Optimize For Gpu

cuML Reference

Review: 2026-10-01. Code below is illustrative unless explicitly described as CPU-tested. GPU execution, performance, GDS, and multi-GPU behavior require validation on target hardware.

cuML is NVIDIA's GPU-accelerated machine learning library within the RAPIDS ecosystem. It provides scikit-learn-compatible APIs for classification, regression, clustering, dimensionality reduction, preprocessing, and model selection. Performance depends on algorithm, shape, dtype, fallback behavior, and transfer cost; benchmark the complete pipeline on representative data.

Full documentation: https://docs.nvidia.com/cuml/26.08/

Table of Contents

1. Installation and Setup

2. Two Usage Modes

3. cuml.accel Accelerator Mode

4. Direct cuML API

5. Algorithm Catalog

6. Input/Output Type Handling

7. Preprocessing

8. Feature Extraction

9. Model Selection and Tuning

10. Forest Inference Library (FIL)

11. Multi-GPU with Dask

12. Model Serialization

13. Memory Management

14. Performance Optimization

15. Interoperability

16. Key Differences from sklearn

17. Common Migration Patterns

Installation and Setup

Use uv add in standalone examples; follow the user's existing project package manager when one is already configured.

uv add "cuml-cu12==26.8.*"    # For CUDA 12.x
uv add "cuml-cu13==26.8.*"    # For CUDA 13.x

cuML wheels are published directly to PyPI (since RAPIDS 25.10) — the --extra-index-url=https://pypi.nvidia.com extra index is no longer required.

Platform: Linux and WSL2 only (no native macOS or Windows). Requires: Python >= 3.11, scikit-learn >= 1.6, NVIDIA GPU with CUDA 12.x or 13.x support.

Verify:

import cuml
print(cuml.__version__)

from cuml.datasets import make_blobs
X, y = make_blobs(n_samples=1000, n_features=10)
print(f"Generated {X.shape[0]} samples on GPU")

Two Usage Modes

1. cuml.accel (Zero-Code-Change)

Transparently intercepts sklearn, umap-learn, and hdbscan calls and routes them to GPU. Falls back to CPU for unsupported operations. Best for: quick acceleration of existing sklearn code, mixed codebases, prototyping.

2. Direct cuML API

Replace from sklearn with from cuml. Maximum performance, explicit control over GPU execution. Best for: production pipelines, maximum performance, new GPU-first code.

cuml.accel Accelerator Mode

The fastest path from sklearn to GPU — no code changes required. Similar to cudf.pandas for pandas.

Activation

# Jupyter/IPython (MUST be the first cell, before any sklearn import)
%load_ext cuml.accel

import sklearn  # Now GPU-accelerated
from sklearn.cluster import KMeans  # Runs on GPU transparently

# Command line
python -m cuml.accel script.py
python -m cuml.accel -v script.py     # With info logging
python -m cuml.accel -vv script.py    # With debug logging

# Programmatic (call BEFORE importing sklearn)
import cuml
cuml.accel.install()

from sklearn.cluster import KMeans  # Now GPU-accelerated

# Environment variable
CUML_ACCEL_ENABLED=1 python script.py

How It Works

• Intercepts sklearn/umap-learn/hdbscan imports and replaces estimators with GPU versions.

• If an operation isn't supported on GPU, it silently falls back to CPU sklearn.

• Uses managed memory by default — host RAM augments GPU VRAM.

• Models pickled under cuml.accel load as standard sklearn objects in non-GPU environments.

• Accelerates 30+ algorithms across sklearn, umap-learn, and hdbscan. Recent releases (26.04-26.06) expanded coverage to preprocessing estimators (StandardScaler, MinMaxScaler, MaxAbsScaler, PolynomialFeatures, LabelEncoder) and SpectralClustering.

• 26.08 requires scikit-learn >=1.6; specific accelerated preprocessors require >=1.8. Check the 26.08 compatibility page for method and parameter restrictions.

Known Fallback Triggers (Runs on CPU Instead)

• Sparse input data (most algorithms)

• Callable parameters (e.g., callable init for KMeans)

• Certain parameter values: n_components="mle" for PCA, positive=True for linear models, warm starts

• Unsupported distance metrics for neighbors algorithms

• Multi-output targets for Random Forest

• String/object dtypes — must pre-encode with LabelEncoder first

Numerical Precision

GPU estimators can use different algorithms, solvers, tree-split approximations, defaults, and random streams. Differences can exceed roundoff. Compare held-out quality, prediction agreement, class/feature order and calibration; use sign/subspace-aware checks for decompositions. A matching seed does not imply matching models.

