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Training Recipes

Recipes aim at providing a simple interface to easily reproduce trainings.

Prerequisites - Introduction to Configuration Files

Training from a Recipe

As explained in our introduction to configuration files, SuperGradients uses the hydra library combined with .yaml recipes to allow you to easily customize the parameters.

The basic syntax to train a model from a recipe is a follows 

python -m super_gradients.train_from_recipe --config-name=<config-name>
With <config-name> corresponding to the name of the recipe.

You can find all of the pre-defined recipes in super_gradients/recipes. Recipes usually contain information about their performance, as well as the command to execute them in the header.

Examples

Customize Training

You may often need to modify certain parameters within a recipe and there are 2 approaches for this: 1. Using hydra overrides. 2. Modifying the recipe.

1. Hydra Overrides

Hydra overrides allow you to change parameters directly from the command line. This approach is ideal when want to quickly experiment changing a couple of parameters.

Here's the general syntax:

python -m super_gradients.train_from_recipe --config-name=<config-name> param1=<val1> path.to.param2=<val2>
  • Parameters - Listed without the -- prefix.
  • Full Path - Use the entire path in the configuration tree, with each level separated by a ..

Example

Suppose your recipe looks like this: 

training_hyperparams:
  max_epochs: 250
  initial_lr: 0.1
  ...

dataset_params:
  data_dir: /local/mydataset
  ...

... # Many other parameters

Changing Epochs or Learning Rate 

python -m super_gradients.train_from_recipe --config-name=<config-name> training_hyperparams.max_epochs=250 training_hyperparams.initial_lr=0.03

Changing the Dataset Path 

python -m super_gradients.train_from_recipe --config-name=<config-name> dataset_params.data_dir=<path-to-dataset>

Note: Parameter names may differ between recipes, so please check the specific recipe to ensure you're using the correct names.

2. Modifying the Recipe

If you are working on a cloned version of SuperGradients (git clone ...) then you can directly modify existing recipes.

If you installed SuperGradients with pip, then you won't have the ability to modify predefined recipes. Instead, you should create your own recipe in your project, but you will still have the ability to build it on top of predefined recipes from SuperGradients.

We explain all of this in a following tutorial, but we strongly recommend you to first finish this tutorial, as it includes information required to fully understand how it works.

Recipe Structure

When browsing the YAML files in the recipes directory, you'll notice that some files contain the key defaults at the beginning of the file. Here's an example of what this looks like:

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

architecture: resnet18

train_dataloader: cifar10_train # Optional, see comments below
val_dataloader: cifar10_val # Optional, see comments below

multi_gpu: Off
num_gpus: 1

experiment_suffix: ""
experiment_name: cifar10_${architecture}${experiment_suffix}

This is a minimal example of the recipe file that contains all mandatory properties to train a model.

Components of a Recipe

We need to introduce some terminology to ensure we stay on the same page throughout the rest of this document.

  • Defaults: The defaults section is critical, and it leverages the OmegaConf syntax. It serves to reference other recipes, allowing you to create modular and reusable configurations.
  • Referencing Parameters: This allows you to point to specific parameters in the YAML file according to where they originate. For example, training_hyperparams.initial_lr refers to the initial_lr parameter from the cifar10_resnet_train_params.yaml file.
  • Recipe Parameters - _self_: The _self_ keyword has a special role. It permits the current recipe to override the defaults. Its impact depends on its position in the defaults list.

A recipe consists of a several sections that are mandatory and required to exist in the recipe file. They are:

  • training_hyperparams - This section contains the hyperparameters related to training regime, such as the learning rate, number of epochs, etc.
  • dataset_params - This section contains the parameters related to the dataset and dataloaders for training and validation. Dataset transformations, batch size, etc. are defined here. The dataset_params section is tightly coupled with the root parameters train_dataloader and val_dataloader. Please note, that train_dataloader and val_dataloader are optional, not mandatory parameters in a broad sense. They are used in conjunction to instantiate the dataloaders for training and validation and exists mostly for convenience purposes in SG-provided recipes. For external datasets we suggest read the Using Custom Datasets section of Datasets documentation page for additional information.
  • arch_params - This section contains the parameters related to the model architecture. The arch_params section goes hand-in-hand with the architecture parameter, which is root property of the recipe. If architecturedefines the specific model architecture, then arch_params defines the parameters for that architecture.
  • checkpoint_params - This section contains the parameters related to checkpoints. It contains settings for loading checkpoint weights for transfer learning, controlling use of pretrained weights and more. See default_checkpoint_params for an example of what parameters are supported.
  • variable_setup: This section is required to enable use of shortcuts for most commonly used overrides which is covered in the next section. Please note it variable_setup must be the last item in the defaults list.

Understanding Override Order

🚨 Warning: The order of items in the defaults section is significant! The overwrite priority follows the list order, meaning that a config defined higher in the list can be overwritten by one defined lower in the list. This is a vital aspect to be aware of when constructing recipes. For a more detailed explanation, please refer to the official documentation.

Organizing Your Recipe Folder

Your recipe folder should have a specific structure to match this composition:

├─ cifar10_resnet.yaml
├─ ...
├─training_hyperparams
│   ├─ cifar10_resnet_train_params.yaml
│   └─ ...
├─dataset_params
│   ├─ cifar10_dataset_params.yaml
│   └─ ...
├─arch_params
│   ├─ resnet18_cifar_arch_params.yaml
│   └─ ...
└─checkpoint_params
    ├─ default_checkpoint_params.yaml
    └─ ...

You're not restricted to this structure, but following it ensures compatibility with SuperGradients' expectations.

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.

Conclusion

This tutorial has introduced you to the world of training recipes within SuperGradients. Specifically, you've learned: - How to Train Models: Utilizing .yaml recipes to effortlessly train and customize models. - Ways to Customize Training: Tailoring your training through hydra overrides or direct modifications to the recipes. - Understanding Recipe Structure: Grasping the organization and conventions that help you align with SuperGradients' expectations.

We've laid the groundwork for understanding how recipes enable flexible and reproducible training.

Next Step: In the next tutorial, we'll explore factories in SuperGradients, revealing how they work with recipes to dynamically instantiate objects. It's a critical step in leveraging the full power of SuperGradients for your unique needs.