microsoft/onnxruntime-inference-examples for ONNX Runtime Integration

Reference Examples for Distinct ONNX Runtime Integration Paths

This repository addresses engineers who already have ONNX models and need examples for calling ONNX Runtime from different application environments. It is not itself an inference engine. ONNX Runtime performs the inference, while this repository organizes examples that show how applications can connect to its APIs. The repository description defines the scope as machine learning inferencing, and the README states that the examples demonstrate ONNX Runtime for inference.

The collection is split by implementation surface. The `c_cxx` directory covers the C and C++ APIs. The `mobile` directory covers ONNX Runtime use in mobile applications. The `js` directory presents JavaScript API examples. The `quantization` directory focuses on quantization with CPU EP and TensorRT EP. Other top-level entries include `c_sharp`, `python`, `swift`, and `plugin_execution_providers`, but the README does not describe their contents or pipeline status. Their exact examples and support levels therefore cannot be confirmed from the repository overview.

This structure makes the project more useful than a single-language snippet collection, but it also means there is no uniform interface to learn across every directory. An engineer choosing this repository should first select the application language and execution environment. Selecting by API surface is more reliable than treating all examples as one interchangeable tutorial. The README labels the C/C++, mobile, JavaScript, and quantization groups, while leaving their deeper setup details to the individual directories.

Execution Provider Choice in the Quantization Examples

The quantization group is the most specific part of the repository description. The README identifies CPU EP and TensorRT EP as its documented targets. Those labels matter because quantization is not a portable model annotation applied identically to every backend. A reader needs the relevant quantization example to determine how the model is prepared, which artifacts are generated, and how the resulting file is consumed.

The README does not document supported quantization formats, calibration data requirements, accuracy checks, or exact ONNX Runtime package versions. It also does not claim that the examples apply to NVIDIA hardware generally. TensorRT EP is named, but no device model, driver, CUDA version, or platform is documented. The supported interpretation is therefore narrow: the repository demonstrates quantization paths for the two named execution providers. It does not establish which one suits a given deployment.

The CPU EP and TensorRT EP distinction can affect adoption cost. A CPU path may align more closely with a deployment that does not depend on the TensorRT execution environment, but the README does not explicitly document compatibility. A TensorRT-oriented path introduces execution-provider-specific considerations, yet the README gives no setup procedure for it. The quantization directory should therefore be reviewed as source material tied to a target environment, not as evidence of backend-neutral deployment.

CI Signals for C/C++ and Mobile Examples

The README attaches pipeline badges to the C and C++ and mobile example groups. For C and C++, it lists Linux CPU and Windows CPU workflows. For mobile, it points to a Visual Studio pipeline. These are project-maintained signals about intended build coverage, not proof that every supported use case has been validated.

The README does not list the workflow triggers, dependency installation steps, compiler versions, runner images, or test cases used by those pipelines. It also leaves the JavaScript and quantization pipeline status blank in the displayed table. That absence does not prove those examples cannot build or run. It means the repository overview does not document a corresponding status for them.

Based on the repository layout, CI configuration appears under `.github/workflows`, while mobile automation may be represented by the external pipeline link printed in the README. That distinction suggests separate validation paths. Before selecting an example, inspect the relevant workflow and directory configuration rather than assuming every example follows the same build procedure. The top-level `ci_build/` and `tools/` entries may also contain relevant automation, but the README does not explain their roles.

Repository Layout Without a Unified Installation Recipe

The README does not provide installation commands. It links to the separate `microsoft/onnxruntime` repository and directs readers to individual example groups, but it does not state whether examples should be built with a package manager, CMake, Gradle, Xcode, npm, or another build system. No setup command can therefore be copied accurately from the README.

A first use begins by obtaining the repository, entering the directory for the required language, and following the instructions located there. Because the README gives no clone command or per-directory setup sequence, this article cannot present an official command or claim a particular first-run output. The repository URL is visible through the project identity, but inventing a command would exceed what the documentation states.

For C and C++, the README identifies `linux-ci.yml` and `win-ci.yml`, which indicates two CI workflow files and can guide a reader toward build definitions. For mobile and JavaScript, it names the example directories but gives no package commands. For quantization, it names CPU EP and TensorRT EP but identifies no runnable script. A successful first use should be judged by whether the selected example builds in the intended environment and produces outputs matching its model contract. Those details must be established from the selected directory because the repository README is silent.

Repository Scope Versus a Production ONNX Runtime Application

The largest limitation is conceptual. `microsoft/onnxruntime-inference-examples` presents examples, not a unified inference service, package, or deployment product. The repository has no GitHub releases, so the README and repository metadata identify no release artifact or tagged distribution. Consumers should not assume that obtaining this repository installs ONNX Runtime itself.

The README also does not document model download instructions, input schemas, output interpretation, threading, batching, error handling, telemetry, or production deployment. It does not name a sample model that a reader can confidently use as a first inference. The examples are most useful to developers who can already identify the model interface and inspect source files for platform-specific setup.

A real alternative is to use the ONNX Runtime project directly, without this example repository, when the goal is production implementation or current API reference coverage. That approach avoids treating examples as a framework, but it requires assembling integration details independently. This repository adds value when the goal is to examine language-specific implementations. Its trade-off is sample maintenance: examples can diverge from current runtime conventions, and the repository has no release history indicating synchronized example versions.

MIT Licensing, Contributions, and Upgrade Review

The repository uses the MIT license. It permits use, modification, and redistribution under the license's stated conditions, while preserving the required license and notice text. This is a repository-level statement rather than legal advice. ONNX Runtime and any separately obtained models, packages, native libraries, or execution providers may have their own licenses that must be reviewed separately.

Contributions are invited, but most require agreement to a Contributor License Agreement. The README states that a CLA bot assesses pull requests and indicates that the agreement is completed once across repositories using that CLA. It also points to `CODE_OF_CONDUCT.md` and Microsoft's Open Source Code of Conduct.

For maintenance, the repository is not archived, and the last push was on 2026-10-16. No GitHub releases are listed. These facts support treating the repository as a current source tree with examples, not as a versioned distribution. Upgrade review should focus on the selected example's runtime dependency, execution provider, and build configuration. The README does not identify compatibility ranges, an upgrade policy, or a support lifecycle, so those points require inspection of the relevant files and the linked ONNX Runtime project.

Editorial conclusion

Adopt this repository when engineers need examples across several ONNX Runtime integration paths or need starting points for CPU EP and TensorRT EP quantization. Do not treat it as a production SDK, a single installable application, or a substitute for model-specific validation. Review the selected example directory and its dependency documentation first, because the repository README provides no unified install command, model schema, supported version matrix, or rollout guidance. The repository is not archived, and its last push was on 2026-10-14, but it has no GitHub releases, so compatibility and upgrade expectations must be checked against the selected example.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. microsoft/onnxruntime-inference-examples on GitHub
4. README