PyTorch - Deployment & Production Engineering

Deploying a model in a high-performance environment often means removing the Python dependency. This guide covers how to serialize models into formats that can be loaded in C++, optimized for edge devices, or executed in high-throughput inference engines like TensorRT.

When to Use

• Moving a model from a Jupyter Notebook to a production web server (FastAPI/Go/Rust).
• Embedding a neural network into a C++ application (LibTorch).
• Running inference on mobile devices (iOS/Android) or edge hardware (NVIDIA Jetson).
• Accelerating inference speed using specialized hardware backends (OpenVINO, TensorRT).
• Ensuring model reproducibility across different versions of PyTorch.

Core Principles

1. Scripting vs. Tracing

• Tracing: PyTorch runs the model once with "example data" and records all operations. Fast, but ignores Python control flow (if, for).
• Scripting: PyTorch compiles the Python source code of the module. Slower to prepare, but preserves logic and control flow.