A command-line tool for logging and browsing ML experiments. Each run records hyperparameters, metrics, and notes in a PostgreSQL database managed by InsForge. A local web dashboard lets you visualize runs and compare results without leaving your machine.

Note: This project was built as an example application to demonstrate using InsForge as a backend-as-a-service. InsForge handles auth, the database, and the REST API — this tool is a thin CLI and dashboard on top of it.

Prerequisites

Python 3.10+
Docker (to run InsForge locally)
InsForge running on http://localhost:7130 — see the InsForge docs for the quickstart Docker command

Screenshots & Demo

The runs table lists every experiment with its hyperparameters, metrics, and timestamps in a single view.

Selecting an experiment reveals a metrics-over-time chart with dual axes for accuracy and loss.

The full workflow from tracker log to browsing results in the dashboard.

Installation

# From the example directory
cd examples/python-ml-experiment-tracker
pip install -e .

This installs the tracker command on your PATH.

Setup

1. Start InsForge

InsForge must be running before any tracker command will work. A typical local start looks like:

docker run -p 7130:7130 insforge/insforge

Refer to the InsForge documentation for your exact Docker command and any environment variables (database URL, secret key, etc.).

2. Register an account

tracker register
# prompts for email and password

3. Log in

tracker login
# prompts for email and password
# credentials are saved to ~/.config/ml-tracker/config.json

4. Create the database tables

Table creation requires admin access in InsForge. Run tracker init to check whether the tables already exist:

tracker init

If the experiments and runs tables are missing, the command prints the SQL you need to run. To run it via the dashboard, open http://localhost:7130 in your browser after starting InsForge, navigate to the SQL editor, and paste and execute the SQL below. Alternatively, submit it through the InsForge admin API.

CREATE TABLE IF NOT EXISTS experiments (
    id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    name TEXT NOT NULL UNIQUE,
    description TEXT,
    created_at TIMESTAMPTZ DEFAULT now()
);

CREATE TABLE IF NOT EXISTS runs (
    id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    experiment_id UUID REFERENCES experiments(id) ON DELETE SET NULL,
    experiment_name TEXT NOT NULL,
    run_name TEXT,
    status TEXT DEFAULT 'completed',
    params JSONB DEFAULT '{}',
    metrics JSONB DEFAULT '{}',
    started_at TIMESTAMPTZ DEFAULT now(),
    finished_at TIMESTAMPTZ,
    notes TEXT,
    created_at TIMESTAMPTZ DEFAULT now()
);

Run tracker init again to confirm the tables are accessible before logging any runs.

CLI Commands

Authentication

# Register a new account
tracker register

# Log in (saves tokens to ~/.config/ml-tracker/config.json)
tracker login

# Point at a non-default InsForge instance
tracker login --server http://my-insforge-host:7130

Logging runs

# Minimal — just experiment name and metrics
tracker log -e my-model -m accuracy=0.91 -m loss=0.32

# With hyperparameters, a run label, and notes
tracker log \
  -e bert-finetune \
  -r "run-lr1e-4" \
  -p learning_rate=1e-4 \
  -p batch_size=32 \
  -p epochs=5 \
  -m accuracy=0.94 \
  -m f1=0.93 \
  -m val_loss=0.21 \
  -n "Warmup schedule, no dropout"

# Provide explicit timestamps (ISO-8601)
tracker log \
  -e nightly-sweep \
  --started-at 2024-06-01T01:00:00Z \
  --finished-at 2024-06-01T03:42:00Z \
  -m rmse=0.045

--params / -p and --metrics / -m each accept KEY=VALUE and can be repeated. Numeric values are stored as floats; everything else is stored as a string.

Viewing runs

# List the 20 most recent runs across all experiments
tracker runs list

# Filter to one experiment
tracker runs list -e bert-finetune

# Show more results or paginate
tracker runs list --limit 50 --offset 50

# Full details for a specific run (use the ID from the list)
tracker runs get a1b2c3d4

Viewing experiments

# List all experiments
tracker experiments list

# Limit results
tracker experiments list --limit 10

Checking the setup

# Verify that required tables exist and are accessible
tracker init

Web Dashboard

The dashboard shows a summary of all experiments and lets you browse runs with their params and metrics.

# Start the dashboard (opens your browser automatically)
tracker serve

# Custom port
tracker serve --port 9000

# Skip opening the browser
tracker serve --no-browser

The dashboard runs at http://127.0.0.1:8765 by default and proxies all API requests to InsForge using your saved credentials. It handles token refresh automatically. Stop it with Ctrl+C.

You must be logged in (tracker login) before starting the dashboard, otherwise API requests will return an auth error.

Configuration

The CLI stores its config at ~/.config/ml-tracker/config.json (mode 0600). You can override the InsForge server URL at login time with --server; the value is persisted for subsequent commands.

Default server URL: http://localhost:7130