# Running Ray on TPU: AI Libraries Explained Leveraging Ray Serve, Ray Data, and Ray Train for efficient TPU utilization. - **Source:** [Google Developers Blog](https://brifing.si/source/1e914d4a-9164-4a1a-82cb-3d169fbad877) - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/fa0613fc-8673-4073-8191-177f1630f465 ## TPU and Ray Integration Ray provides an optimized platform for running high-performance ML workloads on TPUs. The unique hardware structure of TPU slices is managed by Ray Core's slice_placement_group() primitive. This foundational layer creates a robust infrastructure for higher-level libraries. ## Model Deployment with Ray Serve Ray Serve is ideal for deploying large language models on TPUs. With vLLM integration, it enables high-throughput inference. The topology definition is critical for tensor-parallel models. A single YAML field can determine deployment success. ## Data Loading Optimization Ray Data's iter_jax_batches function optimizes the data pipeline to match TPU speeds. It loads JAX arrays directly to devices, eliminating data feeding bottlenecks. This feature is perfect for large-scale training and batch inference jobs. ## Simplified Distributed Training JaxTrainer simplifies distributed training on TPUs. It automatically manages topology definitions and slice shapes. Ray Train's fault tolerance and checkpointing features make long TPU training sessions reliable. Multi-slice support provides scalability. ## TPU Hardware Advantages TPUs are often more cost-effective and available than GPUs for large models. A single TPU host can run models that would require multiple GPUs. Ray's TPU support makes it easy to leverage these hardware advantages at the software layer. ## Production Deployment Tips RayService is a better choice than RayCluster for production environments. You can quickly deploy using TPU images on GKE. Official tutorials provide step-by-step guidance for deploying popular models like Llama 3 and Mistral on TPUs. ## Debugging Guide To avoid common TPU errors, import JAX inside worker functions. Forgetting the topology definition can cause deployments to hang indefinitely. Ray's error messages and documentation are valuable troubleshooting resources. ## Future Potential Ray's TPU support is becoming a key enabler for large-scale AI projects. Particularly for training and deploying very large language models, Ray libraries simplify complex infrastructure management, making researchers' and engineers' jobs easier. ## Sources covering this story - https://developers.googleblog.com/run-ray-on-tpu-part-2-ray-ai-libraries/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._