domino-experiment-tracking

name	domino-experiment-tracking
description	Track traditional ML experiments in Domino using the MLflow-based Experiment Manager. Covers experiment setup, auto-logging for sklearn/TensorFlow/PyTorch, manual logging, artifact storage, run comparison, and model registration. Use when training ML models, logging metrics and parameters, comparing model runs, or registering models.

Domino Experiment Tracking Skill

This skill provides comprehensive knowledge for tracking ML experiments in Domino Data Lab using the built-in MLflow-based Experiment Manager.

Key Concepts

Experiment Manager Overview

Domino's Experiment Manager is built on MLflow and provides:

• Automatic and manual logging of parameters, metrics, and artifacts
• Run comparison and visualization
• Model versioning and registry
• Integration with Domino projects and jobs

Critical Configuration

Experiment names must be unique across the entire Domino deployment. Always append username or project name to ensure uniqueness.

Related Documentation

• MLFLOW-BASICS.md - Auto-logging, manual logging
• COMPARING-RUNS.md - Run comparison, export
• MODEL-REGISTRY.md - Model registration & stages

Quick Start

import mlflow
import os

# CRITICAL: Experiment names must be unique across Domino deployment
username = os.environ.get('DOMINO_STARTING_USERNAME', 'unknown')
experiment_name = f"my-experiment-{username}"

# Set the experiment
mlflow.set_experiment(experiment_name)

# Enable auto-logging (easiest approach)
mlflow.autolog()

# Run training
with mlflow.start_run(run_name="my-first-run"):
    model.fit(X_train, y_train)

    # Optional: manually log additional items
    mlflow.log_param("custom_param", "value")
    mlflow.log_metric("custom_metric", 0.95)

Supported Frameworks

Framework	Auto-log Command
Scikit-learn	mlflow.sklearn.autolog()
TensorFlow/Keras	mlflow.tensorflow.autolog()
PyTorch	mlflow.pytorch.autolog()
XGBoost	mlflow.xgboost.autolog()
LightGBM	mlflow.lightgbm.autolog()
All at once	mlflow.autolog()

Environment Variables

Domino automatically configures MLflow to use the built-in tracking server. These variables are pre-set:

Variable	Description
MLFLOW_TRACKING_URI	Domino's MLflow server URL
DOMINO_STARTING_USERNAME	User running the experiment
DOMINO_PROJECT_NAME	Current project name
DOMINO_RUN_ID	Domino job run ID

Documentation Links

• Domino Experiment Tracking: https://docs.dominodatalab.com/en/latest/user_guide/da707d/track-and-monitor-experiments/
• Domino Model Registry: https://docs.dominodatalab.com/en/latest/user_guide/3b6ae5/manage-models-with-model-registry/