GraFIT

Loads Garmin FIT files into a SQLite database and visualizes them in Grafana.

GraFIT parses .fit files (activities, sleep, GPS, etc.) using python-fitparse, writes them to SQLite using a schema generated from the FIT profile, and ships with Grafana provisioning (datasource + dashboards) so you can browse the data immediately.

Install

uv tool install grafit

Or run without installing:

uvx grafit <command> [options]

Requires Python 3.11+.

Usage

uv run grafit <command> [options]

Commands

• init <db_path> — create and initialize the SQLite database.
• import <path> <db_path> — import a FIT file or directory of FIT files into the database.
  • --force reimport files already present in the database.
  • --no-recursive do not recurse into subdirectories.
  • --fetch-weather look up historical hourly weather (temperature, apparent temperature, humidity) for each session's start coordinates and date from the Open-Meteo archive. Failures are logged and skipped — the FIT import itself never fails because of weather.
• backfill-weather <db_path> — fetch weather for sessions already imported without --fetch-weather. Safe to re-run: only sessions missing weather are queried; rate-limited or failed sessions are picked up on the next run.
  • --force re-fetch weather even for sessions that already have it.
• stats <db_path> — show row counts per table.
• purge <db_path> --file-hash <sha256> — remove all rows imported from a given file.
• generate-schema <output> — write the full DDL SQL to a file.
• install-provisioning <dest> — copy the bundled Grafana provisioning (datasource + dashboards) to a directory.
  • --force overwrite the destination if it exists.
• demo-data <dest> — generate a synthetic Garmin FIT tree (see Demo data).
  • --days days of history (default 365), --end-date YYYY-MM-DD last day (default yesterday), --seed random seed, --force write into a non-empty directory.
• demo-weather <db_path> — fill weather_hourly with modelled Los Angeles weather for demo data, without calling Open-Meteo.

Global flag: -v / --verbose for debug logging.

Upgrading

The schema is generated from the FIT profile, so upgrading GraFIT (or its python-fitparse dependency) can add new fields. On the next import/init, missing columns are added to existing tables automatically (you'll see a Schema: added N new column(s) notice). New columns are NULL for already-imported rows — to backfill history, re-import the affected files with --force or rebuild the database from your FIT source.

Grafana

A docker-compose.yml is provided to run Grafana with the SQLite datasource plugin against ./fit_data.db and the bundled dashboards mounted from ./grafana-provisioning.

Note: the bundled compose file enables anonymous admin access and disables the login form. It is intended for local use only — do not expose it to a network.

Drop your FIT files into ./GARMIN and bring it up:

docker compose up

A grafit-init container installs the bundled provisioning, runs grafit init, and runs grafit import ./GARMIN against shared volumes; Grafana waits for it to finish before starting. Re-running docker compose up reuses the database — already-imported files are skipped by hash.

Grafana is then available at http://localhost:3000.

Demo data

For demo/testing purposes, GraFIT can generate a synthetic dataset for a fictional Los Angeles athlete — a year of activities, sleep, stress, body battery, HRV, SpO2, saved locations etc:

docker compose -f docker-compose.demo.yml up

That generates the FIT files into their own volume, imports them into a separate database and starts Grafana on http://localhost:3000 with everything populated. Set GRAFIT_DEMO_DAYS=90 to generate less history — a year takes a couple of minutes and about 300 MB of database, 90 days about 77 MB.