[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-1edd86ea-3eb0-4424-9f57-add15c08d891":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"1edd86ea-3eb0-4424-9f57-add15c08d891","Hugging Face 把 Hub 升级成 SkyPilot 一等存储后端:FUSE 懒读 + Xet 去重让 20+ 云共享同一份模型数据","Hugging Face 与 SkyPilot 联合把 Hub 升级为 SkyPilot 的 `store: hf` 一等存储后端,通过 `hf:\u002F\u002F` URL 把任意 model、dataset、Space repo 直接挂载到 AWS、GCP、Lambda、CoreWeave 等 20+ 家云以及 Kubernetes\u002FSlurm 集群的 GPU 任务里,核心由两端实现:(1) 新建 `hf-mount` FUSE 后端 — 每次 `read()` 仅拉需要的那几个字节,避免完整复制,GPU 几乎\"立马上工\",并保留本地 on-disk 缓存让 repeat read 命中本地;(2) 基于 Xet 的内容定义分块 (CDC) — 把模型权重切成约 64 KB 的 chunk,只对真正修改部分重传,统一以 `store: hf` 暴露在 SkyPilot 的 file_mounts 里。基准测试用同一个 `qwen-sft.yaml` 在三家云切换 `--infra` 跑 Qwen3.5-4B SFT,模型首读约 30 秒(峰值 ~500 MB\u002Fs),8.43 GB checkpoint 写入 bucket 在 AWS L40S 上跑到 ~168 MB\u002Fs、GCP L4 约 123 MB\u002Fs、Lambda H100 约 112 MB\u002Fs。Xet 把 dedup 推到 Parquet 行级 append — 内部测试追加 10K 行到 100K 行表只上传约 10 MB,二次上传一个 已存的 8.43 GB blob 只需约 8 秒。这一改动是首次把\"数据稳坐 Hub、算力随便跑\"做成产品级契约,打破了长期以来\"对象存储在哪个云,GPU 调度就被钉死在哪个云\"的部署约束,跨云训练与推理的数据搬运成本被压到接近零。","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fskypilot-hf-storage","24d5c6c5-6573-4180-a1fd-f1459842d1af",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"7ac06d8e-b074-4147-abfc-ffaa4c6b8744","ai-efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-07-12T06:30:00Z","2026-07-11T22:14:09.053006Z","2026-07-11T22:14:09.053031Z",true,"agent",2]