GPU Accelerated Scikit-learn for Machine Learning and Data Science with NVIDIA's cuML
Автор: Dr. Data Science
Загружено: 2025-03-19
Просмотров: 740
#machinelearning #scikitlearn #gpu
cuML is an open-source, GPU-accelerated machine learning library developed by NVIDIA as part of the RAPIDS AI suite.
Key Features
*🚀 GPU Acceleration*
Leverages NVIDIA GPUs for significant speedups, achieving 10-50x faster performance compared to CPU-based implementations.
*🔄 scikit-learn Compatibility*
Provides an API similar to scikit-learn, allowing easy transition to GPU-accelerated computing.
*📊 Extensive Algorithm Support*
A wide range of machine learning algorithms, including:
**Clustering**: DBSCAN, HDBSCAN, K-Means Clustering
**Dimensionality Reduction**: PCA, Incremental PCA, Truncated SVD, UMAP, Random Projection, t-SNE
**Regression & Classification**: Linear Regression, Lasso, Ridge Regression, ElasticNet, Logistic Regression, Naive Bayes, Random Forest
**Optimization Techniques**: Stochastic Gradient Descent (SGD), Coordinate Descent (CD)
*🛠️ Zero Code Change Acceleration*
Allows existing scikit-learn codebases to benefit from GPU acceleration by simply loading the `cuml.accel` module.
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