Simplifying Deep Learning Model Deployment
The deep learning (DL) models that are deployed in production environments are often different from the models that are fresh out of the training process. They are usually augmented to handle incoming requests with the highest performance. However, the target environments are often too broad, so a lot of customization is necessary to cover vastly different deployment settings. To overcome this difficulty, you can make use of open neural network exchange (ONNX), a standard file format for ML models. In this chapter, we will introduce how you can utilize ONNX to convert DL models between DL frameworks and how it separates the model development process from deployment.
In this chapter, we’re going to cover the following main topics:
- Introduction to ONNX
- Conversion between TensorFlow and ONNX
- Conversion between PyTorch and ONNX
