Running the Lightning Trainer

Now that you have your model defined, you are ready to configure the Lightning Trainer to run a training loop. This exercise helps you understand how to integrate your model, data loaders, and training configurations - mastering this setup is essential before moving on to more advanced training.

The torch and lightning.pytorch, imported as pl, have been preloaded for you. A model has already been initialized, using the class you defined in the previous exercise.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Scalable AI Models with PyTorch Lightning

पाठ्यक्रम देखें

अभ्यास निर्देश

Configure the Trainer with 3 epochs.
Start a training run with the predefined model.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

from lightning.pytorch import Trainer
# Configure the Trainer with max epochs
trainer = ____(max_epochs=____, devices=1)
# Start training with the predefined model
trainer.____(____, train_dataloader, val_dataloader)

कोड संपादित करें और चलाएँ

यह अभ्यास पाठ्यक्रम का हिस्सा है

Scalable AI Models with PyTorch Lightning

SkillTag.level.intermediateSkillTag.label

4.8+

पाठ्यक्रम मुफ्त में शुरू करें

In this chapter, we'll explore how PyTorch Lightning simplifies the development and deployment of scalable AI models. Starting with foundational concepts, we'll go through the core structure of a PyTorch Lightning project, including essential components like the LightningModule and Trainer, to set a strong foundation for more advanced AI solutions.

Exercise 1: Introduction to PyTorch Lightning Exercise 2: Introducing the LightningModule Exercise 3: Running the Lightning Trainer

वर्तमान अभ्यास

Exercise 4: Defining models with LightningModule Exercise 5: Usage of the LightningModule Exercise 6: Mastering the init method Exercise 7: Perfecting the forward method Exercise 8: Implementing training logic Exercise 9: Implementing the training step Exercise 10: Configuring the optimizer Exercise 11: Training and evaluating

We'll dive deeper into PyTorch Lightning to efficiently manage data and refine model training in this chapter. We'll learn how to create modular and reusable data workflows with LightningDataModule, evaluate your models accurately through validation and testing, and enhance training processes using Lightning Callbacks to automate model improvement and avoid overfitting.

Exercise 1: Managing data with LightningDataModule Exercise 2: Splitting data with LightningDataModule Exercise 3: Creating a train DataLoader Exercise 4: Incorporating validation and testing Exercise 5: Implementing the validation step Exercise 6: Evaluate model accuracy using Torchmetrics Exercise 7: Enhancing training with Lightning callbacks Exercise 8: Classifying Lightning callbacks Exercise 9: Optimizing model training with Lightning

Learn to prepare deep learning models for real-world deployment by making them leaner and faster. This chapter introduces techniques such as dynamic quantization, pruning, and TorchScript conversion, helping you reduce model size and latency without sacrificing accuracy

Exercise 1: Applying dynamic quantization Exercise 2: Apply dynamic quantization Exercise 3: Comparing quantized model performance Exercise 4: Implementing model pruning techniques Exercise 5: Apply pruning to a linear layer Exercise 6: Finalize pruning by removing the mask Exercise 7: Exporting models with TorchScript Exercise 8: Choosing the right conversion method Exercise 9: Optimizing models for scalability Exercise 10: Recap: Scalable AI Models with PyTorch Lightning