pytorch-deployment

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.

2. The ONNX Bridge

ONNX (Open Neural Network Exchange) is a cross-platform format. A model exported to ONNX can be run by Microsoft's ONNX Runtime, which is often faster than standard PyTorch for inference.

3. Quantization

Reducing weights from float32 (4 bytes) to int8 (1 byte). This shrinks the model size by 4x and can speed up inference by 2-3x on CPUs.

Quick Reference: Export Patterns

import torch

model = MyModel().eval()
example_input = torch.randn(1, 3, 224, 224)

# 1. Tracing (Most common)
traced_model = torch.jit.trace(model, example_input)
traced_model.save("model_jit.pt")

# 2. Scripting (For dynamic logic)
scripted_model = torch.jit.script(model)
scripted_model.save("model_script.pt")

# 3. ONNX Export
torch.onnx.export(model, example_input, "model.onnx", 
                  input_names=['input'], output_names=['output'],
                  dynamic_axes={'input': {0: 'batch_size'}})

Critical Rules