AI Runtime example notebooks

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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.

• • 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.