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Tracks traditional ML experiments in Domino using MLflow-based Experiment Manager. Covers auto-logging for sklearn/TensorFlow/PyTorch, manual logging, run comparison, and model registration.
How this skill is triggered — by the user, by Claude, or both
Slash command
/dominodatalab:experiment-trackingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides comprehensive knowledge for tracking ML experiments in Domino Data Lab using the built-in MLflow-based Experiment Manager.
This skill provides comprehensive knowledge for tracking ML experiments in Domino Data Lab using the built-in MLflow-based Experiment Manager.
Domino's Experiment Manager is built on MLflow and provides:
Experiment names must be unique across the entire Domino deployment. Always append username or project name to ensure uniqueness.
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)
| 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() |
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 |
npx claudepluginhub anthropics/claude-plugins-official --plugin dominodatalabSets up MLflow tracking server, configures autologging for ML frameworks, compares runs with metrics, and manages artifacts for reproducible ML workflows.
Sets up ML experiment tracking with MLflow or Weights & Biases: installs packages, initializes tools, and provides logging code for parameters, metrics, and artifacts.
Determines a user's MLflow use case (GenAI or traditional ML) and guides them through relevant quickstart tutorials and initial integration steps.