Introduction
ONNX (Open Neural Network Exchange) is an open-source standard that defines a shared model format to enable interoperability between popular ML frameworks—allowing models to be trained in one environment and deployed across many others with ease. ONNX streamlines the deployment lifecycle by enabling teams to export models from PyTorch, TensorFlow, scikit-learn, and other frameworks into a single standardized format, which can then be optimized and executed on performance-tuned runtimes like ONNX Runtime, TensorRT, OpenVINO, or Triton Inference Server.
Key benefits of using ONNX include:
Framework Interoperability: Allows Cake teams to train models in the framework of their choice and deploy them across diverse runtimes and hardware backends without retraining.
Performance Optimization: Works with ONNX Runtime to enable graph-level optimizations, quantization, operator fusion, and GPU acceleration for faster inference.
Unified Inference Interface: Standardizes input/output formats, preprocessing steps, and runtime behavior—reducing discrepancies between training and production environments.
Wide Ecosystem Support: Compatible with a broad array of tools including MLflow, Hugging Face, NVIDIA Triton, Ray Serve, Seldon MLServer, and vLLM pipelines.
Lightweight and Portable Models: Enables Cake to package and deploy efficient models across cloud, edge, and containerized environments, supporting both CPU and GPU execution.
ONNX is used as a unifying model exchange layer—especially in deployment scenarios where speed, portability, and runtime flexibility are critical. It is common across use cases such as document intelligence, lightweight NLP inference, vision tasks, and real-time classification services, and serves as the deployment target for models developed in PyTorch or exported from Hugging Face Transformers. By adopting ONNX, you can ensure its AI models are portable, performant, and production-ready across diverse environments—accelerating deployment velocity and maximizing infrastructure flexibility.