Accelerating Machine Learning / Deep Learning in the Cloud: Architecture and Benchmark

This whitepaper introduces how to speed up end-to-end  distributed training in the cloud using Alluxio to accelerate data access. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process. This whitepaper also demonstrates how to set up and benchmark the end-to-end performance of the training process, along with a comparison of other popular approaches.

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Alluxio Use Cases Overview

Alluxio started as a virtual distributed file system, a research project out of the AMPLab at U.C. Berkeley. Alluxio foresaw the need for agility when accessing large data stores separated from compute engines like Hadoop or Spark.
Fast forward several years and over a thousand committers later, and Alluxio has blossomed into the industry’s leading data orchestration platform for analytics and AI/ML. But as with any new type of technology, figuring out the best ways to use it depends on your data environment, computational workloads, issues, and goals. 

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Accelerate Cloud Training with Alluxio

Alluxio’s capabilities as a Data Orchestration framework have encouraged users to onboard more of their data-driven applications to an Alluxio powered data access layer. Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface.

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Aunalytics Leverages Alluxio as a “one-stop-shop” for Data I/O

Alluxio is a leading data orchestration platform that offers a compute agnostic, storage agnostic, and cloud agnostic solution for big data and machine learning applications. Aunalytics is a data platform company delivering Insights-as-a-Service to answer enterprise and mid-sized companies’ most important IT and business questions.

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