GoodSeed

ML experiment tracker. Logs metrics and configs to local SQLite files, serves them via a built-in HTTP server, and visualizes them in the browser.

Full documentation at goodseed.ai/docs.

Install

pip install goodseed

Python 3.9+ required. No runtime dependencies.

For development:

pip install -e ".[dev]"

Quick Start

Log metrics and configs from a training script:

import goodseed

run = goodseed.Run(experiment_name="my-experiment")
run.log_configs({"learning_rate": 0.001, "batch_size": 32})

for step in range(100):
    loss = train_step()
    run.log_metrics({"loss": loss}, step=step)

run.close()

Your data is saved to a local SQLite file. You can also use with goodseed.Run(...) as run: to close the run automatically.

Then view your runs:

goodseed serve

Open the printed link in your browser to see your runs, metrics, and configs.

Coming from Neptune?

You can export your data from neptune.ai and import it into GoodSeed using neptune-exporter. See the migration guide for details.

Configuration

Variable	Description
GOODSEED_HOME	Data directory (default: ~/.goodseed)
GOODSEED_PROJECT	Default project name (default: default)

CLI

goodseed                   # Start the server (default command)
goodseed serve [dir]       # Start the server, optionally from a specific directory
goodseed serve --port 9000 # Use a custom port
goodseed list              # List projects
goodseed list -p default   # List runs in a project

Tests

pip install -e ".[dev]"
pytest tests/ -v

See DOCS.md for architecture details and API reference.