ONNX Runtime: High-Performance ML Inference and Training Accelerator 🚀

Welcome to the ONNX Runtime repository! This project focuses on providing a cross-platform, high-performance machine learning inference and training accelerator. It supports various frameworks and is designed to optimize the performance of your machine learning models.

Table of Contents

• Introduction
• Features
• Supported Frameworks
• Installation
• Getting Started
• Usage
• Contributing
• License
• Contact

Introduction

ONNX Runtime is built to support the ONNX (Open Neural Network Exchange) format. It allows developers to easily switch between different machine learning frameworks while maintaining high performance. This project is ideal for anyone looking to leverage the power of machine learning without being tied to a single framework.

You can find the latest releases here. Download the appropriate file and execute it to get started.

Features

• Cross-Platform: Works on various operating systems including Windows, Linux, and macOS.
• High Performance: Optimized for speed and efficiency, making it suitable for both inference and training.
• Hardware Acceleration: Utilizes available hardware resources effectively, supporting GPUs and specialized accelerators.
• Framework Compatibility: Supports multiple machine learning frameworks, allowing seamless integration.
• Scalability: Designed to scale from edge devices to large data centers.

Supported Frameworks

ONNX Runtime supports a variety of machine learning frameworks. Here are some of the most notable ones:

• PyTorch: A popular deep learning framework that offers dynamic computation graphs.
• TensorFlow: A comprehensive open-source platform for machine learning.
• Scikit-Learn: A library for machine learning in Python, featuring various algorithms for classification, regression, and clustering.
• ONNX: The core format that ONNX Runtime supports, allowing interoperability between frameworks.

Installation

To install ONNX Runtime, follow these steps:

1. Clone the repository:

   git clone https://github.com/alinabil74568/onnxruntime.git
   cd onnxruntime

2. Install dependencies:

   pip install -r requirements.txt

3. Build the project:

   python setup.py install

You can find the latest releases here. Download the appropriate file and execute it to get started.

Getting Started

After installation, you can start using ONNX Runtime in your projects. Here’s a simple example to help you get started:

import onnxruntime as ort

# Load the model
session = ort.InferenceSession("model.onnx")

# Prepare input data
input_data = ...  # Your input data here

# Run inference
output = session.run(None, {"input": input_data})

print(output)

Usage

Using ONNX Runtime is straightforward. Here are some common tasks you can perform:

Inference

To run inference with a pre-trained model, follow these steps:

1. Load your ONNX model.
2. Prepare the input data.
3. Call the run method to get predictions.

Training

ONNX Runtime also supports training. To train a model:

1. Define your model architecture.
2. Use ONNX Runtime's training APIs.
3. Monitor training progress and save your model.

Performance Optimization

For optimal performance:

• Use the latest version of ONNX Runtime.
• Ensure that your hardware accelerators are properly configured.
• Profile your model to identify bottlenecks.

Contributing

We welcome contributions! If you want to help improve ONNX Runtime, please follow these steps:

1. Fork the repository.
2. Create a new branch.
3. Make your changes and commit them.
4. Push your branch and create a pull request.

Please ensure your code adheres to our coding standards and includes tests where applicable.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or support, please open an issue in the repository or contact the maintainers directly.

You can find the latest releases here. Download the appropriate file and execute it to get started.

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Feel free to explore the code, report issues, and suggest improvements. Your feedback is valuable as we continue to enhance ONNX Runtime for the community!