| from __future__ import annotations |
|
|
| from matsciml.lightning.callbacks import ( |
| ExponentialMovingAverageCallback, |
| ManualGradientClip, |
| ) |
| from pytorch_lightning.callbacks import StochasticWeightAveraging |
|
|
| """ |
| This script is not intended to work entirely, but |
| shows the key elements needed for training (for |
| the Intel folks, "BKM"). |
| |
| These are: |
| |
| - For energies, use ``AtomWeightedMSE`` as the metric. The new default behavior |
| for ``ForceRegressionTask`` uses this, but if you use another task to represent |
| it this may need to be manually set. |
| - Use stochastic weight averaging callback |
| - Use exponential moving average callback |
| - Use gradient clipping |
| |
| An important thing to note about EMA is that the exponential weights |
| are used _outside_ of training: for validation/testing/ASE usage, |
| the EMA weights will be used instead of the "vanilla" model. This |
| means that logged values are not necessarily directly comparable |
| between training and validation, or rather, validation should be |
| outperforming training. |
| """ |
|
|
| |
| callbacks = [ |
| StochasticWeightAveraging(swa_lrs=1e-2, swa_epoch_start=1), |
| ExponentialMovingAverageCallback(decay=0.99), |
| ManualGradientClip(value=10.0, algorithm="norm"), |
| ] |
|
|
