This guide shows how to use ACK Fluid to mount third‑party storage via host‑path directories as Kubernetes PVs, enabling standard CSI integration, data isolation, and high‑performance, low‑cost access with step‑by‑step commands, YAML manifests, and performance validation.
This guide explains how to use ACK Fluid to accelerate third‑party storage access for Kubernetes workloads in hybrid‑cloud scenarios, covering performance challenges, configuration steps, dataset and JindoRuntime creation, cache pre‑warming, and verification of fast data reads.
This article details how the Fluid CNCF project combined with JindoRuntime was used to overcome storage‑compute separation bottlenecks in an autonomous‑driving machine‑learning platform, achieving up to 300% faster training, reduced OSS bandwidth pressure, and higher GPU utilization through distributed caching on Kubernetes.
Weibo’s deep learning platform faced severe latency and stability issues when accessing massive small‑file datasets via a compute‑storage‑separated architecture, so the team adopted the CNCF Fluid project with JindoRuntime, implementing a distributed cache that leverages POSIX interfaces, dramatically improving data locality, reducing HDFS load, and achieving up to 18‑fold training speedups while raising success rates from 37 % to 98 %.
Fluid is an open‑source Kubernetes‑native engine that orchestrates and accelerates distributed datasets for AI and big‑data workloads, and this guide explains its core concepts, the JindoRuntime implementation, performance benefits, and step‑by‑step instructions to deploy and test JindoRuntime on a K8s cluster.
