pytorch-model-cli

Extract weights from your PyTorch .pth file and reimplement inference in C/C++, then compile into a standalone executable. This approach removes Python runtime requirements while preserving model functionality for deployment.

Yes, weight extraction pulls parameters from PyTorch models into portable formats like JSON or binary that C/C++ and other languages can load directly, enabling cross-language inference pipelines.

Extract model weights, implement the forward pass in C/C++, add image loading and preprocessing, then compile into a native binary. This creates a self-contained classifier executable.

No. Once converted to a native CLI tool with C/C++ inference and compiled weights, the executable runs standalone without requiring Python, Python libraries, or PyTorch installed.

Load extracted weights into memory, then implement layer operations matching your model's architecture in C/C++. Reference outputs from the original model validate correctness before deployment.

Generate reference outputs from PyTorch, process the same inputs through your compiled C/C++ binary, then compare results. Build and test workflows ensure inference accuracy across both implementations.