Optimizer

Configurations for optimizers.

You can add configurations either with a config file or by registering structured configs in code.

Here is an example of how you could register a new configuration in code using hydra-zen.builds:

import hydra_zen
from torch.optim import Adam  # type: ignore

optimizers_store = hydra_zen.store(group="algorithm/optimizer")

AdamConfig = optimizers_store(
    hydra_zen.builds(
        Adam,
        zen_partial=True,
        populate_full_signature=True,
        zen_exclude=["params"],
        zen_dataclass={"cls_name": "AdamConfig", "frozen": False},
    ),
    name="base_adam",
)

From the command-line, you can select both configs that are yaml files as well as structured config (dataclasses).

This works the same way as creating config files for each optimizer under configs/algorithm/optimizer. Config files can also use structured configs in their defaults list.