Detailed Introduction
Fluid is a community-maintained open-source project that provides Kubernetes-native data abstraction and acceleration for big-data and AI applications. It encapsulates heterogeneous storage sources into a unified Dataset abstraction and offers an observable, elastic cache runtime in Kubernetes to significantly improve I/O performance and latency for data-intensive workloads.
Main Features
- Unified dataset abstraction that integrates multiple underlying stores with version management.
- Scalable cache runtimes with support for distributed caching, runtime plugins, and dataset warmup.
- Automated data operations with policy-driven prefetch, writeback, and synchronization to reduce manual operations.
- Data-aware scheduling that improves locality by considering data affinity during workload scheduling.
Use Cases
Fluid is suitable for accelerating large-scale training, model inference, and data analytics workloads, such as speeding training dataset access for deep learning, optimizing remote PVC access, batch data processing, and preparing cached corpora for RAG pipelines in LLM applications.
Technical Features
Built on Kubernetes and CSI, Fluid is designed to integrate with cloud-native ecosystems, supports Helm-based deployment, and integrates with runtimes like Alluxio and Vineyard. The project emphasizes observability, elastic scaling, and security, and is released under the Apache-2.0 license for broad enterprise adoption and extension.