ONNX - Converting Libraries

ONNX (Open Neural Network Exchange) is an open-source format used for representing machine learning models, enabling the exchange of models between various frameworks. By converting models to ONNX, you can use a single runtime to deploy them, enhancing flexibility and portability across platforms.

In this tutorial, we will learn about the converting libraries in the ONNX, explore the available tools for different machine learning frameworks.

Introduction to Converting Libraries

A converting library is a tool that helps translate a model's logic from its original framework (like TensorFlow or scikit-learn) into the ONNX format. These libraries make sure that the converted model's predictions are either exactly the same or very close to the original model's predictions.

Without these converters, you would have to manually rewrite parts of the model, which can take a lot of time and effort.

Why Are Converting Libraries Important?

• Simplifies Model Conversion: Converting libraries automate the complex task of translating a machine learning model's prediction into ONNX format.
• Accuracy: These libraries are designed to maintain the accuracy of the model's predictions after conversion.
• Time-Saving: Manually implementing model parts in ONNX can be time taking. Converting libraries speeding up this process by handling most of the conversion automatically.
• Model Deployment Flexibility: Once converted to ONNX format, models can be run on a wide range of platforms and devices, making it easier to deploy them in production environments.

Available Converting Libraries

Different machine learning frameworks require different converting tools. Here are some commonly used libraries −

• sklearn-onnx Converts models from scikit-learn to ONNX format. If you have a scikit-learn model, this tool makes sure the model works well in the ONNX format.
• tensorflow-onnx This library converts models from TensorFlow to ONNX format. It simplifies the process of converting of deep learning models built using TensorFlow.
• onnxmltools This library converts models from various libraries, including LightGBM, XGBoost, PySpark, and LibSVM.
• torch.onnx It converts models from PyTorch to ONNX format. PyTorch users can convert their models for cross-platform deployment using ONNX runtime.

Common Challenges in Conversion

These libraries need to be updated frequently to match new versions of ONNX and the original frameworks they support. This can happen 35 times a year to keep things compatible.

• Framework-Specific Tools: Each converter is designed to work with a specific framework. For example, tensorflow-onnx works only with TensorFlow, and sklearn-onnx works only with scikit-learn.
• Custom Components: If your model has custom layers, you may need to write custom code to handle those during conversion. This can make the process more difficult.
• Non-Deep Learning Models: Converting models from libraries like scikit-learn can be tricky because they rely on external tools like NumPy or SciPy. You might need to manually add conversion logic for certain parts of the model.

Alternatives to Converting Libraries

An alternative to writing framework-specific converters is to use standard protocols that promote code re-usability across multiple libraries. One such protocol is the Array API standard, which standardizes array operations across several libraries like NumPy, JAX, PyTorch, and CuPy.

ndonnx

Supports execution with an ONNX backend and provides instant ONNX export for code compliant with the Array API. It is ideal for users looking to integrate ONNX export functionality with minimal custom code.

It reduces the need for framework-specific converters. Provides a simple, NumPy-like way to build ONNX models.