Adaptive Experimentation with Meta’s Ax: A Practical Coding Guide

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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.

Copy CodeCopiedUse a different Browserimport importlib, subprocess, sys def _ensure(module, pip_name=None): try: importlib.import_module(module) except ImportError: print(f"Installing {pip_name or module} ...") subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pip_name or module]) _ensure("ax", "ax-platform") _ensure("sklearn", "scikit-learn") import logging, warnings, time import numpy as np import matplotlib.pyplot as plt warnings.filterwarnings("ignore") logging.getLogger("ax").setLevel(logging.WARNING) from ax.api.client import Client from ax.api.configs import RangeParameterConfig, ChoiceParameterConfig from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import StratifiedKFold, cross_val_score np.random.seed(0)

We begin by preparing the Colab environment and installing the required packages for Ax and …

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