# ONNXRuntime-Extensions
[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=209&branchName=main)
## What's ONNXRuntime-Extensions
Introduction: ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via ONNX Runtime Custom Operator ABIs. It includes a set of [ONNX Runtime Custom Operator](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html) to support the common pre- and post-processing operators for vision, text, and nlp models. And it supports multiple languages and platforms, like Python on Windows/Linux/macOS, some mobile platforms like Android and iOS, and Web-Assembly etc. The basic workflow is to enhance a ONNX model firstly and then do the model inference with ONNX Runtime and ONNXRuntime-Extensions package.
## Quickstart
### **Python installation**
```bash
pip install onnxruntime-extensions
````
### **Nightly Build**
#### <strong>on Windows</strong>
```cmd
pip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions
```
Please ensure that you have met the prerequisites of onnxruntime-extensions (e.g., onnx and onnxruntime) in your Python environment.
#### <strong>on Linux/macOS</strong>
Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command
```bash
python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git
```
## Usage
## 1. Generate the pre-/post- processing ONNX model
With onnxruntime-extensions Python package, you can easily get the ONNX processing graph by converting them from Huggingface transformer data processing classes, check the following API for details.
```python
help(onnxruntime_extensions.gen_processing_models)
```
### NOTE: These data processing model can be merged into other model [onnx.compose](https://onnx.ai/onnx/api/compose.html) if needed.
## 2. Using Extensions for ONNX Runtime inference
### Python
There are individual packages for the following languages, please install it for the build.
```python
import onnxruntime as _ort
from onnxruntime_extensions import get_library_path as _lib_path
so = _ort.SessionOptions()
so.register_custom_ops_library(_lib_path())
# Run the ONNXRuntime Session, as ONNXRuntime docs suggested.
# sess = _ort.InferenceSession(model, so)
# sess.run (...)
```
### C++
```c++
// The line loads the customop library into ONNXRuntime engine to load the ONNX model with the custom op
Ort::ThrowOnError(Ort::GetApi().RegisterCustomOpsLibrary((OrtSessionOptions*)session_options, custom_op_library_filename, &handle));
// The regular ONNXRuntime invoking to run the model.
Ort::Session session(env, model_uri, session_options);
RunSession(session, inputs, outputs);
```
### Java
```java
var env = OrtEnvironment.getEnvironment();
var sess_opt = new OrtSession.SessionOptions();
/* Register the custom ops from onnxruntime-extensions */
sess_opt.registerCustomOpLibrary(OrtxPackage.getLibraryPath());
```
### C#
```C#
SessionOptions options = new SessionOptions()
options.RegisterOrtExtensions()
session = new InferenceSession(model, options)
```
#
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"description": "# ONNXRuntime-Extensions\n\n[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=209&branchName=main)\n\n## What's ONNXRuntime-Extensions\n\nIntroduction: ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via ONNX Runtime Custom Operator ABIs. It includes a set of [ONNX Runtime Custom Operator](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html) to support the common pre- and post-processing operators for vision, text, and nlp models. And it supports multiple languages and platforms, like Python on Windows/Linux/macOS, some mobile platforms like Android and iOS, and Web-Assembly etc. The basic workflow is to enhance a ONNX model firstly and then do the model inference with ONNX Runtime and ONNXRuntime-Extensions package.\n\n\n## Quickstart\n\n### **Python installation**\n```bash\npip install onnxruntime-extensions\n````\n\n\n### **Nightly Build**\n\n#### <strong>on Windows</strong>\n```cmd\npip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions\n```\nPlease ensure that you have met the prerequisites of onnxruntime-extensions (e.g., onnx and onnxruntime) in your Python environment.\n#### <strong>on Linux/macOS</strong>\nPlease make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command\n```bash\npython -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git\n```\n\n\n## Usage\n\n## 1. Generate the pre-/post- processing ONNX model\nWith onnxruntime-extensions Python package, you can easily get the ONNX processing graph by converting them from Huggingface transformer data processing classes, check the following API for details.\n```python\nhelp(onnxruntime_extensions.gen_processing_models)\n```\n### NOTE: These data processing model can be merged into other model [onnx.compose](https://onnx.ai/onnx/api/compose.html) if needed.\n## 2. Using Extensions for ONNX Runtime inference\n\n### Python\nThere are individual packages for the following languages, please install it for the build.\n```python\nimport onnxruntime as _ort\nfrom onnxruntime_extensions import get_library_path as _lib_path\n\nso = _ort.SessionOptions()\nso.register_custom_ops_library(_lib_path())\n\n# Run the ONNXRuntime Session, as ONNXRuntime docs suggested.\n# sess = _ort.InferenceSession(model, so)\n# sess.run (...)\n```\n### C++\n\n```c++\n // The line loads the customop library into ONNXRuntime engine to load the ONNX model with the custom op\n Ort::ThrowOnError(Ort::GetApi().RegisterCustomOpsLibrary((OrtSessionOptions*)session_options, custom_op_library_filename, &handle));\n\n // The regular ONNXRuntime invoking to run the model.\n Ort::Session session(env, model_uri, session_options);\n RunSession(session, inputs, outputs);\n```\n### Java\n```java\nvar env = OrtEnvironment.getEnvironment();\nvar sess_opt = new OrtSession.SessionOptions();\n\n/* Register the custom ops from onnxruntime-extensions */\nsess_opt.registerCustomOpLibrary(OrtxPackage.getLibraryPath());\n```\n\n### C#\n```C#\nSessionOptions options = new SessionOptions()\noptions.RegisterOrtExtensions()\nsession = new InferenceSession(model, options)\n```\n\n\n#\n",
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