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We’re excited to introduce Rapid Alluxio Deployer (RAD) on AWS, which allows you to experience the performance benefits of Alluxio in less than 30 minutes. RAD is designed with a split-plane architecture, which ensures that your data remains secure within your AWS environment, giving you peace of mind while leveraging Alluxio’s capabilities.

PyTorch is one of the most popular deep learning frameworks in production today. As models become increasingly complex and dataset sizes grow, optimizing model training performance becomes crucial to reduce training times and improve productivity. 

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Co-hosted by Alluxio and Uber on May 23, 2024, AI/ML Infra Meetup was the community event for developers focused on building AI, ML and data infrastructure at scale. We were thrilled by the overwhelming interest and enthusiasm in our meetup!

This blog post delves into the history behind Trino introducing Alluxio as a replacement for RubiX as a file system cache. It explores the synergy between Trino and Alluxio, assesses which type of cache best suits various needs, and shares real-world examples of Trino and Alluxio adoption.

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Performance, cache operability, and cost efficiency are key considerations for AI platform teams supporting large scale model training and distribution. In 2023, we launched Alluxio Enterprise AI, for managing AI training and model distribution I/O across diverse environments, whether in a single storage with diverse computing clusters or in a more complex multi-cloud, multi-data center environment.

Automaker Geely’s Efficient Data Lake Architecture with Alluxio

This article was originally published on Spiceworks. https://www.spiceworks.com/tech/artificial-intelligence/guest-article/adapting-ai-platform-to-hybrid-cloud/

This blog discusses the challenges of implementing AI platforms in hybrid and multi-cloud environments and shares examples of organizations that have prioritized security and optimized cost management using the data access layer.

Optimizing PyTorch Training and Serving in Practice

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GPU utilization or GPU usage, is the percentage of GPUs’ processing power being used at a particular time. As GPUs are expensive resources, optimizing their utilization and reducing idle time is essential for enterprise AI infrastructure. This blog explores bottlenecks hindering GPU utilization during model training and provides solutions to maximize GPU utilization.

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This article was originally published on ITBrief. The author is Hope Wang, Developer Advocate, Alluxio.

As we celebrate International Women's Day, it is important to reflect on the progress we have made toward gender equality in the tech industry, particularly in open-source software (OSS). While there is still much work to be done, I am proud to be part of a community actively working to empower women and promote diversity. In this article, I want to share my path to the open-source community and offer advice to women developers interested in contributing to open-source projects.