onnx-go - Integrate ONNX Models into Go Applications for Simplified Machine Learning

ONNX-Go: A Journey into Neural Networks for Go Developers

ONNX-Go is a fascinating project that bridges the world of machine learning with the Go programming language. It offers developers the ability to incorporate neural network capabilities into their Go applications with minimal effort and without the need for extensive data science knowledge. Here’s an introduction into the project and its features.

What is ONNX-Go?

ONNX-Go is essentially a Go interface for the Open Neural Network Exchange (ONNX) format. ONNX is an open ecosystem for interchangeable AI models. The purpose of ONNX-Go is to provide a simple way to decode ONNX binary models into a computation backend, enabling developers to seamlessly integrate machine learning models into their Go applications.

Project Revival

Originally created by Olivier Wulveryck, the ONNX-Go project was archived by its creator but has been revived thanks to the efforts of Orama. They are committing significant resources to enhance and maintain the project, making it an active and exciting open-source tool once again.

Key Features

Conversion and Execution: ONNX-Go can decode the binary models from the ONNX format and help execute them within Go applications.
Partial Implementation: It provides a partial implementation of the ONNX specification for importing models. However, export capabilities are not yet implemented.
Go Modules Compatibility: It is easily installable via Go's package manager, making it simple to integrate into existing Go codebases.

How to Use ONNX-Go

Installation

Getting ONNX-Go set up in your project is as easy as running:

go get github.com/owulveryck/onnx-go

This command will include ONNX-Go in your Go modules, making it ready to use.

Running a Simple Example

ONNX-Go provides examples that assume you have a pretrained ONNX model file. The examples demonstrate how to decode models into a backend graph which can then be manipulated as needed. An example that uses a simple backend graph provides a straightforward introduction to the use of ONNX models:

backend := simple.NewSimpleGraph()
model := onnx.NewModel(backend)

b, _ := ioutil.ReadFile("model.onnx")
err := model.UnmarshalBinary(b)

Integrating with Gorgonia

For more advanced use, ONNX-Go can work with Gorgonia, a different Go library that handles sophisticated computation graphs. While Gorgonia does not currently support all ONNX operators, ongoing development aims to enhance compatibility, providing developers with powerful tools to run more complex models.

Understanding the Internals

Protobuf Definition: ONNX uses Protocol Buffers (protobuf) for its model definitions, which are compiled into Go, hiding the complexity from the end-user.

Execution Backend: To execute neural network operations, a backend that can handle computation graphs is needed. ONNX-Go doesn't include a backend but suggests using Gorgonia as the primary option.

Contribution and Community

The ONNX-Go project is open to contributions. While a formal contribution guide is on the roadmap, developers are encouraged to participate through issues or pull requests.

The project fosters a collaborative environment, welcoming contributions underlining a commitment to inclusivity and diversity as per the Contributor Covenant.

License and Authorship

Authored by Olivier Wulveryck, the ONNX-Go project is available under the MIT License, which allows for wide use and modification with minimal restrictions.

This broad overview offers insight into ONNX-Go and its potential applications. Whether you're a seasoned Go developer or just getting started with machine learning, ONNX-Go opens doors to innovative and exciting possibilities within your applications.