Principle:LaurentMazare Tch rs TorchScript Export
| Knowledge Sources | |
|---|---|
| Domains | Model_Deployment, Interoperability |
| Last Updated | 2026-02-08 14:00 GMT |
Overview
Process of converting a Python PyTorch model into a serialized TorchScript representation that can be loaded and executed in non-Python environments like Rust.
Description
TorchScript export converts a PyTorch model from Python into an intermediate representation (IR) that captures the model's computation graph. Two approaches exist: tracing (records operations on example inputs) and scripting (analyzes Python source code). The exported .pt file contains both the model architecture and weights, making it a self-contained deployment artifact. This is the bridge that enables Python-trained models to run in Rust via tch-rs.
Usage
Use this when you need to deploy a PyTorch model in Rust. Trace the model in Python with example inputs, save the .pt file, then load it in Rust via CModule::load. Tracing is preferred for models with fixed control flow; scripting handles dynamic control flow.
Theoretical Basis
TorchScript Export Methods:
Tracing: torch.jit.trace(model, example_input)
- Records all operations during forward pass with example
- Cannot capture data-dependent control flow
- Simpler and works for most CNN models
Scripting: torch.jit.script(model)
- Analyzes Python source code statically
- Handles if/for/while based on tensor values
- More complex but handles dynamic architectures
Output: .pt file containing serialized TorchScript IR + weights