ONNX Runtime and CoreML May Silently Convert Your Model to FP16 In the rapidly evolving world of machine learning and artificial intelligence, the ability to efficiently deploy and run models in production environments is critical. Two popular frameworks, ONNX Runtime and CoreML, have recently come under scrutiny for a potentially concerning behavior - the silent conversion of models to the FP16 (16-bit floating-point) data format. This discovery, reported on Hacker News, highlights the importance of understanding the underlying mechanisms and implications of model optimization techniques used by these frameworks. As developers and data scientists increasingly rely on tools like ONNX Runtime and CoreML to streamline their ML workflows, it's essential to be aware of how these tools may be handling the models, potentially without the user's explicit knowledge or consent. The ONNX (Open Neural Network Exchange) format has gained significant traction as a way to standardize and facilitate the exchange of machine learning models between different frameworks and platforms. ONNX Runtime is a high-performance inference engine that allows developers to run ONNX models efficiently across a variety of hardware architectures, including CPUs, GPUs, and specialized AI accelerators. Similarly, CoreML is Apple's proprietary framework for integrating machine learning models into iOS, macOS, and other Apple ecosystem applications. It provides a unified interface for deploying and running ML models, making it easier for developers to integrate advanced AI capabilities into their apps. The issue at hand is that both ONNX Runtime and CoreML may silently convert a user's model to the FP16 format, even if the original model was stored in a different precision (such as FP32 or INT8). This conversion can have significant implications for the model's performance, accuracy, and compatibility with certain hardware configurations. FP16, or 16-bit floating-point, is a data format that uses half the storage space of the more common FP32 (32-bit floating-point) format. While FP16 can offer performance benefits, particularly on hardware with specialized FP16 support, it also has a more limited dynamic range and precision compared to FP32. This means that the model's outputs may be less accurate, and certain operations, such as gradient calculations during training, may become less stable. The silent nature of this conversion is particularly concerning, as developers may not be aware that their models have been altered. This can lead to unexpected behavior, performance issues, or even incorrect results in production environments, where the model's output may be used to drive critical decisions or actions. The impact of this silent conversion can be particularly problematic in domains where model accuracy is of paramount importance, such as medical diagnostics, financial risk analysis, or autonomous vehicle control. In these scenarios, even small changes in model behavior can have significant real-world consequences. The Hacker News discussion highlights that the issue is not limited to a single framework or platform. Both ONNX Runtime and CoreML have been observed to silently convert models to FP16, with users reporting that they only discovered the conversion after extensive investigation or by chance. One of the key takeaways from this discussion is the need for greater transparency and user control over the model optimization processes employed by these frameworks. Developers should have a clear understanding of how their models are being handled and the potential impact of any transformations or optimizations performed by the underlying tools. Additionally, there is a call for improved documentation and explicit options or settings that allow users to control the precision of their models during deployment. This would empower developers to make informed decisions about the trade-offs between performance, accuracy, and compatibility, rather than relying on potentially opaque and unexpected behaviors. As the machine learning ecosystem continues to evolve, with an ever-increasing array of frameworks, tools, and deployment options, it is crucial that developers maintain a vigilant and informed approach to model management. By understanding the potential pitfalls and advocating for greater transparency, the community can work to ensure that the powerful capabilities of machine learning are deployed in a responsible and reliable manner.