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ONNX Runtime Inference

⬢ 層級 2技術
高
薪資影響
3 個月
學習時長
困難
難度
12
職業
一覽

ONNX Runtime Inference is the practice of running pretrained models efficiently on diverse hardware. Includes Python/C++/JavaScript APIs, hardware acceleration (GPU, TensorRT, OpenVINO), batching, memory management, and monitoring. Used by ML engineers, DevOps, and production teams. Practitioners earn 30-40% premium for inference optimization. Time to mastery: 10-14 weeks. Sits between ONNX format and production deployment.

什麼是 ONNX Runtime Inference

ONNX Runtime is Microsoft's open-source inference engine for running ONNX models efficiently on any hardware: CPUs, GPUs (CUDA/TensorRT), mobile (iOS/Android), web (WebAssembly), and edge devices. It provides language bindings (Python, C++, C#, JavaScript, Java, Go, Rust), optimization passes, hardware acceleration, and performance monitoring. Inference (running pretrained models) is production's bottleneck. ONNX Runtime optimizes latency, throughput, and memory usage. A well-tuned inference pipeline can serve 10x more requests on same hardware.

🔧 工具與生態
ONNX RuntimeTensorRTOpenVINOCUDADockerKubernetesPrometheus/GrafanaApache Beam

💰 各地區薪資

地區初級中級高級
USA$90,000$150,000$240,000
UK£55,000£95,000£150,000
EU€60,000€105,000€160,000
CANADACA$95,000CA$155,000CA$250,000

❓ 常見問題

What's the difference between ONNX Runtime and TensorRT?
ONNX Runtime = general-purpose inference engine. TensorRT = NVIDIA GPU optimization (2-10x faster on GPU). Use ONNX Runtime for CPU/multi-hardware. Use TensorRT on NVIDIA GPUs.
How do I batch requests for efficiency?
Collect requests (100 at a time). Run inference on batch. Return results. Batching increases throughput 5-10x. Trade: latency increases per request but overall system throughput soars.
Should I use GPU or CPU?
GPU if: high throughput (1000s requests/sec), batch processing. CPU if: low latency required (<10ms), single requests. GPU has startup overhead; CPU faster for single inference.
How do I handle model updates without downtime?
Blue-green deployment: run v1 and v2 simultaneously. Switch traffic gradually (10% to v2, 50%, 100%). If v2 fails, instant rollback. Critical for production.
What about quantized model inference?
ONNX Runtime supports int8 quantized models. Just load and run like float32. No code changes. 4x smaller, 3x faster, minimal accuracy loss.

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