Scaling Inference Deployments with NVIDIA Triton Inference Server and Ray Serve
summary
Integration embeds the Triton Python API inside Ray Serve deployments so Python decorators launch Triton-managed models (PyTorch/ONNX/TensorRT) with zero‑copy transfers, Prometheus metrics and FastAPI endpoints. A stable diffusion demo shows using the TensorRT backend yields about 9–10% single‑GPU throughput improvement, and an autoscaling run up to eight GPUs shows roughly a 45% improvement during scale‑out; model repo swapping and containerized deployments work without code rewrites.
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chapters (9)
- 0:00Introduction
- 0:52Ray Serve Architecture
- 5:15Triton Architecture
- 9:42Integration
- 11:15Python API
- 15:15Model Repository
- 18:40Scaling with Ray Serve
- 21:29Summary
- 23:20Questions
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