Rapids
NVIDIA RAPIDS 25.08 Enhances Data Science with New Profiling Tools and Algorithm Support
NVIDIA's RAPIDS 25.08 release introduces new profiling tools for cuML, updates to the Polars GPU engine, and additional algorithm support, enhancing data science accessibility and scalability.
RAPIDS Introduces GPU Polars Streaming and Unified GNN API Enhancements
NVIDIA's RAPIDS suite version 25.06 unveils new features including GPU Polars streaming, a unified GNN API, and zero-code ML speedups, enhancing Python data science capabilities.
NVIDIA RAPIDS Enhances Machine Learning with Zero-Code Acceleration and Performance Gains
NVIDIA's RAPIDS introduces zero-code acceleration for machine learning, boosts IO performance, and supports out-of-core XGBoost training, streamlining data science workflows.
Enhancing Data Science Efficiency with GPU Acceleration
Discover how GPU acceleration, through tools like RAPIDS, significantly improves data science workflows by enhancing performance and efficiency with minimal code changes.
NVIDIA Hackathon Showcases Strategies for RAPIDS-Accelerated Machine Learning
The NVIDIA Hackathon at ODSC West highlighted innovative strategies using RAPIDS for accelerated machine learning, with participants developing efficient models under time constraints.
Enhancing GPU Analytics with RAPIDS and Ray Integration
Explore how RAPIDS and Ray can accelerate GPU analytics, focusing on Ray's Actor model and integration with RAPIDS libraries for optimized data processing.
RAPIDS 24.12 Enhances Data Processing with PyPI Packages and Unified Memory
NVIDIA's RAPIDS 24.12 release introduces cuDF packages on PyPI, CUDA Unified Memory for Polars, and improved GNN training, significantly boosting data processing capabilities.
NVIDIA's RAPIDS cuDF Enhances pandas Through Unified Virtual Memory
NVIDIA's RAPIDS cuDF utilizes Unified Virtual Memory to boost pandas' performance by 50x, offering seamless integration with existing workflows and GPU acceleration.
Optimizing Multi-GPU Data Analysis with RAPIDS and Dask
Explore best practices for leveraging RAPIDS and Dask in multi-GPU data analysis, addressing memory management, computing efficiency, and accelerated networking.
NVIDIA RAPIDS 24.10 Enhances NetworkX and Polars with GPU Acceleration
NVIDIA RAPIDS 24.10 introduces GPU-accelerated NetworkX and Polars with zero code changes, enhancing compatibility with Python 3.12 and NumPy 2.x for improved data processing.