DearDiary.jl

An ML experiment tracker written in Julia.

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.
• Environment capture and 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: single-file 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.

The docs cover a quickstart plus guides for the model registry, child iterations, filesystem and S3 artifact storage, and Manifest-based reproducibility.

Installation

using Pkg
Pkg.add("DearDiary")

or from the REPL, type ]add DearDiary.

Quickstart

using DearDiary
DearDiary.initialize_database()

project_id, _ = create_project("Iris classification")
experiment_id, _ = create_experiment(project_id, DearDiary.IN_PROGRESS, "Decision-tree sweep")

with_iteration(experiment_id) do iter
    create_parameter(iter.id, "max_depth", 7)
    create_metric(iter.id, "accuracy", 0.96)
end

with_iteration opens an iteration, runs the body, marks it SUCCEEDED on a clean return or FAILED (carrying the captured exception text) on a throw, and snapshots the active Julia environment so it can be reconstructed later. The guides cover remote logging through the REST client, the model registry, and the reproducibility workflow.

Motivation

Reproducible ML depends on knowing what code, data, and environment produced each result. Established trackers such as MLflow, Weights & Biases, and Aim are Python-first, and Python environment capture commonly records dependency specifications that the installer re-resolves at install time. DearDiary is Julia-native and persists the Manifest.toml for each run, so the captured dependency environment can be reconstructed later by running DearDiary.restore(iteration_id). The same tracking API applies whether the database is a single-file DuckDB store 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.