{
    "info": {
        "author": "Benoit Anctil-Robitaille",
        "author_email": "benoit.anctil-robitaille.1@ens.etsmtl.ca",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "#  <img src=\"/icons/oil.png\" width=\"90\" vertical-align=\"bottom\">Kerosene\n> Deep Learning framework for fast and clean research development with Pytorch - <b>[see the doc for more details.](https://kerosene.readthedocs.io/en/latest/)</b>\n\n## MNIST Example\n > Here is a simple example that shows how easy and clean it is to train a simple network. In very few lines of code, the model is trained using mixed precision and you got Visdom + Console logging automatically. See full example there: [MNIST-Kerosene](https://github.com/banctilrobitaille/kerosene-mnist)\n \n```python\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.INFO)\n    CONFIG_FILE_PATH = \"config.yml\"\n\n    model_trainer_config, training_config = YamlConfigurationParser.parse(CONFIG_FILE_PATH)\n\n    train_loader = DataLoader(torchvision.datasets.MNIST('./files/', train=True, download=True, transform=Compose(\n        [ToTensor(), Normalize((0.1307,), (0.3081,))])), batch_size=training_config.batch_size_train, shuffle=True)\n\n    test_loader = DataLoader(torchvision.datasets.MNIST('./files/', train=False, download=True, transform=Compose(\n        [ToTensor(), Normalize((0.1307,), (0.3081,))])), batch_size=training_config.batch_size_valid, shuffle=True)\n\n    # Initialize the loggers\n    visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(CONFIG_FILE_PATH))\n\n    # Initialize the model trainers\n    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(model_trainer_config, RunConfiguration(use_amp=False))\n\n    # Train with the training strategy\n    trainer = SimpleTrainer(\"MNIST Trainer\", train_loader, test_loader, model_trainer) \\\n        .with_event_handler(PrintTrainingStatus(every=100), Event.ON_BATCH_END) \\\n        .with_event_handler(PrintModelTrainersStatus(every=100), Event.ON_BATCH_END) \\\n        .with_event_handler(PlotAllModelStateVariables(visdom_logger), Event.ON_EPOCH_END) \\\n        .with_event_handler(PlotGradientFlow(visdom_logger, every=100), Event.ON_TRAIN_BATCH_END) \\\n        .train(training_config.nb_epochs)\n```\n\n## Contributing\n\n#### How to contribute ?\n- [X] Create a branch by feature and/or bug fix\n- [X] Get the code\n- [X] Commit and push\n- [X] Create a pull request\n\n#### Branch naming\n\n##### Feature branch\n> feature/ [Short feature description] [Issue number]\n\n##### Bug branch\n> fix/ [Short fix description] [Issue number]\n\n#### Commits syntax:\n\n##### Adding code:\n> \\+ Added [Short Description] [Issue Number]\n\n##### Deleting code:\n> \\- Deleted [Short Description] [Issue Number]\n\n##### Modifying code:\n> \\* Changed [Short Description] [Issue Number]\n\n##### Merging code:\n> Y Merged [Short Description] [Issue Number]\n\n\nIcons made by <a href=\"http://www.flaticon.com/authors/freepik\" title=\"Freepik\">Freepik</a> from <a href=\"http://www.flaticon.com\" title=\"Flaticon\">www.flaticon.com</a> is licensed by <a href=\"http://creativecommons.org/licenses/by/3.0/\" title=\"Creative Commons BY 3.0\" target=\"_blank\">CC 3.0 BY</a>",
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        "license": "MIT",
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        "name": "torch-kerosene",
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        "summary": "Deep Learning framework for fast and clean research development with Pytorch",
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