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?
Intel and Alluxio collaborate to measure a 20-25% price/performance improvement over the prior generation for machine learning models with PyTorch on AWS. This collaboration … Continued
Many companies have leveraged Alluxio to level up their current Presto platform, including Facebook, TikTok, Electronic Arts, Walmart, Tencent, Comcast, and more. They have … Continued
Unisound is an artificial intelligence company focusing on Internet of Things services. Unisound’s AI technology stacks include the perception and expression capabilities of signals, … Continued
This talk describes the design of shadow cache, a lightweight component to track the working set size of Alluxio cache. Shadow cache can keep … Continued
This talk shares the designs and use cases of the Alluxio and Spark integrated solutions, as well as the best practice and “what not … Continued