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SEAWEEDFS
Simple, scalable distributed file system for billions of files
Apache-2.0
ABOUT
AI and data engineering teams need to store, retrieve, and replicate billions of files — model checkpoints, training datasets, embeddings, logs — across multiple machines. Traditional POSIX filesystems hit capacity and throughput limits, while full-blown distributed systems like HDFS introduce heavy operational overhead. SeaweedFS fills the gap with a lightweight distributed file system that delivers O(1) disk seek for reads, scales horizontally across commodity hardware, and exposes an S3-compatible API for seamless tool integration.
INSTALL
docker run --name seaweedfs -p 9333:9333 -p 8081:8081 chrislusf/seaweedfs serverINTEGRATION GUIDE
1. Store and serve billions of model checkpoints and training artifacts across a distributed GPU cluster
2. Host large-scale embedding stores and vector index snapshots with S3-compatible blob access
3. Build petabyte-scale data lake backends for AI training pipelines with erasure-coded redundancy
4. Serve as a high-throughput content store for data processing and feature engineering workflows
5. Replace single-node filesystem storage in multi-node inference serving and batch processing deployments
TAGS
distributed-storagefile-systems3-compatibleblob-storageobject-storageerasure-codingdata-pipeline