NVIDIA is releasing a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that companies can use to analyse massive amounts of data and make accurate business predictions at unprecedented speed.
RAPIDS open-source software gives data scientists a giant performance boost as they address highly complex business challenges, such as predicting credit card fraud, forecasting retail inventory and understanding customer buying behaviour.
Reflecting the growing consensus about the GPU’s importance in data analytics, an array of companies are supporting RAPIDS - from pioneers in the open-source community, such as Databricks and Anaconda, to tech leaders such as Hewlett Packard Enterprise, IBM and Oracle.
Analysts estimate the server market for data science and machine learning at US$20 billion annually, which, together with scientific analysis and deep learning, pushes up the value of the high-performance computing market to approximately US$36 billion.
“Data analytics and machine learning are the largest segments of the high-performance computing market that have not been accelerated - until now,” says NVIDIA founder and CEO Jensen Huang.
“The world’s largest industries run algorithms written by machine learning on a sea of servers to sense complex patterns in their market and environment, and make fast, accurate predictions that directly impact their bottom line.
“Building on CUDA and its global ecosystem, and working closely with the open-source community, we have created the RAPIDS GPU-acceleration platform. It integrates seamlessly into the world’s most popular data science libraries and workflows to speed up machine learning. We are turbocharging machine learning as we have done with deep learning”
RAPIDS offers a suite of open-source libraries for GPU-accelerated analytics, machine learning and, soon, data visualisation. It has been developed over the past two years by NVIDIA engineers in close collaboration with key open-source contributors.
Initial RAPIDS benchmarking, using the XGBoost machine learning algorithm for training on an NVIDIA DGX-2TM system, shows 50x speedups compared with CPU-only systems.
This allows data scientists to reduce typical training times from days to hours, or from hours to minutes, depending on the size of their dataset.
RAPIDS builds on popular open-source projects - including Apache Arrow, pandas and scikit-learn - by adding GPU acceleration to the most popular Python data science toolchain.
To bring additional machine learning libraries and capabilities to RAPIDS, NVIDIA is collaborating with such open-source ecosystem contributors as Anaconda, BlazingDB, Databricks, Quansight, and scikit-learn.
Additionally, some of the world’s leading technology companies are supporting RAPIDS through new systems, data science platforms and software solutions, including IBM, Cisco, Dell EMC, Lenovo, NERSC, NetApp, Pure Storage, SAP and SAS.