# ONNX Runtime: Microsoft's Cross-Platform ML Inference and Training Accelerator

> ONNX Runtime is Microsoft's open-source engine for running and accelerating machine learning models from PyTorch, TensorFlow, scikit-learn, LightGBM, and XGBoost across multiple hardware targets. Its README is intentionally minimal; the documentation lives at onnxruntime.ai.

**microsoft/onnxruntime** — ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

- Repository: https://github.com/microsoft/onnxruntime
- Website: https://onnxruntime.ai
- Stars: 21,932 · Forks: 4,253
- Language: C++
- License: MIT
- Published: 2026-08-08 · Updated: 2026-08-18 · Language: en
- Canonical page: https://hysenlabs.com/projects/microsoft-onnxruntime

## What ONNX Runtime Is and Who Uses It

ML practitioners who train models in PyTorch or TensorFlow face a practical problem when moving to production: the training environment is rarely the same as the serving environment. PyTorch alone carries a large runtime dependency. Running inference on edge devices, mobile hardware, or browsers requires something lighter and more portable. ONNX Runtime addresses this. The README describes it as a cross-platform inference and training machine-learning accelerator, supporting models from PyTorch and TensorFlow/Keras as well as classical ML libraries including scikit-learn, LightGBM, and XGBoost.

The inference path is for ML engineers and software teams who want to serve a trained model more efficiently or across different hardware. The training path is for teams who want to speed up transformer model training on multi-node NVIDIA GPU infrastructure without rewriting their PyTorch code.

## Inference Across Frameworks and Hardware Targets

The README states that ONNX Runtime provides optimal performance by using hardware accelerators where available alongside graph optimizations and transforms. The repository structure confirms this. A plugin-ep-cuda/ directory at the top level holds the CUDA execution provider plugin, and a plugin-ep-webgpu/ directory holds the WebGPU execution provider plugin. The README also lists a QNN Plugin EP at github.com/onnxruntime/onnxruntime-qnn. The setup.py build script includes flags for additional variants: is_openvino and is_migraphx, indicating OpenVINO and MIGraphX are supported as well.

This execution provider architecture means the same ONNX Runtime package can run inference on an NVIDIA GPU via CUDA, on a Qualcomm chip via QNN, on an AMD GPU via MIGraphX, or in a browser via WebGPU, with the CPU path always available as a fallback. The performance characteristics of each path are not documented in the README; the project points to onnxruntime.ai/docs for that.

## Language Bindings and the Python Package

The repository contains language-specific binding directories at the top level: csharp/, go/, java/, js/, objectivec/, and rust/. Combined with the Python package built from setup.py, ONNX Runtime provides at least seven language targets from a single repository.

The Python package name, as defined in setup.py, is onnxruntime. The pyproject.toml targets Python 3.10 and later. The Python runtime lists its own dependencies in requirements.txt:

```
flatbuffers
numpy >= 1.21.6
packaging
protobuf>=4.25.8
```

FlatBuffers and Protocol Buffers handle model serialization and communication at the binary protocol level. numpy >= 1.21.6 is the minimum version required for tensor operations. The README does not include installation commands for any language binding; it directs all installation inquiries to onnxruntime.ai/docs.

## The Training Path and Its Specific Scope

ONNX Runtime has a dedicated training component, housed in the orttraining/ directory. The README describes its purpose in one sentence: it can accelerate model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition to existing PyTorch training scripts. The companion repository for training examples is microsoft/onnxruntime-training-examples.

This is a narrow scope. The training acceleration is documented specifically for transformer model architectures, not for arbitrary models. It targets multi-node NVIDIA GPU setups, not single-GPU workstations or AMD hardware. And it requires an existing PyTorch training script, not TensorFlow or other frameworks. Teams using PyTorch for transformer training on NVIDIA clusters are the intended users. Teams with other configurations should confirm support at onnxruntime.ai/docs before depending on the training path.

## Telemetry, Documentation Gaps, and Adopting from This Repository

The README includes a data and telemetry notice: the project may collect usage data and send it to Microsoft to help improve products and services, with a reference to a privacy statement at docs/Privacy.md. Teams with strict data governance requirements should review that file before deploying ONNX Runtime in a sensitive environment.

