RAPIDS Runtimes ship a suite of libraries from NVIDIA that bring the power of accelerated GPUs to standard Data Science operations — be it exploratory data analysis, feature engineering, or model training. The RAPIDS libraries are designed as drop-in replacements for common Python data science libraries like cuDF (pandas), cuPy (numpy), cuML (sklearn) and Dask-CUDA (dask) — enabling GPU acceleration for data science workloads of 5X+ without significant code changes. By leveraging the parallel compute capacity of GPUs the time for complicated data engineering and data science tasks can be dramatically reduced, accelerating the timeframes for Data Scientists to take ideas from concept to production. For more information about RAPIDS see rapids.ai.
Data Scientists now can use the RAPIDS Runtimes that enable end-to-end data science and analytics pipelines entirely on GPUs.
To learn more, visit the documentation about the RAPIDS ML Runtimes in CML.
