Thousand-Node Alluxio Cluster Powers Game AI Platform – A Production Case Study from Tencent

Tencent is one of the largest technology companies in the world and a leader in the gaming sector. The game AI platform supports AI research and development at Tencent. To provide model training with the best experience, Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to accelerate the game AI training.

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A Year with Alluxio Community 2021

2021 marked accelerated growth for the Alluxio Open Source Project. We could not be more grateful for what the community has achieved together in this past year. This blog provides a glimpse of the year long summary of our community growth. 

Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds

Alluxio Product School *

Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.

What’s New in Alluxio 2.7: Enhanced Scalability, Stability and Major Improvements in AI/ML Training Efficiency

With this release, Alluxio has strengthened its position as a de-facto data unification and acceleration solution in data analytics and machine learning pipelines. The solution is optimized to support Spark, Presto, Tensorflow, and PyTorch, and is available on multiple cloud platforms such as AWS, GCP, and Azure Cloud, and also on Kubernetes in private data centers or public clouds.

Enabling Hybrid Cloud Analytics and AI with Data Orchestration

IoT World Today Webinar *

Adit Madan and Parviz Peiravi offer an overview of the Alluxio data orchestration layer that provides a unified data access layer for hybrid and multi cloud deployments, leveraging Intel® Optane™ Persistent Memory for higher performance caching at reduced cost. The data access layer enables distributed compute engines like Presto, TensorFlow, and PyTorch to transparently access data from various storage systems (including S3, HDFS, and Azure) while actively leveraging a multi-tier cache to accelerate data access.