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Optimization is a critical step in the deep learning process as it determines how well the network will learn from the training data. SuperGradients supports out-of-the-box pytorch optimizers( SGD, Adam, AdamW, RMS_PROP), but also RMSpropTF and Lamb.

Set the optimizer in the code

Optimizers should be part of the training parameters.

from super_gradients import Trainer

trainer = Trainer(...)

    training_params={"optimizer": "Adam", "optimizer_params": {"eps": 1e-3}, ...}, 

Note: The optimizer_params is a dictionary of all the optimizer parameters you want to set. It can be any argument defined in the optimizer __init__ method , except for params because this argument corresponds to the model to optimize and is automatically provided by the Trainer.

Set the optimizer in the recipes

When working with recipes, you need to modify the recipes/training_hyperparams as below:

# recipes/training_hyperparams/my_training_recipe.yaml


optimizer: Adam
  eps: 1e-3

Use Custom Optimizers

If your own optimizer is not natively supported by SuperGradients, you can always register it!

from super_gradients.common.registry.registry import register_optimizer

class CustomOptimizer:
    def __init__(
            params, # This arg is the only required regardless of your optimizer, the rest depends on your optimizer. 
            alpha: float, 
            betas: float
        defaults = dict(alpha=alpha, betas=betas)
        super(CustomOptimizer, self).__init__(params, defaults)


And then update your training hyperparameters:

# my_training_hyperparams.yaml


optimizer: CustomOptimizer
  alpha: 1e-3
  betas: 1e-3

Customize learning rate for different model blocks

You can define the learning rate to use on each section of your model by working with initialize_param_groups and update_param_groups. - initialize_param_groups defines the groups, and the learning rate to use for each group. It is called on instantiation. - update_param_groups updates the learning rate for each group. It is called by LR callbacks (such as LRCallbackBase) during the training.

If your model (i.e. any torch.nn.Module) is lacking these methods, the same learning rate will be applied to every block. But if you implement them, it will be taken into account by the Trainer just like with any other SuperGradients model.


Assuming that you have your own custom model and that you want work with a different learning rate on the backbone.

You first need to implement the initialize_param_groups and update_param_groups accordingly.

import torch
from super_gradients.common.registry.registry import register_model

@register_model() # Required if working with recipe  
class MyModel(torch.nn.Module):


    def initialize_param_groups(self, lr: float, training_params) -> list:
        backbone_params = {
            "named_params": self.backbone.named_parameters(),
            "lr": lr * training_params['multiply_backbone_lr'] # You can use any parameter, just make sure to define it when you set up training_params

        decoder_named_params = list(self.decoder.named_parameters())
        aux_head_named_parameters = list(self.aux_head.named_parameters())
        layers_params = {
            "named_params": decoder_named_params + aux_head_named_parameters,
            "lr": lr  
        param_groups = [backbone_params, layers_params]
        return param_groups

    def update_param_groups(self, param_groups: list, lr: float, epoch: int, iter: int, training_params, total_batch: int) -> list:
        Update the params_groups defined in initialize_param_groups
        param_groups[0]["lr"] = lr * training_params['multiply_backbone_lr']

        param_groups[1]["lr"] = lr

        return param_groups
Note: If working with recipe, don't forget to register your model.

Now you just need to set a value for multiply_backbone_lr in the training recipe.

# my_training_hyperparams.yaml


multiply_backbone_lr: 10 # This is used in our implementation of initialize_param_groups/update_param_groups
optimizer: OptimizerName # Any optimizer as described in the previous sections
optimizer_params: {} # Any parameter for the optimizer you chose