#2222nd of 120 in ML frameworks and libraries
onnxruntime
Cross-platform inference engine for ONNX models with hardware accelerator support
- Stars
- 22.2k
- License
- MIT
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- C++
19,528 on 27 Mar 202622,156 on 23 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
ONNX Runtime runs ONNX models across different hardware, drivers and operating systems, applying graph optimizations and using hardware accelerators where available. It takes models exported from PyTorch, TensorFlow/Keras and classical libraries such as scikit-learn, LightGBM and XGBoost. A separate training mode accelerates multi-node NVIDIA GPU training of transformer models with a one-line change to existing PyTorch scripts.
Who it is for: Engineers deploying trained models on varied hardware
Strengths
- Accepts models from PyTorch, TensorFlow/Keras, scikit-learn, LightGBM and XGBoost via ONNX
- Runs on multiple hardware targets and operating systems with graph optimizations
- MIT license, with docs, roadmap and companion example repositories
- Training acceleration needs one added line in existing PyTorch scripts
Weaknesses
- Library, not a server: README describes no built-in HTTP API
- Models must be converted to ONNX format first
- README lists no execution providers, language bindings or system requirements
- May collect usage data and send it to Microsoft; see privacy statement
What it needs
- GPU optional
- Docker
- Models: ONNX, PyTorch, TensorFlow/Keras, scikit-learn, LightGBM
Quick answers
Is onnxruntime open source?
Yes. Its code is on GitHub under the MIT license.
Can you run onnxruntime with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Does onnxruntime need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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