PyTorch Hyperparameter Optimization Guide

In PyTorch, you can optimize model hyperparameters using either PyTorch Lightning or the torch.optim module.

1. Optimize hyperparameters using PyTorch Lightning.

PyTorch Lightning offers a convenient interface for hyperparameter optimization, where you can use the Trainer class and its built-in scheduler to adjust hyperparameters. Initially, you need to define a LightningModule class and then pass in the relevant parameters in the Trainer for optimization. For example:

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger

# Define your LightningModule
class MyLightningModule(pl.LightningModule):
    def __init__(self, **hparams):
        super().__init__()
        # Define your model architecture
        
    def training_step(self, batch, batch_idx):
        pass
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=self.hparams['learning_rate'])

# Define hyperparameters and logger
hparams = {
    'learning_rate': 0.001,
    # other hyperparameters
}
logger = TensorBoardLogger(save_dir="logs", name="experiment_name")

# Instantiate Trainer
trainer = Trainer(logger=logger, max_epochs=10, gpus=1)

# Train the model
model = MyLightningModule(**hparams)
trainer.fit(model, train_dataloader, val_dataloader)

1. Optimize hyperparameters using the torch.optim module.

If PyTorch Lightning is not used, one can directly utilize the torch.optim module to define optimizers and adjust hyperparameters. For example:

import torch
import torch.optim as optim

# Define your model and optimizer
model = MyModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Define hyperparameters
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

# Train the model
for epoch in range(num_epochs):
    # Train the model
    for batch in dataloader:
        optimizer.zero_grad()
        output = model(batch)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    
    # Adjust learning rate
    lr_scheduler.step()

These are two methods for optimizing model hyperparameters in PyTorch. You can choose the appropriate method for hyperparameter tuning based on your actual needs.