Direct cuML API

The API follows fit/predict/transform conventions, but signatures, defaults and supported inputs differ. Consult the chosen estimator before replacing imports.

from cuml.cluster import DBSCAN
from cuml.datasets import make_blobs

# Create data directly on GPU
X, y = make_blobs(n_samples=100_000, centers=5, n_features=10, random_state=42)

# Fit — runs on GPU
model = DBSCAN(eps=1.0, min_samples=5)
model.fit(X)
print(model.labels_)

from cuml import LinearRegression
from cuml.datasets import make_regression
from cuml.model_selection import train_test_split

X, y = make_regression(n_samples=100_000, n_features=50, noise=0.1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
score = model.score(X_test, y_test)
print(f"R2 score: {score:.4f}")

Algorithm Catalog

Clustering

Regression

Classification

Dimensionality Reduction and Manifold Learning

Nearest Neighbors

Time Series

Metrics (GPU-Accelerated)

Regression: r2_score, mean_squared_error, mean_absolute_error, mean_squared_log_error, median_absolute_error

Classification: accuracy_score, log_loss, roc_auc_score, precision_recall_curve, confusion_matrix

Clustering: adjusted_rand_score, silhouette_score, silhouette_samples, homogeneity_score, completeness_score, v_measure_score, mutual_info_score

Other: trustworthiness, pairwise_distances, pairwise_kernels

Model Explainability

Input/Output Type Handling

Supported Input Types

cuML accepts: NumPy arrays, CuPy arrays, cuDF DataFrames/Series, pandas DataFrames/Series, Numba device arrays, PyTorch tensors (via __cuda_array_interface__).

NumPy and pandas inputs are automatically transferred to GPU. For best performance, pass CuPy arrays or cuDF DataFrames to avoid transfers.

Controlling Output Type

import cuml

# Global setting
cuml.set_global_output_type('cupy')  # Options: 'input', 'cupy', 'numpy', 'cudf', 'pandas'

# Context manager
with cuml.using_output_type('cudf'):
    result = model.predict(X)  # Returns cudf Series

# Per-estimator
model = cuml.KMeans(output_type='cupy')

Performance ranking (fastest to slowest output type):

1. cupy — no host transfers, most efficient

2. cudf — slight overhead for some shapes

3. numpy / pandas — device-to-host transfer cost

Best practice: Use cupy or cudf for intermediate results. Only convert to numpy/pandas at the end for visualization or export.

Preprocessing

cuML provides GPU-accelerated versions of all common sklearn preprocessors.

Scalers and Transformers

from cuml.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from cuml.preprocessing import Normalizer, PowerTransformer, QuantileTransformer
from cuml.preprocessing import Binarizer, PolynomialFeatures, KBinsDiscretizer

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Encoders

from cuml.preprocessing import LabelEncoder, OneHotEncoder, LabelBinarizer, TargetEncoder

le = LabelEncoder()
y_encoded = le.fit_transform(y)

ohe = OneHotEncoder(sparse_output=False)
X_encoded = ohe.fit_transform(X_categorical)

Imputers

from cuml.preprocessing import SimpleImputer, MissingIndicator

imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)

Pipeline and Composition

from cuml.compose import ColumnTransformer, make_column_transformer
from cuml.preprocessing import StandardScaler, OneHotEncoder

preprocessor = make_column_transformer(
    (StandardScaler(), ['age', 'income']),
    (OneHotEncoder(), ['category', 'region']),
)
X_processed = preprocessor.fit_transform(df)

Preprocessing Functions

scale(), minmax_scale(), maxabs_scale(), robust_scale(), normalize(), binarize(), add_dummy_feature(), label_binarize()

Feature Extraction

from cuml.feature_extraction.text import TfidfVectorizer, CountVectorizer, HashingVectorizer

tfidf = TfidfVectorizer(max_features=10000)
X_tfidf = tfidf.fit_transform(corpus)

Model Selection and Tuning

Train/Test Split

from cuml.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Cross-Validation

from cuml.model_selection import KFold

kf = KFold(n_splits=5, shuffle=True, random_state=42)
for train_idx, test_idx in kf.split(X):
    X_train, X_test = X[train_idx], X[test_idx]
    # ...

Hyperparameter Tuning

For an existing sklearn workflow, use cuml.accel with sklearn search first and inspect fallback. Bound concurrency per GPU to avoid memory oversubscription. Dask-ML is an optional distributed design, not a guarantee of fewer transfers.

import cuml
cuml.accel.install()  # Before sklearn imports, in a fresh process
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier

param_distributions = {
    'max_depth': [8, 12, 16, 20],
    'n_estimators': [100, 200, 500],
    'max_features': [0.5, 0.75, 1.0],
}

search = RandomizedSearchCV(
    RandomForestClassifier(),
    param_distributions,
    n_iter=25,
    cv=5,
    random_state=42,
    n_jobs=1,  # One GPU: start with bounded concurrency
)
search.fit(X_train, y_train)
print(f"Best score: {search.best_score_:.4f}")
print(f"Best params: {search.best_params_}")

Dataset Generators

from cuml.datasets import make_blobs, make_classification, make_regression

X, y = make_blobs(n_samples=100_000, centers=5, n_features=20, random_state=42)
X, y = make_classification(n_samples=100_000, n_features=50, n_informative=25)
X, y = make_regression(n_samples=100_000, n_features=50, noise=0.1)

Forest Inference Library

cuml.fil.ForestInference remains a deprecated compatibility API in 26.08. New forest inference work should evaluate nvForest and its migration guide. Do not infer its removal from a moving cuML latest page while using 26.08.

