Onnxruntime-Inference-Examples

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onnxruntime-inference-examples is an Open Source Coding & Development tool that provides examples for using ONNX Runtime for machine learning inferencing. It offers practical demonstrations across various programming languages and platforms.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is onnxruntime-inference-examples?

onnxruntime-inference-examples is a GitHub repository offering a collection of practical examples for leveraging ONNX Runtime (ORT) in machine learning inferencing. It serves as a valuable resource for developers and data scientists looking to integrate and optimize machine learning models across different environments. The repository includes examples for C/C++, mobile applications, JavaScript APIs, and quantization techniques for CPU and TensorRT Execution Providers. These examples demonstrate how to effectively utilize ORT's capabilities, making it easier to implement efficient inference solutions. The project welcomes contributions and adheres to Microsoft's Open Source Code of Conduct.

Best used for

Ideal for developers who need to understand and implement ONNX Runtime for machine learning inferencing, deploy models on mobile platforms, and optimize model performance through quantization. Especially valuable for those working with C/C++, JavaScript, or mobile development environments.

Common actions

implement machine learning inference

optimize ONNX models

integrate ONNX Runtime

develop mobile AI applications

collaborationworkflowslow-code/no-codeautomated workflowdeepfakegithub copilotopen-sourceface swapping"AI Agents"

Capabilities

Key features

C/C++ API examples
Mobile application examples
JavaScript API examples
Quantization examples
Multiple programming languages

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages are covered in the ONNX Runtime inference examples?

The repository includes examples for a variety of programming languages and environments, such as C/C++, C#, JavaScript, Python, Kotlin, and Swift. This broad coverage allows developers to find relevant examples for their preferred development stack.

Do the examples include mobile application integration for ONNX Runtime?

Yes, the repository features dedicated examples that demonstrate how to effectively use ONNX Runtime within mobile applications. These examples are crucial for developers aiming to deploy machine learning models on mobile devices.

Are there examples for optimizing ONNX models using quantization?

Absolutely. The repository provides specific examples illustrating how to apply quantization techniques for both CPU Execution Providers and TensorRT Execution Providers. This helps in reducing model size and improving inference performance.

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