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Automatic mixed precision (AMP)

Automatic mixed precision (AMP) is a feature in PyTorch that enables the use of lower-precision data types, such as float16, in deep learning models for improved memory and computation efficiency. It automatically casts the model's parameters and buffers to a lower-precision data type, and dynamically rescales the activations to prevent underflow or overflow.

Set up AMP

To use AMP in SuperGradients, you simply need to set mixed_precision=True in your training_params.

In python script

from super_gradients import Trainer

trainer = Trainer("experiment_name")
model = ...

training_params = {"mixed_precision": True, ...:...}

trainer.train(model=model, training_params=training_params, ...)

In recipe

# my_training_hyperparams.yaml

mixed_precision: True # Whether to use mixed precision or not.