The README itself is notably short. It does not document how to convert a model from PyTorch or TensorFlow into the format ONNX Runtime expects, how to choose an execution provider, or how to configure graph optimizations. All of this lives at onnxruntime.ai/docs and onnxruntime.ai. Building from source requires the cmake/ directory, build.sh or build.bat scripts, and dependencies not described in the README. Teams who plan to build from source rather than install a prebuilt package should review the CONTRIBUTING.md and the build scripts before starting.

For inference examples, the README points to the companion repository microsoft/onnxruntime-inference-examples. That separation means the main repository itself gives no runnable code beyond build infrastructure.

## What ONNX Runtime Is Not a Substitute For

Running a PyTorch model directly in PyTorch is simpler to set up and requires no model export step. The trade-off is runtime overhead and framework dependency. ONNX Runtime's case for inference is portability and graph optimization, not simplicity of initial setup. A team that will only ever serve on a single platform with no hardware constraints does not gain much from the export-and-accelerate path.

For training, ONNX Runtime is not a general-purpose distributed training framework. The README positions it specifically as an accelerator for an existing PyTorch training loop, not a replacement for PyTorch's own distributed training or for frameworks like DeepSpeed. Teams looking for a complete training platform rather than a one-line acceleration addition for an existing script will find the scope limiting.

## Releases, Maintenance, and License

The most recent full release is v1.30.0, published on 2026-09-10, alongside a patch release v1.29.1 on the same date. The WebGPU execution provider plugin reached v0.4.0 on 2026-09-22. The last push to the repository was on 2026-09-26. The project publishes a release roadmap at onnxruntime.ai/roadmap, which lists upcoming features and release dates.

The license is MIT, which permits commercial use, modification, and redistribution without restrictions beyond attribution. The repository includes a ThirdPartyNotices.txt that covers the licenses of bundled dependencies. Teams integrating ONNX Runtime into a commercial product should verify that file alongside the MIT terms.

The project has a VERSION_NUMBER file at the root, which is the canonical version reference for builds. Nightly build support is indicated by the --nightly_build flag in setup.py.

## Conclusion

ONNX Runtime is the right choice for an ML engineer who wants to run a trained PyTorch, TensorFlow, or scikit-learn model at lower cost or on different hardware without rewriting the model. It is not the right choice for training from scratch: its training path is documented specifically for transformer models on multi-node NVIDIA GPUs with existing PyTorch scripts. Before committing to ONNX Runtime, verify that your model framework and hardware target have a tested execution provider in the repository and check the release roadmap at onnxruntime.ai/roadmap for the features your deployment requires.

## FAQ

### What is the use of ONNX Runtime?

ONNX Runtime is a cross-platform accelerator that runs models trained in PyTorch, TensorFlow/Keras, scikit-learn, LightGBM, and XGBoost. It targets scenarios where inference needs to run on different hardware than where training occurred, using graph optimizations and execution provider plugins to improve performance. The README also describes a training path that accelerates transformer model training on multi-node NVIDIA GPU setups.

### What is the difference between ONNX and ONNX Runtime?

The README describes ONNX Runtime as the execution engine that runs ML models from frameworks including PyTorch, TensorFlow/Keras, scikit-learn, and XGBoost. It does not document the ONNX model format separately from the runtime; detailed coverage of the model format and conversion steps is at onnxruntime.ai/docs.

### How can I use ONNX Runtime in Python?

The Python package name is onnxruntime, as defined in setup.py. The Python runtime requires flatbuffers, numpy >= 1.21.6, packaging, and protobuf>=4.25.8. Installation steps and usage tutorials are at onnxruntime.ai/docs; the README does not include installation commands.

### How do I install ONNX Runtime?

The README does not include installation commands; it directs readers to onnxruntime.ai/docs for installation documentation and tutorials. The Python package name is onnxruntime and a GPU variant is also available, as indicated by the GPU wheel configuration in setup.py.

### How do I use ONNX Runtime with a GPU?

The repository includes a plugin-ep-cuda/ directory for CUDA GPU support and a plugin-ep-webgpu/ directory for WebGPU support. The setup.py also references MIGraphX, OpenVINO, and QNN variants. Detailed GPU setup steps are not in the README; the project points to onnxruntime.ai/docs for hardware-specific configuration.

## Sources

- [Official documentation](https://onnxruntime.ai)
- [Official README](https://github.com/microsoft/onnxruntime#readme)
- [Project repository](https://github.com/microsoft/onnxruntime)
- [Release notes](https://github.com/microsoft/onnxruntime/releases)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/microsoft-onnxruntime