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?
Ceph Days 2017 - Adit Madan presents on enabling fast big data analytics on Ceph with Alluxio. … Continued
By leveraging Alluxio, Mesos, Minio, and Spark we have created an end-to-end data processing solution that is performant, scalable, and cost optimal. We use … Continued
Alluxio, formerly Tachyon, is the world's first system which unifies data at memory speeds while achieving affordability through Alluxio's innovative tiered storage functionality. This … Continued
Scala by the Bay 2016 - Throughout our four-year history, Scala and Scale By the Bay is leading the way on evangelizing and understanding … Continued
Strata+Hadoop 2016 - In the past year, the Alluxio project experienced a tremendous improvement in performance and scalability and was extended with key new … Continued