Kerosene

> Kerosene is a high-level deep Learning framework for fast and clean research development with Pytorch - see the doc for more details.. Kerosene let you focus on your model and data by providing clean and readable code for training, visualizing and debugging your achitecture without forcing you to implement rigid interface for your model.

Out of The Box Features

• [X] Basic training logic and user defined trainers
• [X] Fine grained event system with multiple handlers
• [X] Multiple metrics and criterions support
• [X] Automatic configuration parsing and model instantiation
• [X] Automatic support of mixed precision with Apex and dataparallel training
• [X] Automatic Visdom logging
• [X] Integrated Ignite metrics and Pytorch criterions

MNIST Example

> 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

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    CONFIG_FILE_PATH = "config.yml"

    model_trainer_config, training_config = YamlConfigurationParser.parse(CONFIG_FILE_PATH)

    train_loader = DataLoader(torchvision.datasets.MNIST('./files/', train=True, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))])), batch_size=training_config.batch_size_train, shuffle=True)

    test_loader = DataLoader(torchvision.datasets.MNIST('./files/', train=False, download=True, transform=Compose(
        [ToTensor(), Normalize((0.1307,), (0.3081,))])), batch_size=training_config.batch_size_valid, shuffle=True)

    visdom_logger = VisdomLogger(VisdomConfiguration.from_yml(CONFIG_FILE_PATH))

    # Initialize the model trainers
    model_trainer = ModelTrainerFactory(model=SimpleNet()).create(model_trainer_config)

    # Train with the training strategy
    SimpleTrainer("MNIST Trainer", train_loader, test_loader, None, model_trainer, RunConfiguration(use_amp=False)) \
        .with_event_handler(PlotMonitors(every=500, visdom_logger=visdom_logger), Event.ON_BATCH_END) \
        .with_event_handler(PlotAvgGradientPerLayer(every=500, visdom_logger=visdom_logger), Event.ON_TRAIN_BATCH_END) \
        .with_event_handler(PrintTrainingStatus(every=100), Event.ON_BATCH_END) \
        .train(training_config.nb_epochs)

Events

Handlers

• [X] PrintTrainingStatus (Console)
• [X] PrintMonitors (Console)
• [X] PlotMonitors (Visdom)
• [X] PlotLosses (Visdom)
• [X] PlotMetrics (Visdom)
• [X] PlotCustomVariables (Visdom)
• [X] PlotLR (Visdom)
• [X] PlotAvgGradientPerLayer (Visdom)
• [X] Checkpoint
• [X] EarlyStopping

Contributing

How to contribute ?

• [X] Create a branch by feature and/or bug fix
• [X] Get the code
• [X] Commit and push
• [X] Create a pull request

Branch naming

Feature branch

> feature/ [Short feature description] [Issue number]

Bug branch

> fix/ [Short fix description] [Issue number]

Commits syntax:

Adding code:

> + Added [Short Description] [Issue Number]

Deleting code:

> - Deleted [Short Description] [Issue Number]

Modifying code:

> * Changed [Short Description] [Issue Number]

Merging code:

> Y Merged [Short Description] [Issue Number]

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