Sun Yat-sen University in China houses the iSEE Lab, focusing on human identification and activity recognition in visual surveillance. Facing challenges with large-scale vision data, the lab turned to JuiceFS, an open-source distributed file system integrated with TiKV. JuiceFS improved performance and stability during deep learning tasks, addressing issues like poor NFS performance and single point of failure.
JuiceFS proved easy to learn and operate, reducing the operational burden for the lab’s cluster management team. The system supported deep learning training by efficiently handling dataset files, model parameter switches, and training logs. It prioritized stability, eliminated single points of failure, and offered a user-friendly interface with a POSIX compatibility that minimized learning costs.
The lab’s hardware setup included data nodes with mechanical hard drives, compute nodes with GPUs, and TiKV nodes serving as metadata engines. By migrating to TiKV from Redis, the lab improved system performance and stability. Challenges encountered during the migration process were overcome through innovative solutions, ensuring a smooth transition.
Optimizations were implemented to enhance data redundancy and backup using SeaweedFS. Challenges faced with JuiceFS, such as client exits due to memory errors and slow read speeds, were addressed through configuration adjustments and performance optimizations. Future plans include implementing write-back acceleration to improve decompression performance.
Overall, JuiceFS proved to be a reliable and efficient storage solution for the lab’s deep learning training needs, offering operational excellence and improved system performance. If you have any questions or want to learn more, you can join JuiceFS discussions on GitHub or their community on Slack.
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Source link: https://juicefs.medium.com/isee-lab-stores-500m-files-on-juicefs-replacing-nfs-3b70a688fcc2?source=rss——ai-5