AI Runtime example notebooks

Important

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AI Runtime provides serverless GPU compute for inference, training, and fine-tuning AI and deep learning models. The pages below group example notebooks by task: classic ML, recommendation systems, computer vision, post-training open-source LLMs, batch inference, and multi-GPU distributed training.

Task	Description
Classic machine learning	Examples for traditional machine learning tasks including XGBoost training, zero-shot tabular prediction, and time series forecasting.
Recommendation systems	Examples for building recommendation systems using modern deep learning approaches like two-tower models.
Computer vision	Examples for computer vision tasks including object detection and image classification.
Post-training OSS models (LLMs)	Examples for fine-tuning and post-training open-source large language models, including parameter-efficient methods.
Batch inference	Examples for large-scale batch inference with Ray Data and vLLM across multiple GPUs.
Multi-GPU distributed training	Examples for scaling training across multiple GPUs and nodes using the Serverless GPU API.