hydra-torch
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pytorch
Configuration classes enabling type-safe PyTorch configuration for Hydra apps
See #72 for discussion and template on how to do this.
Adding these as they might be useful for configuring 'backbones' for larger compositional models.
e.g. if `MNIST` inherits from `Datset`, `MNISTConf` should probably be a subclass of `DatasetConf`.
Assuming this approach is agreed upon within the onging torchvision datasets PR, do this for the `hydra-configs-torch` as well.
Will do this after outstanding config PRs are merged to limit possible conflicts.
We intend to release versions of singular project packages - think `hydra-configs-torch` or `hydra-configs-torchvision` via release branches that get tagged for upload to PyPI. This enables users to get the...
This is low priority since much of this can be achieved by reading the documentation in this larger repo, but eventually it might be nice to make it stand alone....
Write functions of the form: `hydra_configs.torch.register()`, `hydra_configs.torch.optim.register()`, etc. Within these functions, call config store API: ```python cs = ConfigStore.instance() cs.store(name="adamconf", node=AdamConf) ``` Call these in `__init__.py` for the module.
In general, we need to discuss how to test instantiation of 'Distributed' classes. This is one class we can configure, but have not experimented with tests for yet.
This test requires improvements to hydra's `instantiate`. Namely being able to instantiate with non-keyword passthrough arguments.