nebulgymaccelerates training by means of class decorators. A class decorator is a very elegant and non-intrusive method that allows
nebulgymto tag your model (
@accelerate_model) and your dataset (
@accelerate_dataset) and add functionalities to their classes. When you run a training session,
nebulgymwill greatly reduce the training time of your decorated model. As simple as that!
nebulgymclass decorators right before defining your dataset and model classes.
@accelerate_datasetmust be entered before the dataset definition.
nebulgymwill cache dataset samples in memory, so that reading these samples after the first time becomes much faster. Caching the dataset makes data loading faster and more efficient, solving what could become the main bottleneck of the whole training process.
@accelerate_modelmust be entered before the model definition.
nebulgymwill accelerate both forward and backward propagations by reducing the number of computationally expensive propagation steps and making computations more efficient.