A Python package for versioning and monitoring dataset changes throughout the machine learning lifecycle.
dstrack helps data scientists and ML engineers track how datasets evolve over time, catching schema drift, distribution shifts, and unexpected mutations before they silently break pipelines or degrade model performance.
pip install dstrackRequires Python 3.11 or later.
Initialize a store in your project, then take your first snapshot.
1. Create a local store - this adds a .dstrack/ directory at the current location:
dstrack initℹ Generating local store structure at /path/to/.dstrack.
✔ Finished creating local store: /path/to/.dstrack
2. Track a dataset - point dstrack at a data file to snapshot it. Given a small
data.csv:
id,name,value
1,alpha,10.5
2,beta,20.0
3,gamma,15.25dstrack track data.csvℹ Reading data.csv and computing snapshot...
✔ Snapshot <snapshot-uuid> written (new dataset, dataset <dataset-uuid>).
ℹ Stored at /path/to/.dstrack/datasets/<dataset-uuid>/snapshots/<snapshot-uuid>.json
3. Re-track as the data changes - running track again on the same path extends the
dataset's lineage instead of starting a new one:
dstrack track data.csv✔ Snapshot <snapshot-uuid> written (continued lineage, dataset <dataset-uuid>).
Each snapshot captures the file's schema, a content fingerprint, and per-column
statistics. See the Getting Started guide
for options such as --name, --reader, and --dataset-id.
- Dataset versioning - snapshot a dataset and track its lineage across pipeline stages
- Rich snapshots - schema hash, content fingerprint, and per-column statistics (ranges, null rates, and more)
- CSV out of the box - pure standard-library reader, no heavy dependencies
- Lightweight CLI - a small, git-like local store you can commit alongside your code
Change detection and drift monitoring are on the way - comparing snapshots, surfacing schema and distribution shifts, and failing CI on drift. See the roadmap for what's planned.