Training and evaluating

In this exercise, we'll bring together al that we've practiced so far, by training and evaluating a neural network on the real-world dataset of handwritten Ethiopian MNIST characters.

ImageClassifier is a predefined neural network model implemented using PyTorch Lightning. It consists of convolutional layers for feature extraction, activation functions for introducing non-linearity, and fully connected layers for classification.

The Ethiopic MNIST dataset was pre-imported for you.

この演習はコースの一部です

Scalable AI Models with PyTorch Lightning

コースを見る

演習の手順

Import the Trainer.
Define ImageClassifier model and trainer.
Train the model.
Evaluate the model on the validation set.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# Import the Trainer
from lightning.pytorch import ____

# Define ImageClassifier model & trainer and set epoch parameter
model = ____()
trainer = ____(max_epochs=5)

# Train the model
trainer.fit(____, train_loader, val_loader)

# Evaluate the model
val_results = trainer.____(____, val_loader)
print("Validation Accuracy:", val_results[0]["val_acc"])

コードを編集して実行

この演習はコースの一部です

Scalable AI Models with PyTorch Lightning

SkillTag.level.intermediateSkillTag.label

4.8+

無料でコースを始める

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 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

現在の演習

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