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Bright Computing Announces New Product to Help Get Enterprise Data Scientists Up and Running Quickly with Deep Learning

Bright Cluster Manager for Data Science brings together everything needed to build and manage a complete, scalable deep learning environment for data scientists

DENVER, Nov. 14, 2017 /PRNewswire-iReach/ -- Bright Computing, a global leader in cluster and cloud infrastructure automation software, today announced Bright Cluster Manager for Data Science, an all-in-one product for data scientists. The new product brings together all of the tools needed to build, manage, and operate an enterprise-class data science cluster.

"Organizations are spending precious and costly time gathering, configuring, testing and troubleshooting machine learning environments for their data scientists, at the expense of time that could be used to deliver insights to the business," said Bill Wagner, CEO of Bright Computing. "This new product eliminates the drudgery of getting a machine learning environment up and running, along with Hadoop or Apache Spark, in a cluster-ready environment that can automatically scale up as your demand for machine learning capacity grows".

Last year, Bright began offering tested and verified deep learning libraries and frameworks to its customers. Since then, demand for machine learning has grown, and Bright has responded by greatly expanding its machine learning portfolio, adding more libraries, more frameworks, and deepening the integration with its management software suite. Now, with the addition of big data, Bright offers a complete set of tools allowing data scientists to accelerate their work.

Bright Cluster Manager for Data Science integrates with all of the leading deep learning frameworks including Tensorflow, NVIDIA CUDA Deep Neural Network library (cuDNN), Deep Learning GPU Training System (DIGITS), MXNet, Caffe, Caffe2, pyTorch, and CNTK as well as key big data platforms: Spark, Hadoop, and Cassandra.

The new product also includes jupyter, Zeppelin, and DIGITS notebook front ends data scientists can use to perform interactive queries without having to use the command line.

It also provides a best-in-class management solution for the hardware that lies at the heart of deep learning installations: NVIDIA and AMD GPUs, and Intel accelerators. As a result, customers can choose their numeric computation accelerators of choice.

"Our mission has always been to make managing infrastructure easy so that our customers can focus on their work," Wagner said. "This new offering enables us to extend that mission to data scientists working with compute-intensive, data-intensive, deep learning projects."

Bright Cluster Manager for Data Science, including the ability to build custom versions of included packages using EasyBuild, will be demonstrated at Supercomputing 17, November 13-16 in Denver Colorado, at booth #937.

About Bright Computing

Bright Computing is the leading provider of hardware-agnostic cluster and cloud management software in the world. Bright Cluster Manager(TM), Bright Cluster Manager for Big Data(TM), and Bright OpenStack(TM) provide a unified approach to installing, provisioning, configuring, managing, and monitoring HPC clusters, big data clusters, deep learning environments, and OpenStack clouds. Bright's products are currently deployed in more than 650 data centers around the world. Bright Computing's customer base includes global academic, governmental, financial, healthcare, manufacturing, oil/gas/energy, and pharmaceutical organizations such as Boeing, Intel, NASA, Stanford University, and St. Jude Children's Research Hospital. Bright partners with Amazon, Cray, Dell, HPE, Intel, Nvidia, and other leading vendors to deliver powerful, integrated solutions for managing advanced IT infrastructure such as high-performance computing clusters, big data clusters, and OpenStack-based private clouds.

Media Contact: Lionel Gibbons, Bright Computing, 4083376076, [email protected]

News distributed by PR Newswire iReach: https://ireach.prnewswire.com

SOURCE Bright Computing

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