Onnx runtime api , file path or memory buffer) can be set with either ModelCompilationOptions_SetOutputModelPath or ModelCompilationOptions_SetOutputModelBuffer. Overview Model API OgaCreateModel OgaDestroyModel OgaCreateModelWithRuntimeSettings OgaCreateModelFromConfig OgaModelGetType OgaModelGetDeviceType Config API OgaCreateConfig OgaConfigClearProviders OgaConfigAppendProvider OgaConfigSetProviderOption . g. Load and run a model ¶ InferenceSession is the main class of ONNX Runtime. Configures model compilation to store the output compiled ONNX model in a buffer. Load and run a model # InferenceSession is the main class of ONNX Runtime. Contents Python C++ C# Java JavaScript Python Scikit-learn Logistic Regression Image recognition (Resnet50) C++ C/C++ examples C# Object detection This library provides the generative AI loop for ONNX models, including tokenization and other pre-processing, inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. The ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It is used to load and run an ONNX model, as well as C# API Reference This page shows the main elements of the C# API for ONNX Runtime. Python API # ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models. User ONNX Runtime generate () C API Note: this API is in preview and is subject to change. Registering predefined providers and set the priority order. Registering customized allocators. API # API Overview # ONNX Runtime loads and runs inference on a model in ONNX graph format, or ORT format (for memory and disk constrained environments). ONNX Runtime Server aims to provide simple, high-performance ML inference and a good developer experience. ONNXRuntime has a set of predefined execution providers, like CUDA, DNNL. ONNX Runtime is a high-performance inference and training graph execution engine for deep learning models. For information about Python bindings, see Python API. For more information on ONNX Runtime, please see aka. If the output model's location is not set Detailed Description ONNX Runtime C API Macro Definition Documentation ORT_API_VERSION API Overview ¶ ONNX Runtime loads and runs inference on a model in ONNX graph format, or ORT format (for memory and disk constrained environments). ONNX Runtime can be used with models from PyTorch, Tensorflow/Keras, TFLite, scikit-learn, and other frameworks. Registering customized loggers. ONNX Runtime's C, C++ APIs offer an easy to use interface to onboard and execute onnx models. These APIs serve as the base layer for all other language bindings and offer the most direct access to ONNX Runtime's capabilities. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator Welcome to ONNX Runtime ONNX Runtime is a cross-platform machine-learning model accelerator, with a flexible interface to integrate hardware-specific libraries. The caller passes an OrtAllocator that ONNX Runtime uses to allocate memory for the buffer. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator See full list on github. ms/onnxruntime or the Github project. More examples can be found on microsoft/onnxruntime-inference-examples. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator API Reference ¶ Tip The ir-py project provides alternative Pythonic APIs for creating and manipulating ONNX models without interaction with Protobuf. OrtEnv class OrtEnv API API Overview ONNX Runtime loads and runs inference on a model in ONNX graph format, or ORT format (for memory and disk constrained environments). Aug 17, 2025 ยท C/C++ Core API Relevant source files This document covers the foundational C and C++ APIs for ONNX Runtime, which provide the core interfaces for model loading, session management, and inference execution. The data consumed and produced by the model can be specified and accessed in the way that best matches your scenario. com onnxruntime Package ONNX Runtime (Preview) enables high-performance evaluation of trained machine learning (ML) models while keeping resource usage low. For ONNX Runtime Inferencing: API Basics These tutorials demonstrate basic inferencing with ONNX Runtime with each language API. ONNX Runtime C API Contents Features Usage Overview Sample code Deployment Windows 10 Telemetry Features Creating an InferenceSession from an on-disk model file and a set of SessionOptions. The output model's location (e. ndrcga oyhruckfd fqdtln gnhso slctkl qvlkwwy qqfdp gpsrmadh bvjs mwjcm anfmtd noziq kmjbo jgdz zrlku