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The promise of machine learning is pulling every organization in every industry into the realm of high-performance computing. Many organizations have little or no experience with these systems, and even the most experienced high-performance computing practitioners will tell you that building and managing high-performance Linux clusters is no easy task. With hundreds or thousands of hardware and software elements that must work in unison, spanning compute, networking and storage, the skills, knowledge and experience required to do this is often more than an organization can cope with. Bright Cluster Manager for Data Science offers an integrated solution for building and managing machine learning clusters that reduces complexity, accelerates time to value and provides enormous flexibility. Bright Cluster Manager for Data Science also provides a pretested catalog of popular machine learning frameworks and libraries, as well as integration with Jupyter Notebook, to ensure that data scientists can be as productive as possible and not waste time managing their work environment.
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Bright Edge allows organizations to deploy and centrally manage computing resources in distributed locations as a single clustered infrastructure, from a single interface. The distributed computing nodes deployed and managed by Bright Edge can be imaged to support any workload, re-imaged on the fly to support different workloads when desired, and are monitored to ensure that you always know precisely what’s going on. And when you need to add more computing capacity at any location, bring additional nodes online is quick and easy.
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轻松构建和管理面向 HPC 的集群 使用 Bright Cluster Manager 可以在几分钟内从裸机快速构建完整的集群，并有效地管理它们。Bright 在一个工具中结合了配置、监控和变更管理功能，跨越集群的整个生命周期，从而使管理员可以为最终用户和您的企业提供更好的支持。