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Definition of ONNX (Open Neural Network Exchange)
ONNX (Open Neural Network Exchange) is A standardized format to run AI models on different hardware and software platformswithout depending on a specific framework.
Why is ONNX crucial?
- Facilitates the portability of the models Between Pytorch, Tensorflow and other frameworks.
- Allows models to be run on several types of equipment (CPU, GPU, FPGA, TPU).
- Optimizes inference by using appropriate hardware accelerators.
Concrete examples
🔹 Microsoft Azure AI Use ONNX to run IA models optimized.
🔹 Meta and Nvidia contributed to the development of ONNX to improve IA compatibility.
Advantages and challenges
| Benefits | Challenge |
|---|---|
| 🚀 Multi-platform execution | ❗ Compatibility with some still limited models |
| 🔋 Optimization for inference | ⚙️ requires a process for converting models |
| 📡 Independence from frameworks | 🔄 Adoption still in progress in certain companies |
The future of Onnx
✅ Massive adoption in IA cloud computing.
✅ Standardization of AI models for increased interoperability.
✅ Optimization of IA inference on all types of equipment.
