Reproducible ML

Overview

Reproducibility means: same code + same data + same config → same result. It is a prerequisite for trusting comparisons, debugging regressions, and shipping. Three pillars: seed everything, pin everything, version the data.

When to use

• Results vary run-to-run.
• Setting up a new project or research codebase.
• Preparing work others must reproduce (papers, audits, reviews).

Pillar 1 — Seed everything

import os, random, numpy as np, torch