What is the method for deploying ONNX models?

There are multiple ways to deploy ONNX models, depending on different needs and environments: 1. Using ONNX Runtime: ONNX Runtime is a high-performance, cross-platform inference engine that can directly load and run ONNX models. It supports various hardware accelerators, including CPU, GPU, and dedicated accelerators, suitable for local and cloud deployment. 2. Using the inference engine of deep learning frameworks: Many deep learning frameworks (such as TensorFlow, PyTorch, and Caffe) provide support for ONNX models. You can use the inference engine of these frameworks to load and run ONNX models. For example, PyTorch provides the torch.onnx API for loading and running ONNX models. 3. Using a dedicated hardware’s inference engine: Some hardware vendors provide inference engines specifically for their hardware accelerators, which can load and run ONNX models. For example, NVIDIA TensorRT is a high-performance inference engine that can accelerate ONNX model inference on NVIDIA GPUs. 4. Using a cloud service provider’s platform: Many cloud service providers offer inference services based on ONNX models. You can upload ONNX models to the cloud and then use the provider’s API for inference. The choice of method depends on specific needs and environments. If high-performance local deployment is needed, consider using ONNX Runtime or a dedicated hardware inference engine. If flexibility and cross-platform support are needed, consider using the inference engine of deep learning frameworks. If cloud deployment is needed, consider using a cloud service provider’s platform.