SustainaML AutoML

A lightweight, energy‑aware AutoML toolkit. A Flask back‑end trains multiple ML frameworks while tracking CO₂/energy with , and a Streamlit front‑end lets you explore results, feature importance, and hyper‑parameter impact, all in your browser.

Table of Contents

1. Features

2. Project Structure

3. Quick Start

4. API Reference

5. Package Versions

6. Troubleshooting

7. Acknowledgement

1. Features

• AutoML search across frameworks — FLAML, “H2O”, MLJAR

• Time‑budgeted training with incremental / warm‑start loops

• Energy & CO₂ tracking via CodeCarbon (offline mode)

• Interactive Streamlit UI ▸ upload CSV ▸ pick frameworks & algorithms ▸ edit hyper‑parameters ▸ visualise leaderboards, scatter plots, feature importance, pipeline diagrams, time‑budget trade‑offs

• REST API (/run_automl) for programmatic access

2. Project Structure

├── backend.py # Flask API – training & carbon tracking

├── frontend.py # Main Streamlit interface (rich UI)

├── app.py # Minimal Streamlit demo (optional)

└── README.md # You are here

Tip : frontend.py and app.py do not share state; keep one running at a time.

3. Quick Start

1 · Clone & create env

git clone <your‑repo‑url> sustainaml-automl

cd sustainaml-automl

python -m venv venv

source venv/bin/activate # Windows: venv\Scripts\activate_ 2 · Install requirements

pip install -r requirements.txt

#OR – for the exact versions we tested

#pip install -r requirements_locked.txt

3 · Run the stack

In two terminals or two split panes 

#Terminal 1 – REST back‑end

python backend.py

#Terminal 2 – UI front‑end

streamlit run frontend.py # or streamlit run app.py

Visit the URL Streamlit prints (default http://localhost:8501). Upload a CSV where the last column is the target label.

4. API Reference

POST /run_automl

{

"frameworks": ["FLAML", "H2O", "MLJAR"],

"algorithms": {

  "FLAML": {"RF": true, "XGBoost": true},
  
  "H2O":   {"GLM": true}
  
  ..............................

},

"hyperparams": {

  "FLAML": {"RF": {"n_estimators": 150}}
  
  .................................

},

"time_budget":10, 30, 60, 120

"data": "<pandas.DataFrame>.to_json()" // last col = y

}

Returns per‑algorithm metrics, carbon/energy figures, hyper‑params & feature importances.

5. Package Versions

6. Troubleshooting

Symptom Fix

-ModuleNotFoundError: Re‑run pip install -r requirements.txt inside the activated venv

• Tracker raises No GPU found: Ignore – CodeCarbon falls back to CPU measurement

• Streamlit shows blank page: Refresh browser; check that backend.py is still running

7. Acknowledgements

CodeCarbon Library for carbon estimation & the open‑source community • 🌱