# Mltrackr – ML experiment tracking in 2 lines, no server, no account > Source: > Published: 2026-06-30 12:07:55+00:00 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.