tinytrainlog 0.1.6

Tiny train logger

🚅🪵 Tiny Train Log 🚅🪵

Structured, queryable logging for ML experiments. Track metrics across machines, merge results into one SQLite database, and analyze everything with plain SQL.

Quick start

pip install tinytrainlog

from tinytrainlog import MetricsLogger

with MetricsLogger("./runs", run_name="lr-sweep-3e4") as log:
    log.set_config({"model": "resnet50", "lr": 3e-4, "epochs": 10})
    log.add_tags(["sweep", "baseline"])

    for epoch in range(10):
        train_loss = train(model, loader)
        log.log_epoch(epoch=epoch, train_loss=train_loss)

        val_loss, val_acc = evaluate(model, val_loader)
        log.log_eval(epoch=epoch, val_loss=val_loss, val_acc=val_acc)

        torch.save(model.state_dict(), log.checkpoint_path(epoch=epoch))

    log.log_test(test_acc=0.94, test_loss=0.21)

Everything lands in a single runs.db SQLite file — query it however you like:

import sqlite3

conn = sqlite3.connect("./runs/runs.db")

# Compare learning rates across runs
conn.execute("""
    SELECT c.run_name, c.value AS lr, e.value AS val_acc
    FROM config c
    JOIN eval e USING (run_name)
    WHERE c.key = 'lr' AND e.key = 'val_acc'
    ORDER BY CAST(e.value AS REAL) DESC
""").fetchall()

Multi-server merging

Ran experiments on multiple machines? Merge them into one database:

MetricsLogger.merge(target_dir="./all_runs", source_dir="/mnt/gpu-box-1/runs")
MetricsLogger.merge(target_dir="./all_runs", source_dir="/mnt/gpu-box-2/runs")