A collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problems of Deep Learning model training in the cloud. Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost.
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This article describes how Alluxio can accelerate the training of deep learning models in a hybrid cloud environment when using Intel’s Analytics Zoo open source platform, powered by oneAPI. Details on the new architecture and workflow, as well as Alluxio’s performance benefits and benchmarks results will be discussed.
Are you using SQL engines, such as Presto, to query existing Hive data warehouse and experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications?
Many organizations have taken advantage of the scalability and cost-savings of cloud computing as well as cloud storage services to meet their data-powered workload … Continued
The ever increasing challenge to process and extract value from exploding data with AI and analytics workloads makes a memory centric architecture with disaggregated … Continued
Cloud has changed the dynamics of data engineering in many ways, from changing expectations of on-demand platform services to the popularity of the object … Continued
The AWS EMR service has made it easy for enterprises to bring up a full-featured analytical stack in the cloud that elastically scales based … Continued
Haoyuan Li's keynote at O'Reilly Beijing discusses open source data orchestration and the value of leveraging Alluxio with rising trends driving the need for … Continued
The data orchestration layer bridging the gap between data locality with improved performance and data accessibility for analytics workloads in Kubernetes, and enables portability … Continued