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DearDiary.jl

An ML experiment tracker made in Julia.

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Features

  • Tracking surface: projects, experiments, iterations, parameters, metrics, tagged resources. Iterations form parent/child trees for HPO sweeps and distributed workers, and a status enum records failures with the captured exception text.
  • Server + client: built-in REST API for remote logging and a native Julia client (DearDiary.connect, with_iteration, …) that auto-finalises iterations whether the body returns or throws.
  • Bit-exact environment replay: every iteration records a Manifest.toml snapshot, the Julia version, and the git SHA. DearDiary.restore(iteration_id) writes the captured environment to a fresh directory for Pkg.instantiate.
  • Pluggable storage: portable DuckDB metadata store. Artifact bytes live inline, on a local filesystem, or in any S3-compatible object store (AWS S3, MinIO, Cloudflare R2). migrate_artifacts! moves rows between backends on a live database.

Motivation

Reproducible ML depends on knowing what code, data, and environment produced each result. Existing trackers either route every interaction through a Python client (MLflow, Weights & Biases, Aim) or capture environments as pip freeze strings that re-resolve their transitive dependencies at install time. DearDiary is Julia-native and persists the exact Manifest.toml per run, so you can reconstruct an iteration months later by running DearDiary.restore(iteration_id). You use the same tracking API whether you run a single-file DuckDB database on a laptop or a multi-worker S3-backed deployment.

Contributing

Open an issue or pull request on the GitHub repository. Follow the existing code style and include tests for new features.