onnxruntime-inference-examples
by microsoft
Examples for using ONNX Runtime for machine learning inferencing.
AI summary
Inference framework
Repository providing examples for using ONNX Runtime (ORT) to perform machine learning inferencing.
- stars
- 1.2K
- forks
- 342
- watching
- 38
Similar projects
Found by comparing what the projects do, not just their names.
Transformer accelerator
Accelerates training of large transformer models by providing optimized kernels and memory optimizations.
ML accelerator
A cross-platform, high-performance machine learning accelerator
Model benchmarking suite
Measures the performance of deep learning models in various deployment scenarios.
Inference engine
An onnx inference engine for embedded devices with hardware acceleration support
ONNX runtime
An embedded device-friendly C ONNX runtime with zero dependencies
Triton client libraries
Client libraries and examples for communicating with Triton using various programming languages
LLaMa inference toolset
A project providing onnx models and tools for inference with LLaMa transformer model on various devices
ONNX compiler
Generates C code from ONNX files for efficient neural network inference on microcontrollers
Deep Learning Engine
An API and backend for running ONNX models in Scala 3 using typeful, functional deep learning and classical machine learning.
Machine learning examples
Provides code and materials to apply SAS machine learning techniques in software development
NCNN wrapper
A ROS wrapper for NCNN, allowing developers to integrate high-performance neural network inference into ROS projects
Neural Network Library
A Go package that allows developers to import pre-trained neural network models without being tied to a framework or library.
Model deployment hub
A platform for deploying and fine-tuning computer vision models in production-ready environments.
Machine Learning Framework
A framework for hosting and training machine learning models on a blockchain, enabling secure sharing and prediction without requiring users to pay for data or model updates.