FIL provides GPU inference for supported tree-based models trained in other frameworks. Its value depends on model structure and inference batch size, so compare warm and end-to-end latency with the deployment baseline.

from cuml.fil import ForestInference

# Load an XGBoost model file; use load_from_sklearn for a fitted sklearn estimator
fil_model = ForestInference.load("xgboost_model.ubj", is_classifier=True)

# Optional: optimize for specific batch size
fil_model.optimize()

# Predict on GPU
predictions = fil_model.predict(X_test)
probas = fil_model.predict_proba(X_test)

Supports: XGBoost, LightGBM, sklearn Random Forests, any Treelite-compatible model.

This is especially valuable when you have a model already trained on CPU and want to speed up inference without retraining.

Multi-GPU with Dask

For datasets too large for a single GPU or when you want to use multiple GPUs.

from dask.distributed import Client
from dask_cuda import LocalCUDACluster

# One Dask worker per GPU
cluster = LocalCUDACluster(
    rmm_pool_size="12GB",
    enable_cudf_spill=True,
)
client = Client(cluster)

# Create distributed data
from cuml.dask.datasets import make_blobs
X, y = make_blobs(
    n_samples=1_000_000,
    n_features=20,
    centers=5,
    n_parts=len(client.scheduler_info()['workers']) * 2,  # 2 partitions per worker
)

# Use Dask estimator
from cuml.dask.cluster import KMeans
kmeans = KMeans(n_clusters=5)
kmeans.fit(X)
labels = kmeans.predict(X)

# Convert to single-GPU model for serialization
single_model = kmeans.get_combined_model()

client.close()
cluster.close()

Available Multi-GPU Estimators (cuml.dask)

• Clustering: KMeans, DBSCAN

• Linear models: LinearRegression, Ridge, Lasso, ElasticNet

• Ensemble: RandomForestClassifier, RandomForestRegressor

• Decomposition: PCA, TruncatedSVD

• Manifold: UMAP (inference only)

• Neighbors: NearestNeighbors, KNeighborsClassifier, KNeighborsRegressor

• Naive Bayes: MultinomialNB

• Preprocessing: LabelEncoder, LabelBinarizer, OneHotEncoder

Model Serialization

import pickle

# Save cuML model
with open("model.pkl", "wb") as f:
    pickle.dump(model, f, protocol=5)

# Load cuML model
with open("model.pkl", "rb") as f:
    model = pickle.load(f)

• Models trained under cuml.accel can be pickled and loaded as standard sklearn objects in non-GPU environments.

• Dask distributed models must be converted first: single_model = dask_model.get_combined_model().

• joblib also works for serialization.

Memory Management

RMM (RAPIDS Memory Manager)

import rmm

# Pre-allocate a memory pool for faster allocation
rmm.reinitialize(pool_allocator=True, initial_pool_size=2**32)  # 4 GB pool

Aligning with cuDF and CuPy

When using cuML alongside cuDF and CuPy, align all libraries on the same RMM allocator:

import rmm
from rmm.allocators.cupy import rmm_cupy_allocator
import cupy
cupy.cuda.set_allocator(rmm_cupy_allocator)

cuml.accel Memory

cuml.accel uses managed memory by default (host RAM augments GPU VRAM). Disable with --disable-uvm flag if experiencing slowdowns. Managed memory does NOT work on WSL2 or when RMM is externally configured.

Best Practices

• float32 halves array storage relative to float64; throughput gains depend on hardware and algorithm.

• Keep data on GPU throughout the pipeline — avoid NumPy/pandas round-trips.

• For datasets larger than GPU memory: use Dask multi-GPU or chunk processing.

• Pre-allocate RMM pools to avoid fragmentation.

Performance Optimization

Benchmarking

1. Verify whether cuml.accel used a GPU implementation or fell back to scikit-learn.

2. Compare the same estimator parameters, train/test split, random seed, and output semantics.

3. Warm the CUDA context and any lazy compilation before timed repetitions.

4. Report fit, transform/predict, and end-to-end times separately, including conversion and transfer costs paid by the application.

