ONNX Examples

This repository contains examples demonstrating the usage of ONNX (Open Neural Network Exchange) format and runtime.

Setup

Prerequisites

• Python 3.8 or higher
• uv (Python package installer)

Virtual Environment Setup

1. Create a virtual environment:

uv venv

2. Activate the virtual environment:

# On macOS/Linux
source .venv/bin/activate

# On Windows
.\.venv\Scripts\activate

3. Install dependencies for a specific example:

cd example_directory  # e.g., cd 01_basic_neural_network
uv pip install -r requirements.txt

Examples

01_basic_neural_network

A simple example demonstrating:

• Creating a basic neural network using PyTorch
• Exporting the model to ONNX format
• Loading and running inference using ONNX Runtime

The network architecture includes:

• Input layer (2 neurons)
• Hidden layer (3 neurons) with ReLU activation
• Output layer (1 neuron) with Sigmoid activation

02_sentence_similarity

An example using the Redis langcache-embed-v1 model for semantic similarity:

• Loading a pre-trained sentence transformer model
• Computing sentence embeddings and similarities
• Exporting to ONNX format
• Running inference with ONNX Runtime

The model features:

• 768-dimensional embeddings
• 8192 token maximum sequence length
• Fine-tuned on Quora dataset
• High accuracy on semantic similarity tasks

Contributing

Feel free to contribute additional examples or improvements to existing ones by submitting pull requests.

License

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