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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.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Scalable AI Models with PyTorch Lightning

ดูคอร์ส

คำแนะนำการฝึกหัด

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

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# 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)
แก้ไขและรันโค้ด