pytorch lightning trainer documentation
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PyTorch Lightning is a lightweight PyTorch wrapper that simplifies the training loop and provides a standardized interface for deep learning projects. In this tutorial, we will focus on the PyTorch Lightning Trainer module, which is a key component for training models in a Lightning-based project. The Trainer module handles various aspects of training, such as optimization, logging, and checkpointing, allowing you to concentrate more on the model architecture and less on boilerplate code.
First, make sure you have PyTorch and PyTorch Lightning installed:
Let's create a simple example using PyTorch Lightning's Trainer module. We'll train a basic neural network on a toy dataset. Here's a step-by-step guide:
The Trainer class has various configuration options. Here are some commonly used ones:
This tutorial covered the basics of using the PyTorch Lightning Trainer module. You learned how to define a simple Lightning module, prepare a dataset, and train a model using the Trainer. Additionally, we explored advanced configurations to customize the training process.
For more details, refer to the official PyTorch Lightning documentation: https://pytorch-lightning.readthedocs...
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