Deep Learning framework for fast and clean research development with Pytorch

# 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

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

[ToTensor(), Normalize((0.1307,), (0.3081,))])), batch_size=training_config.batch_size_train, shuffle=True)

[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
.with_event_handler(PlotMonitors(every=500, visdom_logger=visdom_logger), Event.ON_BATCH_END) \
.with_event_handler(PrintTrainingStatus(every=100), Event.ON_BATCH_END) \
.train(training_config.nb_epochs)


## Events

Event Description
ON_TRAINING_BEGIN At the beginning of the training phase
ON_TRAINING_END At the end of the training phase
ON_VALID_BEGIN At the beginning of the validation phase
ON_VALID_END At the end of the validation phase
ON_TEST_BEGIN At the beginning of the test phase
ON_TEST_END At the end of the test phase
ON_EPOCH_BEGIN At the beginning of each epoch (training, validation, test)
ON_EPOCH_END At the end of each epoch (training, validation, test)
ON_TRAIN_EPOCH_BEGIN At the beginning of each training epoch
ON_TRAIN_EPOCH_END At the end of each training epoch
ON_VALID_EPOCH_BEGIN At the beginning of each validation epoch
ON_VALID_EPOCH_END At the end of each validation epoch
ON_TEST_EPOCH_BEGIN At the beginning of each test epoch
ON_TEST_EPOCH_END At the end of each test epoch
ON_BATCH_BEGIN At the beginning of each batch (training, validation, test)
ON_BATCH_END At the end of each batch (training, validation, test)
ON_TRAIN_BATCH_BEGIN At the beginning of each train batch
ON_TRAIN_BATCH_END At the end of each train batch
ON_VALID_BATCH_BEGIN At the beginning of each validation batch
ON_VALID_BATCH_END At the end of each validation batch
ON_TEST_BATCH_BEGIN At the beginning of each test batch
ON_TEST_BATCH_END At the end of each test batch
ON_FINALIZE Before the end of the process

## Handlers

• PrintTrainingStatus (Console)
• PrintMonitors (Console)
• PlotMonitors (Visdom)
• PlotLosses (Visdom)
• PlotMetrics (Visdom)
• PlotCustomVariables (Visdom)
• PlotLR (Visdom)
• Checkpoint
• EarlyStopping

## Contributing

#### How to contribute ?

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

#### Branch naming

##### Feature branch

feature/ [Short feature description] [Issue number]

##### Bug branch

fix/ [Short fix description] [Issue number]

#### Commits syntax:

+ 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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## Project details

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