featured · github
ONNX Runtime: Ship ML Models Anywhere, Once
Deploy trained models to CPU, GPU, or edge devices without rewriting inference code. 2–5x faster than native frameworks.
microsoft/onnxruntime ↗You train a model in PyTorch or TensorFlow. Then you need to run it in production—on a server, a phone, a browser, or an IoT device. ONNX Runtime lets you export that model once and run it everywhere without rewriting anything.
Here's the win: a single compiled format, optimized inference across hardware tiers, and measurable speed gains (typically 2–5x latency reduction). You write the model training logic once, the deployment logic once. No more "works in Colab, breaks in production" surprises.
Matters most if you're shipping inference-heavy features—recommendations, real-time vision, on-device ML—where latency or per-inference cost directly affects UX or margin.
Share kit
ONNX Runtime: train once, deploy everywhere (2–5x faster)
Export a PyTorch or TensorFlow model. Run it on CPU, GPU, mobile, or edge hardware without touching the inference code again. ONNX Runtime is the practical answer to multi-target model deployment—mature ecosystem, real speed gains, no reinvention required.
ONNX Runtime: you train the model once. you deploy it everywhere. CPU, GPU, phone, edge—same compiled format, same code path. 2–5x faster inference than native frameworks. if you ship ML features, this cuts deployment friction and latency in half. github.com/microsoft/onnxruntime
If you're shipping ML inference in production, ONNX Runtime solves a real problem: write your model once, deploy it everywhere (CPU, GPU, mobile, edge) without rewriting your inference stack. Measured wins: 2–5x latency reduction, one codebase instead of three. Worth the 30-min onramp if you're building model-driven features.
Just integrated ONNX Runtime into a model serving pipeline. One thing stood out: took a PyTorch model trained in-house, compiled it once, deployed the same binary to CPU servers, GPU boxes, and edge devices. No rewrites. Inference latency dropped 2-5x depending on hardware. ONNX Runtime handles the translation layer between frameworks (PyTorch, TensorFlow, etc.) and silicon. You get a single inference engine that speaks both the model format and the hardware—CPU, GPU, quantized edge chips, the works. Why this matters: most teams end up maintaining separate deployment paths per target. ONNX collapses that. Write once, optimize once, ship everywhere. If you're moving models from research to production and tired of framework lock-in, it's worth the Friday afternoon spike. https://github.com/microsoft/onnxruntime
spent the day with onnxruntime. took a pytorch model, compiled to onnx once, ran it on cpu / gpu / edge without touching the code again. 2-5x latency cut. no framework rewrites. this is how you actually ship ml to production. https://github.com/microsoft/onnxruntime