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Open Format for ML: ONNX Explained

Updated on Dec 27,2023

Open Format for ML: ONNX Explained

Table of Contents:

  1. Introduction
  2. What is ONNX Model Format?
  3. Core Benefits of ONNX
    • Interoperability
    • Hardware Access
  4. Supported Formats
    • TensorFlow
    • PyTorch
    • Keras
    • scikit-learn
    • Core ML
    • Other Frameworks
  5. Comparisons and Performance
  6. Saving ONNX Models
    • Saving Keras Models as ONNX
    • Saving PyTorch Models as ONNX
  7. Deploying ONNX Models on iOS
    • Apple Neural Engine
    • Core ML Framework
  8. Converting Models to ONNX
    • Converting Keras Models to ONNX
    • Converting PyTorch Models to ONNX
  9. Image Classification Example
  10. Image Segmentation Example
  11. Conclusion
  12. Next Steps

What is ONNX Model Format and Its Core Benefits

ONNX model format is an open format designed to represent machine learning models. It provides interoperability and hardware access, making it a versatile option for developers. ONNX allows You to convert models from different frameworks into a unified format, enabling seamless integration and easy deployment across various platforms.

Core Benefits of ONNX

Interoperability

The primary AdVantage of ONNX is its interoperability. With ONNX, you can convert models trained in frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn into the ONNX format. This means you can utilize models created in different frameworks without worrying about compatibility issues. For example, if you have trained a model in PyTorch but need to deploy it using TensorFlow Lite, you can easily convert the model to ONNX and then convert it to TensorFlow Lite.

Hardware Access

ONNX also provides hardware access optimizations, allowing you to leverage hardware-specific optimizations to enhance model performance. ONNX supports various hardware platforms, including CPUs, GPUs, and the Apple Neural Engine. By utilizing ONNX models, you can take advantage of these hardware optimizations to speed up your model's execution time.

Supported Formats

ONNX supports a wide range of formats, making it compatible with popular machine learning frameworks. Some of the supported formats include:

  • TensorFlow: TensorFlow models can be converted to ONNX format using libraries like tf2onnx.
  • PyTorch: PyTorch models can be converted to ONNX using the torch.onnx.export function.
  • Keras: Keras models can be saved as ONNX using the keras2onnx library.
  • scikit-learn: Models trained in scikit-learn can also be converted to ONNX.
  • Core ML: If you are developing apps for iOS, you can convert ONNX models to Core ML format for seamless integration.
  • Other Frameworks: ONNX supports other frameworks like Cafe.

By supporting these formats, ONNX ensures that you can work with your preferred framework and easily convert models to ONNX format.

Comparisons and Performance

Several performance comparisons have shown that ONNX models offer faster execution times compared to other formats. In tests conducted using different hardware platforms, ONNX models consistently outperformed other baselines. The optimizations provided by ONNX, particularly when utilizing hardware acceleration, result in improved inference speeds. However, it is worth noting that to achieve the maximum benefits of ONNX's hardware optimizations, it is essential to deploy the models on compatible hardware.

Saving ONNX Models

Saving models in ONNX format is a straightforward process. Depending on the framework you are using, there are specific methods and libraries available for saving models as ONNX.

For Keras models, you can use the keras2onnx library to convert and save the model as ONNX. Similarly, for PyTorch models, you can utilize torch.onnx.export to convert the model and save it as ONNX.

In the next tutorial, we will provide a step-by-step guide on saving Keras and PyTorch models as ONNX, ensuring you have a clear understanding of the process.

Deploying ONNX Models on iOS

When it comes to deploying ONNX models on iOS, the Apple Neural Engine and Core ML framework play crucial roles. The Apple Neural Engine is a dedicated chip in Apple devices designed to accelerate deep learning tasks. By leveraging the Apple Neural Engine, you can achieve optimized performance for inference on iOS devices.

Core ML is the framework used for deep learning inference on iOS devices. It provides integration with the Apple Neural Engine and offers seamless deployment of ONNX models within iOS apps. Once you have converted your model to ONNX format, you can easily convert it to a Core ML model and integrate it into your iOS application.

While this article focuses on the concepts and benefits of ONNX, we encourage further exploration of iOS app development and Core ML deployment. Developing an iOS app that utilizes ONNX models for tasks such as image classification or segmentation can be a rewarding project, offering opportunities for innovation and creativity.

In the next tutorial, we will dive into an image classification example using ONNX, followed by an image segmentation example in subsequent tutorials.

Conclusion

In conclusion, ONNX model format provides a versatile solution for representing machine learning models. Its core benefits of interoperability and hardware access make it a valuable tool for developers. By converting models to ONNX format, you can ensure compatibility across different frameworks and optimize model performance using hardware-specific optimizations.

In the next tutorial, we will explore how to convert Keras models to ONNX and demonstrate an image classification example. To stay updated, don't forget to hit the Subscribe button and leave your comments if you have any specific topics or questions you would like us to cover.

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