atcold/torch-torch7-tools

Torch7-tools is a set of utility scripts that fill in gaps in Torch7, an older framework for building machine learning models. The author created these scripts during day-to-day work and was allowed to share them publicly. They handle practical tasks that Torch7 didn't do on its own out of the box. The main tools address four specific needs. "Network lightener" strips out unnecessary data (like gradients and temporary values) from a saved model so the file takes up less space. "Gradient repopulation" reverses that process: when you reload a slimmed-down model to continue training, it restores the internal data that was removed. The command line parser is a demonstration of how to pass configuration settings to a script via the terminal using flags and options. Finally, the Cuda-to-Float converter takes a model built for GPU-based processing and produces an equivalent version that runs on regular CPU hardware. Someone working with Torch7 models would use these scripts to save disk space, resume training sessions, or make their models runnable on machines without specialized graphics cards. For example, if you trained a model on a powerful GPU server but wanted to run it on a standard laptop, the converter would handle that transition. The README notes that the first two tools (the lightener and gradient repopulator) are deprecated because a newer, more comprehensive package called net-toolkit is being released to replace them. The collection is written in Lua, which was the primary language for Torch7 before the machine learning community largely moved to Python-based frameworks. It's a small, pragmatic set of tools rather than a polished product, essentially the author's personal utilities extracted from real work and made available for others who might face the same gaps.