Ryan Soklaski

https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#reproducibility ```python from pytorch_lightning import Trainer, seed_everything seed_everything(42, workers=True) # sets seeds for numpy, torch and python.random. model = Model() trainer = Trainer(deterministic=True) ```

## Summary `hydra_zen.hydrate` can be applied to a namespace/module, and return a corresponding Hydra node of configs that describe that namespace/module. In effect, the entire [`hydra-torch` project](https://github.com/pytorch/hydra-torch) could be replaced...

There is a sizeable demand for Hydra to support custom help messages through the CLI: https://github.com/facebookresearch/hydra/issues/633 It appears that the leading candidate for supporting this, at least for structured configs,...

Hello! Could you compute and publish the sha256 hashes for your `ani-1_dataset.tar.gz` file and include them in your README? This will help users to ensure that the data that they...

Scenes 41, 49, and 71 contain patches with 180-deg rotations

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Hi @rmitra I hope all is well. I discovered that my model was doing anomalously-poorly on scenes 41, 49, and 71 from your data set. After some probing, I discovered...

This should work: ```python builds(something, zen_wrappers=partial(my_wrapper, param=100)) ``` Currently, one would have to write: ```python builds(something, zen_wrappers=builds(my_wrapper, param=100, zen_partial=True)) ``` we just need to check if a wrapper is an...

Specifying a schema in terms of a Protocol

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Hello! Thanks for making `xarray-schema`! It would be great to be able to write a xarray schema in terms of a `typing.Protocol`. This would enable the schema to be used...

Currently we use `Workflow.run` within `hydra.main`, which no longer works: https://github.com/mit-ll-responsible-ai/responsible-ai-toolbox/blob/5348f7d6a96837d0f9d9ce1b5e71ebfdee6f4b88/experiments/madry/run.py#L29 We should update this to leverage `zen`, but to make sure that the plotting still works (i.e. we give...

Derived from: https://arxiv.org/pdf/1709.04114.pdf Here is a trivial scenario where we are merely perturbing the "logits" themselves so that the specified targets will be optimized for. Let's see that the longer...

https://pytorch.org/docs/stable/generated/torch.add.html ```python >>> a = torch.randn(4) >>> a tensor([ 0.0202, 1.0985, 1.3506, -0.6056]) >>> b = torch.randn(4) >>> b tensor([-0.9732, -0.3497, 0.6245, 0.4022]) >>> c = torch.randn(4, 1) >>> c...