Pickle is sour, let’s use ONNX

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The Open Neural Network Exchange (ONNX) format offers several advantages over using Python’s pickle format for storing and sharing machine learning models, particularly in the context of deep learning and neural networks:

1. Framework Agnostic: ONNX provides a way to represent models that can be used across different machine learning and deep learning frameworks. This means you can train a model in one framework (e.g., PyTorch) and deploy it in another (e.g., TensorFlow), which is not possible with pickle, as pickle is Python-specific and often tied to a specific framework’s version and structure.
2. Optimization: ONNX models can be optimized for different hardware architectures, potentially leading to faster inference times. Tools like ONNX Runtime can provide significant performance improvements by optimizing the execution on specific hardware (CPU, GPU, or even specialized accelerators like FPGAs).
3. Version Control and Standardization: ONNX models are versioned, ensuring compatibility and making it easier to track model versions. This standardized format aids in model sharing, collaboration, and deployment across different platforms and devices, unlike pickle files, which can suffer from compatibility issues across different Python and library versions.
4. Security: Pickle files can execute arbitrary code during loading, which poses a security risk, especially when loading models from untrusted sources. ONNX, being a more structured and focused format for neural network models, does not execute code on loading, thereby reducing the risk of code injection attacks.
5. Interoperability: ONNX supports a wide range of AI models, including deep learning, traditional ML, and hybrid models, facilitating interoperability between different types of models and tools within the AI ecosystem. This is in contrast to pickle, which is primarily a Python object serialization method and may not be suitable for all types of models, especially when interoperability with non-Python environments is required.
6. Portability: ONNX models are inherently more portable due to their framework-agnostic nature. They can be deployed on various platforms, including cloud, edge devices, and mobile, without the need for the original training environment, which is not always feasible with pickle-serialized models due to their dependency on the specific Python and framework environment.
7. Community and Tool Support: The ONNX format is supported by a broad community, including major companies and organizations in the AI field. This support has led to the development of a rich ecosystem of tools for converting, optimizing, visualizing, and deploying ONNX models, which is generally not available for models serialized with pickle.

In summary, while pickle might be convenient for quick serialization of Python objects, including some machine learning models, within a homogeneous environment, ONNX offers a more robust, secure, and interoperable solution for sharing and deploying machine learning models across various platforms and frameworks.