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PyTorch Lightning is a lightweight PyTorch wrapper that simplifies the training process of deep learning models. One essential feature is the ability to save and load model checkpoints, enabling you to resume training or perform inference on pre-trained models. This tutorial will guide you through the process of loading a model from a checkpoint using PyTorch Lightning.
Before you begin, make sure you have PyTorch Lightning and any other necessary dependencies installed:
Let's start by creating a simple PyTorch Lightning model. For this tutorial, we'll use a basic example with a dummy neural network.
Next, create a PyTorch Lightning Trainer. This class manages the training loop, including saving and loading checkpoints.
Now, train the model using the PyTorch Lightning Trainer you created.
During training, PyTorch Lightning automatically saves model checkpoints by default. However, you can customize this behavior by configuring the ModelCheckpoint callback. The saved checkpoints include the model's state, optimizer state, and other necessary information.
To load a model from a checkpoint, use the load_from_checkpoint method provided by PyTorch Lightning. This method takes the path to the checkpoint file as an argument.
Now you have successfully loaded your PyTorch Lightning model from a checkpoint. You can use the loaded_model for inference or resume training.
This tutorial provides a basic overview of loading models from checkpoints in PyTorch Lightning. For more advanced features and customization options, refer to the official PyTorch Lightning documentation: https://pytorch-lightning.readthedocs....
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