In this tutorial, we implement NVIDIA cuML as a GPU-accelerated machine learning framework and build a practical workflow that demonstrates how RAPIDS can accelerate familiar data science and machine learning tasks. With GPU-accelerated pipelines now a default expectation for production ML workloads in 2026, understanding where cuML delivers the biggest gains—and where it does not—has become essential for practitioners.

What You Will Learn

We begin by configuring the GPU environment and examining cuml.accel, which lets us accelerate existing scikit-learn workloads with minimal code changes, before moving to the native cuML API for direct CuPy and cuDF interoperability. We then benchmark CPU and GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN, using synchronized timing to obtain meaningful performance measurements.

We also build GPU-based manifold-learning and clustering pipelines with UMAP, t-SNE, HDBSCAN, and trustworthiness metrics; explore high-throughput forest inference with FIL; validate GPU-generated SHAP explanations; perform hyperparameter optimization with scikit-learn meta-estimators; and finally serialize trained models while examining portability between GPU and CPU environments.

Environment Setup and Configuration

The script below establishes baseline parameters, seeds, and helper utilities. It also verifies that an NVIDIA GPU is present and prints device information via nvidia-smi before any workloads run.

import os
import sys
import time
import json
import shutil
import warnings
import subprocess
import importlib
import traceback
warnings.filterwarnings("ignore")

QUICK = False
SEED = 42
SCALE = 0.25 if QUICK else 1.0
NMAIN = int(200000 * SCALE)
D_MAIN = 64
NRF = int(50000 * SCALE)
D_RF = 32
NNNINDEX = int(50_000 * SCALE)
NNNQUERY = int(5_000 * SCALE)
NDBSCAN = int(20000 * SCALE)
NMANIFOLD = int(60000 * SCALE)
NACCEL = int(80000 * SCALE)
RESULTS = []
NOTES = []

def banner(title):
line = "=" * 78
print(f"\n{line}\n  {title}\n{line}", flush=True)

def section(title, fn, args, *kwargs):
banner(title)
t0 = time.perf_counter()
try:
fn(args, *kwargs)
except Exception:
print(f"[!] Section skipped due to an error:\n{traceback.format_exc()}")
print(f"[section wall time: {time.perf_counter() - t0:.1f}s]", flush=True)

def bootstrap():
if shutil.which("nvidia-smi") is None:
raise SystemExit(
"No NVIDIA GPU found. In Colab: Runtime > Change runtime type > GPU."
)
print(subprocess.run(
["nvidia-smi",
"--query-gpu=name,memory.total,computecap,driverversion",
"--format=csv"],
capture_output=True, text=True).stdout)
try:
import cuml
print("cuM...

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