Using Alluxio to Optimize and Improve Performance of Kubernetes-Based Deep Learning in the Cloud

This article presents the collaborative work of Alibaba, Alluxio, and Nanjing University in tackling the problem of Artificial Intelligence and Deep Learning model training in the cloud. We adopted a hybrid solution with a data orchestration layer that connects private data centers to cloud platforms in a containerized environment. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture.

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Accelerate and Scale Big Data Analytics with Alluxio and Intel® Optane™ Persistent Memory

International Data Corporation (IDC) reported that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 20251. This trend becomes more and more complicated with the variety and velocity of data growth, and it continuously changes the ways data is collected, stored, processed, and analyzed. New analytics solutions, including machine learning, deep learning, and artificial intelligence (AI), and new architectures and tools are being developed to extract and deliver value from the huge datasphere. 

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Alluxio Accelerates Deep Learning in Hybrid Cloud using Intel’s Analytics Zoo open source platform powered by oneAPI

This article describes how Alluxio accelerates the training of deep learning models in a hybrid cloud environment with 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.

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Get Insights Faster with Alluxio and Intel

Learn more about Alluxio and Intel’s joint solution, which allows companies to unify on-premises and cloud data silos into a single, cloud-based data layer, increasing data accessibility and elasticity while virtually eliminating the need for copies—for less complexity, lower costs, and greater speed and agility.

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“Zero-Copy” Hybrid Cloud for Data Analytics – Strategy, Architecture and Benchmark Report

This whitepaper details how to leverage a public cloud, such as Amazon AWS, Google GCP, or Microsoft Azure to scale analytic workloads directly on data on-premises without copying and synchronizing the data into the cloud. We will show an example of what it might look like to run on-demand Presto and Hive with Alluxio in the public cloud using on-prem HDFS. We will also show how to set up and execute performance benchmarks in two geographically dispersed Amazon EMR clusters along with a summary of our findings.

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Why Data Orchestration?

Today’s current pace of innovation is hindered by the necessity of reinventing the wheel in order for applications to efficiently access data. When an engineer or scientist wants to write an application to solve a problem, he or she needs to spend significant effort on getting the application to access the data efficiently and effectively, rather than focusing on the algorithms and the application’s logic.

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AWS + Alluxio: Data Orchestration for Analytics & AI in the cloud

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 demands. In addition, as data is increasingly siloed and lives everywhere, there’s a need for data orchestration to bring the needed data closer to compute. With Alluxio’s data orchestration platform, bring back data locality for your compute with in-memory & tiered data access.