post_iteration_callback(ptt.callback.CsvWriter(save_every=1,\n                                                                extra_header=[validation_callback.state_attribute_name],\n                                                                callback=lambda state: [state.get(validation_callback.state_attribute_name)]))\n# run the training\ntrainer.train(train_loader, max_epochs=100)\n\n```\n\n## Dependencies\n\n- python > 3.5\n- pytorch > 1.0.0 (install instructions from the official [PyTorch website](https://pytorch.org/get-started/locally))\n\n\n## Contributing\n\nFeel free to submit an issue or pull request. But before you do please read the [contributing guidelines](CONTRIBUTING.md)\n\n\n\n",
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