This article delves into how the zkPyTorch compiler launched by Polyhedra Network integrates the mainstream AI framework PyTorch with zk-SNARKs technology, reducing the development threshold for ZKML and achieving credible verification and privacy protection in the machine learning inference process. It covers its three core modules (preprocessing, quantization, circuit optimization), key technologies (DAG, lookup tables, FFT convolution), multi-level circuit optimization strategies, and showcases breakthroughs in performance and accuracy of zkPyTorch through empirical data from VGG-16 and Llama-3.