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These notebooks train computer vision models on AI Runtime. They cover image classification, hyperparameter tuning with Ray Tune, and object detection.
| Tutorial | Description |
|---|---|
| Image classification using convolutional neural network | This notebook provides a simple example of how to train a 2-D convolution neural network on serverless GPUs for image classification. |
| CIFAR-10 hyperparameter tuning with Ray Tune | This notebook runs concurrent fractional-GPU trials for a PyTorch image classifier and uses the asynchronous successive halving algorithm (ASHA) to stop underperforming configurations early. |
| Object detection using RetinaNet | This notebook demonstrates how to train an object detection model using RetinaNet on serverless GPU. |
| Object detection using YOLO11n | This notebook demonstrates how to train a YOLO11n object detection model on the COCO128 dataset using serverless GPU, with MLflow tracking and Model Serving deployment. |