Σημείωμα
Η πρόσβαση σε αυτήν τη σελίδα απαιτεί εξουσιοδότηση. Μπορείτε να δοκιμάσετε να εισέλθετε ή να αλλάξετε καταλόγους.
Η πρόσβαση σε αυτήν τη σελίδα απαιτεί εξουσιοδότηση. Μπορείτε να δοκιμάσετε να αλλάξετε καταλόγους.
These examples train computer vision models on AI Runtime. They cover image classification, hyperparameter tuning with Ray Tune, and object detection.
| Example | Interface | Description |
|---|---|---|
| Image classification using convolutional neural network | Notebook | This notebook provides a simple example of how to train a 2-D convolution neural network on AI Runtime for image classification. |
| CIFAR-10 hyperparameter tuning with Ray Tune | Notebook | 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 | Notebook | This notebook demonstrates how to train an object detection model using RetinaNet on AI Runtime. |
| Object detection using YOLO11n | Notebook | This notebook demonstrates how to train a YOLO11n object detection model on the COCO128 dataset using AI Runtime, with MLflow tracking and Model Serving deployment. |
| ResNet-50 fine-tuning on Imagenette | CLI | Use Ray Train to fine-tune ResNet-50 with one distributed data parallel worker per A10 GPU across two nodes and log metrics to MLflow. |