🎉 RAPIDS v24.12 Released! 🚀 The latest update brings incredible improvements to GPU-accelerated data science and analytics workflows: ✅ cuDF now on PyPI: Installation just got easier—integrate GPU dataframes seamlessly into your Python workflows. ✅ Polars GPU Engine Enhancements: Supports larger-than-GPU memory queries using Unified Memory, enabling effortless handling of massive datasets. ✅ Faster GNN Training: Boosted performance with hierarchy-based gather operations—perfect for graph-based learning tasks. ✅ Optimized Groupby and AWS S3 Reads: Streamlined data aggregation and cloud-native workflows for faster insights. Benefits for Civil Engineers 💡 Big Data in Infrastructure Planning: • Analyze traffic patterns, utility networks, and urban growth trends faster with RAPIDS’ GPU-accelerated dataframes. • Unified Memory support means handling city-scale datasets or GIS data just got easier. 💡 Graph Neural Networks (GNNs): • Faster GNN training allows civil engineers to model complex networks like roadways, drainage systems, or power grids for predictive analysis and optimization. 💡 Seamless Integration with Cloud Workflows: • Optimized AWS S3 reads make it easier to pull large datasets like LiDAR scans, land use records, or satellite imagery into analysis pipelines. RAPIDS is transforming how industries like civil engineering can harness GPU power for real-time decision-making and large-scale simulations. 👏 Big thanks to the RAPIDS team for pushing innovation forward!
🎉 RAPIDS v24.12 released 🙌 ✅ cuDF now on PyPI for simplified installation ✅ Polars GPU engine now handles larger-than-GPU memory queries with Unified Memory ✅ Faster GNN training with hierarchy-based gather operations ✅ Optimized groupby and AWS S3 reads Get the latest updates ➡️ https://nvda.ws/3ZQaCW6