cuML : RAPIDS Machine Learning Library
cuML is a GPU accelerated machine learning library developed by NVIDIA as part of the RAPIDS AI ecosystem. It is designed to enable fast and scalable execution of traditional machine learning algorithms on NVIDIA GPUs, allowing data scientists to significantly speed up workflows while maintaining compatibility with popular Python machine learning tools like scikit-learn.
Components of cuML
1. GPU Acceleration: cuML harnesses the parallel computing power of NVIDIA GPUs via CUDA to accelerate classical machine learning algorithms. This often results in speedups of up to 50x compared to CPU only implementations.
2. Scikit-learn Compatible API: cuML provides an API that closely mirrors scikit-learn’s interface. This design choice means users can switch to GPU acceleration with minimal or zero modification to their existing code.
3. Wide Range of Algorithms: cuML supports many foundational ML algorithms, including:
- Supervised Learning: Linear and logistic regression, random forests, support vector machines (SVM), etc.
- Unsupervised Learning: K-means clustering, DBSCAN, Principal Component Analysis (PCA), UMAP, t-SNE for dimensionality reduction.
- Nearest Neighbors: Fast similarity search algorithms optimized for GPUs.
4. Data Interoperability: It works seamlessly with GPU DataFrames provided by cuDF (a GPU implementation of pandas), CuPy arrays, NumPy arrays and Numba device arrays, enabling end-to-end GPU data processing with minimal CPU-GPU data transfer overhead.
5. Zero-Code-Change GPU Acceleration: With the cuml.accel module, cuML can transparently accelerate supported scikit-learn workflows on GPUs without any code modifications. The compatibility layer intercepts calls to scikit-learn algorithms and redirects those supported on GPU to their cuML implementations, falling back to CPU versions when needed.
How cuML Works Internally
cuML is built on top of NVIDIA’s CUDA platform, employing highly optimized implementations of machine learning algorithms tailored for GPU parallelism. Key characteristics include:
- Utilization of massively parallel GPU cores for matrix math, distance computations and other algorithm-specific operations.
- Efficient memory management using CUDA Unified Memory, which allows the GPU to seamlessly access host memory when datasets exceed GPU memory limits.
- Algorithm implementations are designed to maximize throughput by batching inputs and minimizing costly memory allocations or CPU-GPU transfers.
- cuML carefully aligns its outputs and behavior to match the expected scikit-learn semantics, ensuring compatibility with the broader Python ML ecosystem.
Benefits of Using cuML
- Dramatically Increased Speed: Routine ML tasks that often take minutes or hours on CPU complete in seconds or less on GPUs.
- Ease of Adoption: Designed for data scientists comfortable with Python and scikit-learn, it lowers the barrier to leveraging GPU power.
- Large Dataset Handling: With GPU acceleration and integration with distributed computing frameworks, cuML scales to very large datasets.
- Seamless Integration: Works well alongside other RAPIDS libraries (cuDF, cuGraph, cuSignal) and Python data science tools.
- Continuous Improvement: NVIDIA actively expands algorithm coverage, adds new features and optimizes performance.
Setting Up Your Environment
To set up and install cuML (the RAPIDS machine learning library) properly, here is the latest and recommended step-by-step installation guide tailored for your environment based on RAPIDS official documentation and best practices:
Prerequisites
- NVIDIA GPU with CUDA Compute Capability 7.0 or higher (Volta architecture or newer).
- CUDA Toolkit version compatible with your GPU (CUDA 11.2 or newer, preferably CUDA 12).
- Supported operating system (Linux distributions like Ubuntu 20.04+ or Windows 11 with WSL2).
- NVIDIA driver matching your CUDA version (check with nvidia-smi).
Step 1: Install Conda (if you don't have it already)
If you don’t have conda, install Miniforge (lightweight conda):
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
Step 2: Create and activate a new conda environment with cuML
Use the RAPIDS release selector or below command adjusted for your CUDA version to install cuML and dependencies.
Example command for RAPIDS 25.06 with CUDA 12.9 (adjust versions as per your setup):
conda create -n rapids-25.06 -c rapidsai -c nvidia -c conda-forge \
rapids=25.06 python=3.13 cudatoolkit=12.0 -y
conda activate rapids-25.06
- This installs cuML along with other RAPIDS components compatible with CUDA 12.
- Replace the versions if needed to fit your CUDA Toolkit version or Python version (e.g., use Python 3.8 if necessary)
Example Usage
An exemplary code snippet showing a basic cuML workflow with random forest:
- A cuDF DataFrame is created on the GPU to hold the features and labels, replacing the typical CPU-based pandas DataFrame.
- The RandomForestClassifier from cuML is initialized similarly to the scikit-learn counterpart.
- Model training occurs on the GPU by invoking the fit method on the cuDF DataFrame objects.
- Prediction is also executed on the GPU via the predict method.
import cudf
from cuml.ensemble import RandomForestClassifier
import numpy as np
# Creating a cuDF DataFrame on GPU
gdf = cudf.DataFrame({
'feature1': np.random.rand(100000),
'feature2': np.random.rand(100000),
'label': np.random.randint(0, 2, 100000)
})
X = gdf[['feature1', 'feature2']]
y = gdf['label']
# Initialize and train a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X, y)
# Predict on the same dataset
predictions = clf.predict(X)
Output:
Google Colab : cuML
Zero-Code-Change GPU Acceleration
For scikit-learn users, cuML also offers a zero-code-change acceleration layer (cuml.accel) which lets existing scripts leverage GPU computation with a simple import or execution command:
- In Jupyter Notebook:
%load_ext cuml.accel
- Or when running a script:
python -m cuml.accel your_script.py
This proxy intercepts scikit-learn calls and transparently redirects them to GPU-accelerated cuML implementations when supported, falling back to CPU when not.
Practical Considerations
- For best performance, minimize CPU-GPU data transfer by keeping data and preprocessing steps on the GPU.
- cuML API is continually improving, with new algorithms and features being added regularly.
- Some numerical results may differ slightly compared to CPU scikit-learn because of differences in floating-point computations and parallel execution order.
- Integrates well with distributed machine learning setups for very large-scale data processing, such as Dask-cuML.
When to Use cuML
- When working with large datasets that make CPU-based ML slow and inefficient.
- For performance-critical applications where fast training and inference improve productivity or deployment speed.
- When NVIDIA GPUs are available to leverage massive parallelism.
- When maintaining compatibility with scikit-learn code and minimizing code changes is important.
- For workflows requiring scalable multi-GPU or distributed training.
- In pipelines that benefit from fully GPU-resident data processing, avoiding expensive memory transfers.
Limitations of cuML
- Not all scikit-learn algorithms are supported or fully accelerated; some advanced or niche algorithms (e.g. Support Vector Regression, mean shift clustering) remain unsupported or experimental.
- Multi-node and multi-GPU support for certain algorithms (like Random Forest) is considered experimental and may present stability or performance issues.
- Requires compatible NVIDIA GPUs with sufficient memory bandwidth; large datasets demand GPUs with ample memory resources.
- May fallback to CPU implementations for unsupported features or operations, reducing speed gains.
- Some pipelines require careful tuning and design to minimize CPU-GPU data movement and ensure tight integration with RAPIDS ecosystem components like cuDF and Dask.
- The ecosystem is rapidly evolving, meaning some advanced use cases might face limitations or require additional adaptation.
Algorithms Supported by cuML
Dimensionality Reduction:
Classification:
Regression:
Other : K-Nearest Neighbors (KNN) (for classification and regression)
