chudegao/onnxruntime

Analysis updated 2026-07-18 · repo last pushed 2021-09-30

Audience · developerComplexity · 3/5DormantSetup · moderate

Mindmap

mindmap
  root((repo))
    What it does
      Runs trained models fast
      Optimizes computation graph
      Uses GPUs when available
    Tech stack
      ONNX
      PyTorch
      TensorFlow
      XGBoost
    Use cases
      Speed up production inference
      Run models on mobile offline
      Accelerate multi-GPU training
    Audience
      ML engineers
      Mobile developers
      Researchers
    Platforms
      Windows Linux Mac
      Mobile and WebAssembly

mindmap root((repo)) What it does Runs trained models fast Optimizes computation graph Uses GPUs when available Tech stack ONNX PyTorch TensorFlow XGBoost Use cases Speed up production inference Run models on mobile offline Accelerate multi-GPU training Audience ML engineers Mobile developers Researchers Platforms Windows Linux Mac Mobile and WebAssembly

What do people build with it?

USE CASE 1

Speed up millisecond-level predictions for a production recommendation engine.

USE CASE 2

Run object detection locally on a mobile phone without calling the cloud.

USE CASE 3

Add one line to existing PyTorch training code to accelerate training on multi-GPU clusters.

What is it built with?

ONNXPyTorchTensorFlowXGBoostscikit-learnC++

How does it compare?

	chudegao/onnxruntime	0verflowme/alarm-clock	0verflowme/seclists
Language	—	CSS	—
Last pushed	2021-09-30	2022-10-03	2020-05-03
Maintenance	Dormant	Dormant	Dormant
Setup difficulty	moderate	easy	easy
Complexity	3/5	2/5	1/5
Audience	developer	vibe coder	ops devops

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

GPU acceleration and multi-platform builds may require extra setup depending on target device.

No license information was found in the explanation.

Copy-paste prompts

Prompt 1

Show me how to export my trained PyTorch model to ONNX and run it with ONNX Runtime for faster inference.

Prompt 2

Help me deploy an ONNX Runtime model on a mobile app so it runs object detection offline.

Prompt 3

Explain how ONNX Runtime optimizes a model's computational graph to speed up inference.

Prompt 4

Walk me through adding ONNX Runtime training acceleration to my existing multi-GPU PyTorch training script.

Frequently asked questions

What is onnxruntime?

A Microsoft open-source tool that makes trained machine learning models run faster and more efficiently across laptops, servers, mobile devices, and browsers.

Is onnxruntime actively maintained?

Dormant — no commits in 2+ years (last push 2021-09-30).

What license does onnxruntime use?

No license information was found in the explanation.

How hard is onnxruntime to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is onnxruntime for?

Mainly developer.

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