About ONNX

ONNX is an open-source format designed for machine learning models. It ensures cross-platform compatibility. This format also supports major languages and frameworks, facilitating efficient model exchange.

The ONNX format allows for model serialization. Models are represented as graphs of common machine learning operations. These graphs are saved in a portable format called protocol buffers. It simplifies the exchange of models across various platforms. These platforms include cloud, web, edge, and mobile experiences on Microsoft Windows, Linux, Mac, iOS, and Android. ONNX models also offer flexibility to export and import model in many languages such as Python, C++, C#, and Java to name a few. The ONNX format is useful for compute-heavy tasks such as training machine learning models and data processing that often uses trained models. Many leading machine learning development frameworks such as TensorFlow, Pytorch, and Scikit-learn, offer the capability to convert models into the ONNX format.

Once you represent the models in the ONNX format, you can run them with the ONNX Runtime. The architecture of the ONNX Runtime is adaptable, enabling providers to modify or enhance how some operations are implemented to make better use of particular hardware, such as, Graphical Processing Units (GPUs), Single Instruction Multiple Data (SIMD) instruction sets or specialized libraries. To learn more on ONNX Runtime, see https://onnxruntime.ai/docs/.

The ONNX Runtime integration with Oracle Database allows for the import of ONNX-formatted models, including embedding models. To support embedding models, Oracle Machine Learning has introduced a machine learning technique called embedding. You can only use ONNX embedding models in the Oracle database if they were converted using Oracle’s Python utility package. Oracle's Python utility downloads a pretrained model, converts the model to ONNX format augmented with pre-processsing and post-processing operations and imports the ONNX format model to Oracle Database. For more information on the Python utility tool, see Convert Pretrained Models to ONNX Format.

Oracle supports ONNX Runtime version 1.20.1.