Data Processing Libraries

GPU-accelerated libraries to accelerate data processing workflows for tabular, text, and image data.

cuDF

Accelerate tabular data, including pandas, Polars, and Apache Spark with zero code changes.

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cuVS

Accelerate vector search for data mining and semantic search applications—including world-class performance from the GPU-native nearest neighbors algorithm CAGRA.

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cuML

Speed up ML algorithms in scikit-learn, UMAP, HDBSCAN, and Apache Spark with zero code changes.

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cuOpt

Open source, GPU-accelerated decision optimization engine designed to tackle large-scale problems with millions of variables and constraints, enabling accelerated decision-making.

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cuGraph

Scale up and speed up graph analytics with GPU-accelerated NetworkX.

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NeMo Curator

Improves generative AI model accuracy by processing text, image, and video data at scale for training and customization, with pre-built pipelines for generating synthetic data.

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Morpheus

Open application framework that optimizes cybersecurity AI pipelines for analyzing large volumes of real-time data.

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nvComp

High-throughput GPU-accelerated compression and decompression library that minimizes storage footprint and speeds up data transfer rates for AI training, HPC, data science, and analytics applications.

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GPU Direct Storage

NVIDIA GPUDirect Storage creates a direct data path between local or remote storage, such as NVMe or NVMe over Fabrics (NVMe-oF), and GPU memory.

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Dask

Expand data science pipelines to multiple nodes with NVIDIA RAPIDS on Dask.