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PyTorch Lightning is a lightweight PyTorch wrapper that provides a high-level interface for training deep learning models. One of the key features of PyTorch Lightning is its built-in scheduler, which allows you to dynamically adjust learning rates during training. In this tutorial, we will explore how to use the scheduler in PyTorch Lightning with a code example.
Before we begin, make sure you have PyTorch and PyTorch Lightning installed. You can install them using the following commands:
Let's create a simple neural network model for this tutorial. In this example, we'll use a basic feedforward neural network.
Now, let's create a PyTorch Lightning module that includes the model, training, and validation steps.
In this example, we use the StepLR scheduler, which adjusts the learning rate by a factor (gamma) after a certain number of epochs (step_size).
For demonstration purposes, let's use a dummy dataset. Replace this part with your actual dataset and DataLoader setup.
Now, instantiate the Lightning model and train it using the Trainer class.
In this example, the learning rate will be adjusted using the StepLR scheduler every 10 epochs with a gamma factor of 0.1.
This is a basic example of using a scheduler in PyTorch Lightning. Depending on your use case, you might want to explore other schedulers provided by PyTorch or even create your custom scheduler by extending the PyTorch lr_scheduler class.
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