In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then use Ax’s ask-tell optimization loop to run constrained Bayesian optimization, multi-objective optimization, and parameter-constrained experimentation. Along the way, we visualize convergence, inspect the Pareto frontier, use Ax’s built-in analysis tools, and persist the experiment for future reuse.

import importlib, subprocess, sys

def _ensure(module, pip_name=None):

try:

importlib.import_module(module)