DearDiary.jl: A machine learning experiment tracking tool for Julia (github.com)

DearDiary.jl is a new, lightweight experiment-tracking package written in Julia that provides a complete solution for logging experiments, models, hyperparameters and results. It ships with a portable SQLite backend for local storage and a built-in REST API server for remote logging and querying, making it easy to record runs from local scripts or distributed jobs. The package is available through the Julia package manager (Pkg.add("DearDiary") or ]add DearDiary in the REPL) and includes a tutorial to get started quickly. For the Julia ML/AI community this fills a practical gap: many existing trackers are heavyweight or poorly integrated with Julia workflows. DearDiary.jl emphasizes simplicity, reproducibility and portability (SQLite makes experiment databases easy to move or version-control) while the REST API enables remote clients, dashboards or multi-node logging. As an open-source project on GitHub, it welcomes issues and pull requests, so users can extend backends, add integrations (cloud storage, visualization tools, or MLFlow bridges) or harden production features — a promising base for reproducible experiment management in Julia ecosystems.