5. Record rows, features, sparsity, dtype, estimator parameters, CPU/GPU models, and software versions.

6. For stochastic or approximate algorithms, compare quality metrics as well as time.

Key Optimization Tips

1. Use float32 when the model's accuracy and stability permit it. Validate metrics after changing precision; architecture-specific throughput ratios are not a correctness argument.

2. Keep data on GPU. Pass CuPy arrays or cuDF DataFrames. Every NumPy/pandas conversion triggers a device-host transfer.

3. Use enough work to amortize setup and transfer. Do not rely on a fixed row threshold; features, sparsity, estimator, batch size, and reuse all matter.

4. Benchmark the actual feature shape. Width affects compute, memory traffic, and temporary storage differently for each estimator.

5. First call has JIT overhead. Benchmark on subsequent calls, not the first.

6. Use RMM pools when allocation overhead or fragmentation is visible in profiling. Pools amortize allocator costs but reserve device memory and should be sized deliberately.

7. Bound search concurrency per GPU and measure transfer/fallback behavior. The search scheduler alone does not ensure device residency.

8. Evaluate FIL for supported tree-model inference. Benchmark the target model and production batch sizes against the existing serving path.

Interoperability

• cuDF: Accepted by many estimators; assembling dense matrices or changing dtype can copy.

• CuPy: Zero-copy via __cuda_array_interface__. Most efficient intermediate format.

• NumPy/pandas: Accepted as input (auto-transferred to GPU). Output type configurable.

• PyTorch: Tensors accepted via array interface.

• sklearn: Similar estimator interfaces. Interconversion is estimator-specific; use cuml.accel for supported transparent acceleration.

• XGBoost/LightGBM: FIL provides GPU inference for externally-trained tree models.

• Dask: Native distributed support via cuml.dask module.

End-to-End RAPIDS Pipeline

import cudf
import cuml
from cuml.preprocessing import StandardScaler
from cuml.ensemble import RandomForestClassifier
from cuml.model_selection import train_test_split

# Load data on GPU
df = cudf.read_parquet("data.parquet")
X = df.drop("target", axis=1)
y = df["target"]

# Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Preprocess
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train
model = RandomForestClassifier(n_estimators=100, max_depth=16)
model.fit(X_train, y_train)

# Evaluate
score = model.score(X_test, y_test)
print(f"Accuracy: {score:.4f}")

The main tabular and estimator stages use GPU data. I/O, setup, scalar scoring/printing, internal conversions, and unsupported operations can still involve the CPU or transfers.

Key Differences from sklearn

1. Platform: Linux and WSL2 only. No native macOS or Windows.

2. Sparse data: Coverage is estimator-specific; do not densify a large sparse matrix blindly or assume all sparse inputs fall back. Check the pinned compatibility page.

3. String data: Must be pre-encoded to numeric. No native string column support in estimators.

4. Multi-output: Not supported for Random Forest.

5. Warm starts: Not supported for most algorithms.

6. Unsupported parameters: May trigger fallback or raise; never silently drop a constraint such as positive=True merely to obtain GPU execution.

7. Model equivalence: Different algorithms can change fitted models substantially. Validate scientific conclusions and held-out quality; do not promise equivalent quality.

8. Memory: Limited by GPU VRAM (typically 8-80 GB). Use managed memory or Dask for larger datasets.

9. Missing fitted attributes: Some proxy attributes remain unavailable; inspect the estimator-specific compatibility list before using diagnostics.

Common Migration Patterns

Pattern 1: Zero-Effort (cuml.accel)

# Add one line at top of notebook:
%load_ext cuml.accel

from sklearn.cluster import KMeans  # Now GPU-accelerated
from sklearn.decomposition import PCA  # Now GPU-accelerated
# Everything else stays exactly the same

Pattern 2: Direct Import Swap

# Before
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# After
from cuml.ensemble import RandomForestClassifier
from cuml.preprocessing import StandardScaler
from cuml.model_selection import train_test_split

Pattern 3: Full RAPIDS Pipeline (cuDF + cuML)

import cudf
from cuml.preprocessing import StandardScaler, LabelEncoder
from cuml.ensemble import RandomForestClassifier
from cuml.model_selection import train_test_split

# Load and preprocess entirely on GPU
df = cudf.read_parquet("data.parquet")
le = LabelEncoder()
df["category_encoded"] = le.fit_transform(df["category"])

X = df[["feature1", "feature2", "category_encoded"]].to_cupy()
y = df["target"].to_cupy()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = RandomForestClassifier(n_estimators=200, max_depth=16)
model.fit(X_train, y_train)
print(f"Accuracy: {model.score(X_test, y_test):.4f}")

Pattern 4: GPU Inference for CPU-Trained Models

from cuml.fil import ForestInference

# Load a supported XGBoost/LightGBM/sklearn model for GPU inference
fil_model = ForestInference.load("my_xgboost_model.ubj", is_classifier=True)
predictions = fil_model.predict(X_test)