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Sure, I'd be happy to provide you with a tutorial on how to save the best model using PyTorch Lightning. PyTorch Lightning is a lightweight PyTorch wrapper that simplifies the training process and provides various functionalities, including model checkpointing. Saving the best model during training is essential to ensure that you retain the model with the highest performance on your validation set.
Here's a step-by-step tutorial with a code example:
Make sure you have PyTorch Lightning installed. You can install it using pip:
In your Python script or Jupyter notebook, import the required libraries:
Define your PyTorch Lightning module, which is a subclass of LightningModule. For this example, let's create a simple neural network for demonstration purposes:
Prepare your dataset and create PyTorch data loaders:
Create a Lightning Trainer with appropriate configurations, including the checkpoint callback to save the best model:
Train your model using the created trainer:
During training, the ModelCheckpoint callback will save the best model based on the specified monitoring metric (val_loss in this example).
Once training is complete, you can load the best model using the following code:
Replace SimpleModel with the actual class name of your model.
That's it! Now you have a PyTorch Lightning script that saves the best model during training. Adjust the code according to your specific use case and model architecture.
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