How to disable model/weight serialization fully with AllenNLP settings? - allennlp

Question

I wish disable serializing all model/state weights in standard AllenNLP model training via the use of jsonnet config files.
The reason for this is that I am running automatic hyperparameter optimization using Optuna.
Testing dozens of models fills up a drive pretty quickly.
I already have disabled the checkpointer by setting num_serialized_models_to_keep to 0:
trainer +: {
checkpointer +: {
num_serialized_models_to_keep: 0,
},
I do not wish to set serialization_dir to None as I still want the default behaviour regarding logging of intermediate metrics, etc. I only want to disable the default model state, training state, and best model weights writing.
Besides the option I set above, are there any default trainer or checkpointer options to disable all serialization of model weights? I checked the API docs and webpage but could not find any.
If I need to define the functionality for such an option myself, which base function(s) from the AllenNLP should I override in my Model subclass?
Alternatively, is their any utility for cleaning up intermediate model state when training is concluded?
EDIT: #petew's answer shows the solution for a custom checkpointer, but I am not clear on how to make this code findable to allennlp train for my use-case.
I wish to make the custom_checkpointer callable from a config file as below:
trainer +: {
checkpointer +: {
type: empty,
},
What would be best practice to load the checkpointer when calling allennlp train --include-package <$my_package>?
I have my_package with submodules in subdirectories such as my_package/modelss and my_package/training.
I would like to place the custom checkpointer code in my_package/training/custom_checkpointer.py
My main model is located in my_package/models/main_model.py.
Do I have to edit or import any code/functions in my main_model class to use the custom checkpointer?

Answer 1

You could create and register a custom Checkpointer that basically just does nothing:
#Checkpointer.register("empty")
class EmptyCheckpointer(Registrable):
def maybe_save_checkpoint(
self, trainer: "allennlp.training.trainer.Trainer", epoch: int, batches_this_epoch: int
) -> None:
pass
def save_checkpoint(
self,
epoch: Union[int, str],
trainer: "allennlp.training.trainer.Trainer",
is_best_so_far: bool = False,
save_model_only=False,
) -> None:
pass
def find_latest_checkpoint(self) -> Optional[Tuple[str, str]]:
pass
def restore_checkpoint(self) -> Tuple[Dict[str, Any], Dict[str, Any]]:
return {}, {}
def best_model_state(self) -> Dict[str, Any]:
return {}