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Optimizing model training with Lightning

By implementing automated techniques like ModelCheckpoint and EarlyStopping, you'll ensure your model selects the best-performing parameters while avoiding unnecessary computations.

The dataset, a subset of the Osmanya MNIST dataset, provides a real-world use case where scalable AI training techniques can significantly improve efficiency and accuracy.

OsmanyaDataModule and ImageClassifier have been predefined for you.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Scalable AI Models with PyTorch Lightning

पाठ्यक्रम देखें

अभ्यास निर्देश

  • Import callbacks that you'll use for model checkpointing and early stopping.
  • Train the model with the ModelCheckpoint and EarlyStopping callbacks.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

# Import relevant checkpoints
from lightning.pytorch.callbacks import ____, ____

class EvaluatedImageClassifier(ImageClassifier):
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        acc = (y_hat.argmax(dim=1) == y).float().mean()
        self.log("val_acc", acc)

model = EvaluatedImageClassifier()
data_module = OsmanyaDataModule()
# Train the model with ModelCheckpoint and EarlyStopping checkpoints
trainer = Trainer(____=[____(monitor="val_acc", save_top_k=1), ____(monitor="val_acc", patience=3)])
trainer.fit(model, datamodule=data_module)
कोड संपादित करें और चलाएँ