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?
In a real development environment our customers leverage ArcGIS to read and write geospatial data to a plethora of distributed data stores, such as … Continued
Spark Summit SF 2017 - We briefly introduce Alluxio and present different ways Alluxio can help Spark jobs, along with best practices. We also … Continued
Strata Data Conference London 2017 - Learn about stream processing on Alluxio from real-world workloads at Qunar, as well as how to position Alluxio … Continued
For business to not just survive — but to flourish — it’s become imperative to make decisions with near immediacy, continuously pivot strategy and … Continued
Global Big Data Conference 2017 - In the past year, the Alluxio project experienced significant improvement in performance and scalability and was extended with … Continued
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