Learning Schedule for Fast Conformer
Yes, only one cycle. It is preferred to use max steps since it makes it easier to configure the scheduler but you can provide other args and it will work fine  Thank you for the clarification!

Regarding model averaging, the paper mentions taking average of the last 5 checkpoint. In your implementation, do you take average of the last 5 checkpoints or simply take average of the 5 best checkpoints? Last 5 checkpoints is more stable and gets adequate improvements. Best 5 can be unstable based on how "stale" the checkpoints are - potentially an epoch 50 xheckpoint can somehow be merged with epoch 500 and that usually causes degradation in performance  I see. And the val_check_interval is 1.0 so the last 5 checkpoints are the model at the end of the last 5 epochs, right? Usually, but not always. It's usually within the bracket of last 5-15 Epochs   I see. Thank you very much!
Hi,

I don't see the max_steps specified in this [config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_ctc_bpe.yaml#L184) for Fast-Conformer CTC BPE. Does that mean there's only one cycle in the whole training process because the max_steps param will be the total training steps - warmup_steps?

Do I need to specify the max_steps to something like the total number of iteration per epoch?

Thanks!