hydra-typing

Typed dataclass configs for Hydra — one import, zero boilerplate.

Your @hydra.main function receives an untyped DictConfig. With hydra-typing, it receives your @dataclass instance instead — full IDE autocompletion, mypy/pyright checking, and all Python types supported (Literal, Enum, Union, Path, datetime, nested dataclasses, List[Dataclass], Dict[str, Dataclass], etc.).

All Hydra features work unchanged — defaults: groups, ${} interpolation, CLI overrides, --multirun, sweepers, launchers, output management.

Install

pip install hydra-typing

One dependency: hydra-core.

Usage

Transparent patch (recommended) — keep your @hydra.main:

import hydra
import hydra_typing; hydra_typing.patch()

@hydra.main(config_path="conf", config_name="config", version_base=None)
def main(cfg: TrainConfig) -> None:
    # cfg is typed!  No DictConfig, no OmegaConf.
    print(cfg.model.hidden_dim)  # IDE autocompletion works

Explicit decorator:

from hydra_typing import hydra_main

@hydra_main(config_path="conf", config_name="config")
def main(cfg: TrainConfig) -> None:
    ...

Programmatic (notebooks, scripts):

from hydra_typing import load_config

cfg = load_config(TrainConfig, config_name="base",
                  overrides=["model=large", "lr=0.001"])

Features

• Typed configs — real @dataclass instances, not DictConfig
• Full Python type support — Literal, Enum, Union, Path, datetime, nested dataclasses, List[Dataclass], Dict[str, Dataclass]
• HydraConfig — typed access to Hydra's built-in runtime config (run.dir, job.name, overrides.task, etc.)
• _target_ / instantiate — standard Hydra _target_ pattern works as a typed field
• to_omegaconf() — 100% compatibility fallback: convert typed config back to OmegaConf DictConfig
• Non-invasive — functions without type annotations pass through unchanged
• Single file — hydra_typing.py, ~700 lines, one dependency

Complex nested configs

@dataclass
class LayerConfig:
    type: Literal["attention", "mlp"] = "attention"
    dim: int = 256

@dataclass
class TrainConfig:
    layers: List[LayerConfig] = field(default_factory=lambda: [
        LayerConfig(type="attention", dim=256),
        LayerConfig(type="mlp", dim=512),
    ])

CLI overrides for nested collections:

# List elements by index
python train.py model.layers.0.dim=1024

# Dict elements by key
python train.py model.heads.attention.dim=512

# Append to list
python train.py +model.layers.2.type=conv +model.layers.2.dim=512

_target_ / instantiate

@dataclass
class LoRAConfig:
    _target_: str = "__main__.LoRAConfig"
    rank: int = 8
    alpha: int = 16

    def __post_init__(self):
        self.scaling = self.alpha / self.rank

# Deferred instantiate via OmegaConf round-trip (100% compat)
import hydra.utils
oc = hydra_typing.to_omegaconf(cfg.model.lora)
lora = hydra.utils.instantiate(oc)

HydraConfig — typed built-in config

@dataclass
class TrainConfig:
    hydra: HydraConfig = field(default_factory=HydraConfig)

# Auto-populated:
cfg.hydra.run.dir          # "outputs/2026-08-05/15-24-20"
cfg.hydra.job.name         # "train"
cfg.hydra.overrides.task   # ["model=large", "lr=0.001"]

vs Hydra

API

hydra_typing.patch()           # make @hydra.main typed (call once)
hydra_typing.hydra_main(...)   # explicit decorator
hydra_typing.load_config(...)  # programmatic (notebooks)
hydra_typing.to_plain(cfg)     # dataclass → dict
hydra_typing.to_omegaconf(cfg) # dataclass → OmegaConf DictConfig (100% compat)

About

This project was built to scratch a personal itch: I wanted Hydra's YAML composition and CLI, but with real typed configs I can trust my IDE with. I'm not yet writing the actual training code — but I want the config management to be clean from day one.

This code was written entirely by Claude Code (Anthropic) using the DeepSeek API. I acted as the product manager — specifying what the library should do, reviewing the output, and iterating. The implementation, tests, examples, and documentation were all generated by Claude.

License

MIT