IBM integrates NVIDIA's RAPIDS for machine learning

Nvidia is keen to exploit the potential of GPUs for compute-intensive workloads

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, as well as Wes McKinney, head of Ursa Labs and creator of Apache Arrow and pandas, the fastest-growing Python data science library. These encompass such mission-critical applications as credit card fraud detection, retail inventory forecasts, and customer purchasing prediction, each one of these represents billions of dollars to the economy.

"The compute world today requires powerful processing to handle complex workloads like data science and analytics - it's a job for NVIDIA GPUs". NVIDIA announced RAPIDS from their GTC Europe 2018 conference happening this week in Munich.

"IBM and NVIDIA's close collaboration over the years has helped leading enterprises and organisations around the world tackle some of the world's largest problems", says NVIDIA accelerated computing vice president and general manager Ian Buck. "But we're hitting a brick wall in our ability to use data".

The main component of the open-source RAPIDS platform is a suite of CUDA-accelerated libraries for GPU-based analytics, machine learning and data visualization.

For the first time, it gives scientists the tools they need to run the entire data science pipeline on GPUs. Initial RAPIDS benchmarking, using the XGBoost machine learning algorithm for training on an NVIDIA DGX-2 system, shows 50 times speedups compared with CPU-only systems.

Nvidia's claims are given credence by a slew of big tech companies that have thrown their weight behind the RAPIDS platform.

IBM, HPE, Dell EMC, Oracle, Cisco, and Lenovo also stand to benefit, although perhaps not to the same extent as NVIDIA, inasmuch as they can also sell CPU-only systems for their big data customers.

To facilitate broad adoption, NVIDIA is integrating RAPIDS into Apache Spark, the open-source framework for analytics and data science.

PowerAI is IBM's software layer that optimizes how today's data science and AI workloads run on heterogeneous computing systems, and our goal is for this improved performance trajectory for GPU accelerated workloads on POWER9 to continue with RAPIDS.

RAPIDS also focuses on common data preparation tasks for analytics and data science. Python all-star Anaconda and technology giants like IBM, Hewlett Packard Enterprise and Oracle are in on the action too.

Analyst Patrick Moorhead of Moor Insights & Strategy said RAPIDS was all about Nvidia trying to make its GPUs more accessible to enterprises interested in running AI workloads.

"Nvidia has had lots of success getting hyperscalers like AWS and Azure to integrate deep learning and machine learning into their workflows", Moorhead said.

MapR Technologies, provider of the industrys leading data platform for AI and Analytics, enables enterprises to inject analytics into their business processes to increase revenue, reduce costs, and mitigate risks.

"For Nvidia, coming from the hardware side, it is clear that it has to win the hearts and minds of developers and data scientists and create a widely adopted software platform", Mueller said. RAPIDS is aimed at the data science crowd, that is, researchers, engineers, and other developers looking to make the most out of their datasets - both literally and figuratively.

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