Mltrackr – ML experiment tracking in 2 lines, no server, no account

Mltrackr launches a lightweight ML experiment tracking tool that requires no server, no account, and no configuration, logging metrics in two lines of code. The open-source Python package stores data locally in a SQLite file and provides a visual dashboard, hyperparameter suggestions, and anomaly detection. It aims to replace heavier tools like MLflow and Weights & Biases for quick, offline tracking.

Track ML experiments in 2 lines of code. No server. No account. No config. You're running a training loop. You want to know which hyperparameters worked best. You don't want to: - Set up a tracking server - Create an account on any service - Write to a cloud API - Configure environment variables - Install 47 dependencies mltrackr is the answer. Install it, wrap your loop, open a beautiful local dashboard. Done. 1. Install pip install mltrackr This installs the mltrackr command and import mltrackr Python package. 2. Generate a ready-to-run example python -m mltrackr init --framework plain -o demo.py On most systems mltrackr init works directly. If not, use python -m mltrackr instead. 3. Run the demo creates 6 fake training runs python demo.py 4. Inspect results in the terminal python -m mltrackr list python -m mltrackr best accuracy python -m mltrackr suggest accuracy 5. Open the visual dashboard python -m mltrackr ui Then open http://localhost:7000 in your browser. Press Ctrl+C to stop. python import mltrackr with mltrackr.run "resnet-baseline", tags= "cv", "baseline" : mltrackr.log lr=1e-3, batch size=64, optimizer="adam" for epoch in range 50 : loss, acc = train one epoch model, dataloader mltrackr.log loss=loss, accuracy=acc, epoch=epoch mltrackr.note "Solid baseline - try lr=5e-4 next" If 'mltrackr' works directly on your system: mltrackr ui mltrackr list mltrackr best accuracy mltrackr suggest accuracy mltrackr report If not e.g. Windows , use: python -m mltrackr ui python -m mltrackr list python -m mltrackr best accuracy Everything is saved locally in ~/.mltrackr/experiments.db . A single SQLite file. Copy it, back it up, open it in any SQLite browser. Got good results?Run mltrackr share accuracy to generate a ready-to-post Twitter/X or Hacker News summary. If mltrackr saved you time, a ⭐ on GitHub goes a long way The real problem: you're hacking on a model, you want to log some metrics, but setting up MLflow takes 15 minutes and W&B wants you to create an account and send your data to the cloud. So you end up writing metrics to a text file or just... not tracking anything. Then you forget which hyperparameters worked. Then you run the same failed experiment again. mltrackr is the experiment tracker that's actually available when you need it. mltrackr | MLflow | Weights & Biases | | |---|---|---|---| | Setup time | 5 seconds | ~15 minutes | ~5 minutes | | Requires account | ❌ No | ❌ No | ✅ Yes | | Requires running server | ❌ No | ✅ Yes | ❌ No cloud | | Works offline | ✅ Always | ❌ No | | | Data stays local | ✅ Always | ✅ Yes | ❌ No | | Live anomaly detection | ✅ Built-in | ❌ No | | | Hyperparameter suggestions | ✅ Built-in | ❌ No | | | Auto-generated reports | ✅ Built-in | ❌ No | ❌ No | | Free forever | ✅ MIT | ✅ Apache | Wrap any loop. Log any value. Works with every framework. python import mltrackr with mltrackr.run "gpt-finetune", tags= "nlp", "v3" : mltrackr.log lr=2e-5, epochs=3, model="gpt2" for step, batch in enumerate dataloader : loss = model.train step batch mltrackr.log loss=loss.item , step=step mltrackr ui Opens at http://localhost:7000 — a fast, dark-mode single-page app with: - Searchable run list with inline sparkline charts in the sidebar Trend indicators ↑ ↓ showing whether each metric is improving Side-by-side comparison of any runs you select best value highlighted Auto-generated time-series charts with gradient fills Metric progress bars showing where the latest value sits in its historical range- Global statistics view — success rate, most-logged metrics, run timeline - Auto-refresh every 5 seconds — open while training, watch it update mltrackr.configure watch nan check=True, divergence window=5, plateau window=15 with mltrackr.run "training" : for epoch in range 100 : mltrackr.log loss=compute loss Automatically warns if: loss → NaN, loss diverges for 5 epochs, loss plateaus for 15 epochs and suggests adjusting LR Stop wasting GPU hours on runs that are already failing. mltrackr suggest accuracy Analyzes your run history and tells you which hyperparameter values are statistically correlated with better results. No black box — plain English insights like: Best config: lr=0.001 → avg accuracy 0.943 vs 0.871 for other values, +8.2% Next experiment: try batch size=128 — larger batches correlated with +5.1% accuracy mltrackr report --output results.md Generates a thesis-ready markdown report with: - Summary statistics total runs, completion rate, best configurations - Chronological experiment timeline - Key findings computed automatically - Notes from all your runs - Optional AI narrative: mltrackr