Update the docs to reflect the variable_setup

documentation/source/configuration_files.md

@@ -132,14 +132,15 @@ In the experiment directory a `.hydra` subdirectory will be created. The configu
 Two Hydra features worth mentioning are _YAML Composition_ and _Command-Line Overrides_.
 
 #### YAML Composition
-If you brows the YAML files in the `recipes` directory you will see some file containing the saved-key `defaults:` at the beginning of the file.
+If you browse the YAML files in the `recipes` directory you will see some file containing the saved-key `defaults:` at the beginning of the file.
 ```yaml
 defaults:
   - training_hyperparams: cifar10_resnet_train_params
   - dataset_params: cifar10_dataset_params
   - arch_params: resnet18_cifar_arch_params
   - checkpoint_params: default_checkpoint_params
   - _self_
+  - variable_setup
 
 ```
 The YAML file containing this header will inherit the configuration of the above files. So when building a training recipe, one can structure
@@ -161,6 +162,39 @@ initial learning-rate. This feature is extremely usefully when experimenting wit
 Note that the arguments are referenced without the `--` prefix and that each parameter is referenced with its full path in the 
 configuration tree, concatenated with a `.`.
 
+##### Command-Line Override Shortcuts
+
+Although you can override any parameter from the command line, writing the full path of the parameter can be tedious.
+For example, to change the learning rate one would have to write `training_hyperparams.initial_lr=0.02`. 
+To change the batch size one would have to write
+`dataset_params.train_dataloader_params.batch_size=128 dataset_params.val_dataloader_params.batch_size=128`.
+
+To make it easier, we have defined a few shortcuts for the most common parameters that aims to reduce the amount of typing required:
+
+* Learning rate: `lr=0.02` (same as `training_hyperparams.initial_lr=0.02`)
+* Batch size: `bs=128` (same as `dataset_params.train_dataloader_params.batch_size=128 dataset_params.val_dataloader_params.batch_size=128`)
+* Number of train epochs: `epochs=100` (same as `training_hyperparams.max_epochs=100`)
+* Number of workers: `num_workers=4` (same as `dataset_params.train_dataloader_params.num_workers=4 dataset_params.val_dataloader_params.num_workers=4`)
+* Resume training for a specific experiment: `resume=True` (same as `training_hyperparams.resume=True`)
+* Enable or disable EMA: `ema=true` (same as `training_hyperparams.ema=true`)
+
+To use these shortcuts, a `variable_setup` section should be a part of hydra defaults in the recipe file.
+Please note it `variable_setup` **must be the last item** in the defaults list:
+
+```yaml
+defaults:
+  - training_hyperparams: cifar10_resnet_train_params
+  - dataset_params: cifar10_dataset_params
+  - arch_params: resnet18_cifar_arch_params
+  - checkpoint_params: default_checkpoint_params
+  - _self_
+  - variable_setup
+
+...
+```
+
+More informaiton can be found in corresponding YAML file in the `recipes` directory:
+https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/recipes/variable_setup.yaml
 
 ## Resolvers 
 Resolvers are converting the strings from the YAML file into Python objects or values. The most basic resolvers are the Hydra native resolvers.