This guide walks you through setting up an Ubuntu environment, training a LightGBM stock‑movement model in Python, exporting it to ONNX, and deploying fast, cross‑platform inference in Go using ONNX Runtime, complete with code snippets and project structure.
In this detailed engineering guide we diagnose a heavyweight ERNIE‑Base text‑classification service consuming 128 CPU cores and 96 GB RAM, then apply a three‑step optimization—model selection, structured pruning with PaddleSlim, and engine migration to ONNX Runtime—achieving a 75% reduction in resource usage while keeping recall above 99.5% and boosting inference speed by over 20%.
This article walks through exporting a PyTorch image‑classification model to ONNX, identifying a hidden layer node, adding it as an output, and using ONNX Runtime to retrieve both the final prediction and the intermediate feature tensor.
