Rosario Scalise

Pickup where we left off in Basic Tutorial To address: * Configuring the model * Configuring the dataset * Swapping in and out different Optimizers/Schedulers Another thing to think about...

Quick fix to address the issue encountered in: #12 .

This working properly currently depends on @awaelchli 's fix here: https://github.com/PyTorchLightning/pytorch-lightning/pull/5155 Ironing out a few details to clean it up. Testing is a good idea too.

This has been coming up frequently in different groups. Provide a possible MWE and a centralized place to have community members provide suggestions.

Probably add a simple one until recursive instantiation is fully supported in hydra.

Include a basic example of configuring callbacks.

Modify `mnist_00.py` to use lightning example with lightning configuration classes.

Ensure basic MNIST Lightning example runs with latest hydra / latest lightning.

Quoting @omry: Something like: ``` trainer: _target_: Trainer callback: _target_: hydra.utils.get_method path: my_callback ``` Another possibility using existing hydra methods: ``` _target_ : foo.Foo callback: ${get_function:my_callback} ```