report --ai uses local Ollama, no API keys mltrackr init plain Python example mltrackr init --framework pytorch PyTorch training loop mltrackr init --framework sklearn scikit-learn grid search mltrackr init --framework keras Keras callback Generates a complete working script you can run immediately. | Framework | How | |---|---| PyTorch | mltrackr.log loss=loss.item , acc=acc inside the training loop | scikit-learn | mltrackr.log params, cv score=score in your hyperparam loop | Keras / TF | One-file TrainlogCallback for model.fit | HuggingFace | Custom TrainerCallback — see examples/huggingface example.py | XGBoost / LightGBM | Log in the eval callback | JAX / Flax | Log at end of each training step | Plain Python | Anything that produces a number | python import mltrackr ── Tracking ────────────────────────────────────────────────────────────────── with mltrackr.run "name", tags= "tag1", "tag2" as run id: mltrackr.log accuracy=0.95, loss=0.05 log any key-value pairs mltrackr.note "Cosine LR schedule helped a lot" attach plain-text notes mltrackr.tag run id, "production" add tags after the fact mltrackr.tag "experiment-name", "best" also works by name ── Querying ────────────────────────────────────────────────────────────────── runs = mltrackr.get runs all runs, newest first best = mltrackr.get best run "accuracy" highest final value best low = mltrackr.get best run "loss", mode="min" lowest final value cmp = mltrackr.compare runs 1, 2, 3 list of run dicts ── Anomaly detection ───────────────────────────────────────────────────────── mltrackr.configure watch nan check=True, warn on NaN/Inf values divergence window=5, warn if metric diverges for N steps plateau window=15, warn if metric plateaus for N steps enabled=True, Or temporarily with a context manager: with mltrackr.watch divergence window=3 : stricter watch for this block mltrackr.log loss=0.5 ── Export & analysis ───────────────────────────────────────────────────────── mltrackr.export csv "results.csv" mltrackr.export json "results.json" mltrackr.generate report "report.md", use ollama=False suggestions = mltrackr.suggest "accuracy", mode="max", top n=3 mltrackr.clear all deletes everything irreversible ── Config ──────────────────────────────────────────────────────────────────── mltrackr.configure verbose=False suppress auto-summary panels after each run Dashboard mltrackr ui open at localhost:7000 mltrackr ui --port 8080 --no-browser custom port, no auto-open Inspect runs mltrackr list rich table, newest first mltrackr list --limit 50 mltrackr compare 1 2 3 side-by-side metric comparison mltrackr best accuracy best run for a metric mltrackr best loss --mode min Annotate mltrackr tag 42 production tuned add tags to run 42 mltrackr note 42 "Try cosine annealing" add note to run 42 Analyse mltrackr stats aggregate statistics mltrackr suggest accuracy hyperparameter recommendations mltrackr suggest loss --mode min --top 5 Generate mltrackr report write report.md mltrackr report -o results.md --ai with Ollama AI narrative mltrackr init --framework pytorch generate example script Export / clean mltrackr export --format csv -o data.csv mltrackr export --format json -o data.json mltrackr clear delete all asks confirmation Share mltrackr share accuracy generate Twitter/X + HN ready post mltrackr share loss --mode min for metrics where lower is better SQLite — ~/.mltrackr/experiments.db . One file. No server. Inspect it with any SQLite browser. Back it up with cp . Flask — the dashboard is a local Flask server. Vanilla JS, Chart.js, zero npm, zero build step. Thread-local state — each training job in its own thread gets an isolated run context. Concurrent experiments just work. Git-aware — captures the current commit hash via git rev-parse HEAD . Silently skipped outside a git repo. Watch hooks — anomaly detection runs inside every log call. Zero external services, works offline. mltrackr init --framework pytorch -o train.py python train.py mltrackr ui That's the whole flow. Five commands. Zero config. Done ✅ - Live anomaly detection configure watch - Auto-generated experiment reports mltrackr report , Ollama support - Hyperparameter suggestions mltrackr suggest - Quick-start example generator mltrackr init - Sparkline charts in sidebar with trend indicators - Metric progress bars and trend arrows in detail view - Framework examples: PyTorch, scikit-learn, Keras, HuggingFace Coming up - mltrackr.log artifact "model.pt" — save file paths alongside metrics - Native PyTorch TrainlogCallback pip-installable plugin - VS Code extension — inline run summary on hover - mltrackr serve — shareable read-only dashboard URL ngrok/localtunnel - Team sync via shared git-tracked SQLite - Slack / Discord webhook on run completion Have an idea? Open a feature request https://github.com/NaiaLorente/Datalog/issues/new — or submit a PR. See CONTRIBUTING.md /NaiaLorente/datalog/blob/main/CONTRIBUTING.md . TL;DR: pip install -e . , make your change, open a PR. All contributions welcome — typos, docs, features, bug fixes. MIT — use it however you want, forever.