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. We can pass the additional argument to the experiment manager at runtime.
python examples/asr/asr_ctc/speech_to_text_ctc.py \
model.train_ds.manifest_filepath=/path/to/my/train/manifest.json \
model.validation_ds.manifest_filepath=/path/to/my/validation/manifest.json \
trainer.devices=2 \
trainer.accelerator='gpu' \
trainer.max_epochs=50 \
exp_manager.ema.enable=True # pass this additional argument to enable EMA
To change the decay rate, pass the additional argument.
python examples/asr/asr_ctc/speech_to_text_ctc.py \
...
exp_manager.ema.enable=True \
exp_manager.ema.decay=0.999
We also offer 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. |