Callbacks

Exponential Moving Average (EMA)

During training, EMA maintains a moving average of the trained parameters. EMA parameters can produce significantly better results and faster convergence for a variety of different domains and models.

EMA is a simple calculation. EMA Weights are pre-initialized with the model weights at the start of training.

Every training update, the EMA weights are updated based on the new model weights.

\[ema_w = ema_w * decay + model_w * (1-decay)\]

Enabling EMA is straightforward in your .yaml file. For example:

exp_manager.ema.enable=True
exp_manager.ema.decay=0.999

Also offers other helpful arguments.

Argument	Description
exp_manager.ema.validate_original_weights=True	Validate the original weights instead of EMA weights.
exp_manager.ema.every_n_steps=2	Apply EMA every N steps instead of every step.
exp_manager.ema.cpu_offload=True	Offload EMA weights to CPU. May introduce significant slow-downs.