Git-style versioning for ML experiments. Track hyperparameters, metric curves, and artifact hashes across every training run — searchable in milliseconds.
Don't take our word for it.
Verify every claim row by row. Each ✓ is backed by a code snippet below. No marketing copy — just receipts.
◐ = available with paid plan or significant configuration overhead · last verified 2026-02-24
The table checks out.
Diff any two runs like `git diff`
See exactly what changed between run_c1e9b7 and run_a8f3d2 — every param, every metric delta, every artifact hash.
2 lines. Any training script. Zero config.
Drop into any PyTorch, TensorFlow, or JAX script. No YAML files, no dashboard login, no SDK wrapper classes.
Promote models to production via API
Wire your model registry into GitHub Actions, GitLab CI, or any HTTP client. One API call promotes a run to staging.
The relief of total recall.
“I reproduced a NeurIPS result from 8 months ago at 1:47 AM the night before the rebuttal deadline. track diff found the exact learning rate schedule in 3 seconds. That's the whole product pitch right there.”
“We had 47 spreadsheet tabs of hyperparameter grids. I spent 4 hours migrating to track on a Friday afternoon. By Monday, the whole team was using it. The CLI is stupid fast — `track log` adds 0.2ms per step.”
“Our MLOps pipeline promotes models to production via `track registry promote`. Failed experiments now block CI automatically if val_loss regresses. We caught 3 silent regressions last quarter that would have shipped.”
Zero config.
Total recall.
One package install. Works with PyTorch, TensorFlow, JAX, and any Python training script. Your first run is tracked in under 60 seconds.
Python 3.8+ · MIT License · 2.3MB install · no system dependencies