Configuration classes enabling type-safe PyTorch configuration for Hydra apps.
This repo is work in progress.

The config dataclasses are generated using configen, check it out if you want to generate config dataclasses for your own project.

# For now, please obtain through github. Soon, versioned (per-project) dists will be on PyPI. pip install git+https://github.com/pytorch/hydra-torch 

Here is one of many configs available. Notice it uses the defaults defined in the torch function signatures:

@dataclass class TripletMarginLossConf: _target_: str = "torch.nn.modules.loss.TripletMarginLoss" margin: float = 1.0 p: float = 2.0 eps: float = 1e-06 swap: bool = False size_average: Any = None reduce: Any = None reduction: str = "mean"

from hydra_configs.<package_name>.path.to.module import <ClassName>Conf

where <package_name> is the package being configured and path.to.module is the path in the original package.

Inferring where the package is located is as simple as prepending hydra_configs. and postpending Conf to the original class import: e.g.

#module to be configured from torch.optim.adam import Adam #config for the module from hydra_configs.torch.optim.adam import AdamConf

Take a look at our tutorial series:

1. Basic Tutorial
2. Intermediate Tutorial (coming soon)
3. Advanced Tutorial (coming soon)

A list of projects following the hydra_configs convention (please notify us if you have one!):

Pytorch Lightning

hydra-torch is licensed under MIT License.