hydra-zen v0.9.0 released!

[rsokl]

Hi all,

I am excited to announce the release of hydra-zen v0.9.0¹. This release helps to remove Hydra-specific boilerplate code from your Hydra application.

Auto extract/resolve/instantiate fields from a config with the zen wrapper

Use the zen wrapper to write Hydra-agnostic task functions for your app:

E.g., take this old task function:

import hydra
from hydra.utils import instantiate

@hydra.main(config_name="my_app", config_path=None, version_base="1.2")
def trainer_task_fn(cfg):
   model = instantiate(cfg.model)
   data = instantiate(cfg.data)
   partial_optim = instantiate(cfg.partial_optim)
   trainer = instantiate(cfg.trainer)

   optim = partial_optim(model.parameters())
   trainer(model, optim, data).fit(cfg.num_epochs)

if __name__ == "__main__":
   trainer_task_fn()

and use zen to get rid of that Hydra-specific code:

# note: no Hydra or hydra-zen specific logic here
def trainer_task_fn(model, data, partial_optim, trainer, num_epochs):
   optim = partial_optim(model.parameters())
   trainer(model, optim, data).fit(num_epochs)

if __name__ == "__main__":
    from hydra_zen import zen

    # All config-field extraction & instantiation is automated/mediated by zen.
    # I.e. `zen` will extract & instantiate model, data, etc. from the input
    # config and pass it to `trainer_task_fn`
    zen(trainer_task_fn).hydra_main(config_name="my_app", config_path=None)

Use ZenStore for a safe, isolated abstraction over Hydra's global config store

ZenStore is an abstraction over Hydra’s config store. It enables users to maintain multiple, isolated store instances before populating Hydra’s global config store. It also protects users from accidentally overwriting entries in Hydra’s global store. ZenStore possesses auto-config capabilities: it will automatically apply builds() and just() in intuitive ways on inputs to generate the stored configs.

Using hydra_zen.store auto-generate and store configs

from hydra_zen import ZenStore
from torch.optim import Adam, AdamW, RMSprop

torch_store = ZenStore("torch_store")

# Specify defaults for storing entries (group=optim)
# and for generating configs (_partial_=True and lr=1e-3)
optim_store = torch_store(group="optim", zen_partial=True, lr=0.001)

# Automatically applies `builds(<obj>, zen_partial=True, lr=0.001)`
# to create and then store configs under the "optim" group
optim_store(Adam, name="adam", amsgrad=True)
optim_store(AdamW, name="adamw", betas=(0.1, 0.999))
optim_store(RMSprop, name="rmsprop")

torch_store.add_to_hydra_store()  # populate Hydra's global store

The store can also be populated using a decorator pattern. E.g.,

from dataclasses import dataclass
from hydra_zen import store

profile_store = store(group="profile")

# Adds two store entries under the "profile" group of the store
# with configured defaults for `has_root`
@profile_store(name="admin", has_root=True)
@profile_store(name="basic", has_root=False)
@dataclass
class Profile:
    username: str
    schema: str
    has_root: bool


db_store = store(group="database")

# calls `builds(profile_database, [...])` under the hood and
# adds the config to the store under the "database" group
@db_store(name="std")
@db_store(name="test", size=1)
def profile_database(size):
    ...

Please read more about this, and the other improvements in 0.9.0. here:

Links:

Release notes: https://mit-ll-responsible-ai.github.io/hydra-zen/changes.html#v0-9-0
Docs: https://mit-ll-responsible-ai.github.io/hydra-zen/
GitHub: https://github.com/mit-ll-responsible-ai/hydra-zen

Footnotes

1. What is hydra-zen? It is a library that makes it easier – less boilerplate and more Pythonic – to use Hydra! ↩