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ONNX Runtime Inference

⬢ NIVÅ 2Tekniskt
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3 månader
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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.

Vad är 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.

🔧 VERKTYG & EKOSYSTEM
ONNX RuntimeTensorRTOpenVINOCUDADockerKubernetesPrometheus/GrafanaApache Beam

💰 Lön per region

OmrådeNybörjareMidErfaren
USA$90k$150k$240k
UK£55k£95k£150k
EU€60k€105k€160k
CANADAC$95kC$155kC$250k

⚖ Jämför med

Onnx Model Format

❓ Vanliga frågor

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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