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. Keep reading on … Continued
Originally published at The New Stack: https://thenewstack.io/this-is-how-to-optimize-pytorch-for-faster-model-training/ 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. In this article, I’ll share the latest performance tuning tips to accelerate the training … Continued
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 event brought together over 100 AI/ML infrastructure engineers and enthusiasts to discuss the latest … Continued
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. Trino is an open-source distributed SQL query engine … Continued
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 … Continued
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. In recent years, AI platforms have undergone significant transformations as GenAI and AI continue to … Continued
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. 1. Why … Continued
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 … Continued
In the rapidly-evolving field of artificial intelligence (AI) and machine learning (ML), the efficient handling of large datasets during training is becoming more and more pivotal. Ray has emerged as a key player, enabling large-scale dataset training through effective data streaming. By breaking down large datasets into manageable chunks and dividing training jobs into smaller … Continued