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Data science workflows have been slow and cumbersome. Yet they depend on CPUs to load, filter, and manipulate data, as well as train and deploy models. GPUs significantly reduce infrastructure costs and deliver superior performance for end-to-end data science workflows using NVIDIA RAPIDS™ libraries. GPU-accelerated data science is available everywhere - on the desktop, in the data center, in the peripherals, and in the cloud.
Accelerate your entire Python toolchain with straightforward open source software integration and minimal code customization.
Accelerate machine learning up to 100x with more iterations for higher model accuracy.
Reduce data science computing infrastructure costs and increase data center efficiency.
APACHE SPARK 3.0 ACCELERATES WITH RAPIDS GRAPHICS PROCESSORS
Version 3.0 is the first release of Spark to offer fully integrated and seamless GPU acceleration for analytics and AI workloads. Take advantage of Spark 3.0 with GPUs either on-premises or in the cloud - without having to change code each time. The breakthrough performance of GPUs enables organizations and researchers to train larger models more frequently - unlocking the value of Big Data with the power of AI.
NVIDIA's GPU-accelerated XGBoost enables the world's leading machine learning algorithm to deliver performance gains in games. With significantly faster training performance over CPUs, data science teams can tackle larger data sets, iterate faster, and optimize models for maximum predictive accuracy and business value.
CPU: Core i9 | End-to-end time = Data Prep + Conversion + Training + Validation
RAPIDS is built on more than 15 years of NVIDIA® CUDA® development and machine learning expertise. It is powerful new software for fully executing end-to-end data science learning pipelines in the GPU, shrinking learning time from days to minutes.
RAPIDS libraries are open source, written in Python and built on Apache Arrow. The software is developed together with open source communities worldwide. Download RAPIDS, the acceleration of your machine learning as well as your data science workflows.