Challenges and pain points
- Storage management challenges of billions to tens of billions of small files;
- Providing high-performance and stable data access guarantee for AI operations (such as model training) under the scale of massive data storage;
- Different data access interfaces are needed for different types of components such as deep learning framework, MPI framework, scientific computing library, and big data computing engine;
- AI pipelines are complicated and have long processes. Different stages of the process have different storage systems requirements;
- It’s difficult to combine AI jobs natively with Kubernetes, which is frequently used as an orchestration platform for AI scenarios, to maximize the benefits of a container platform on the cloud.
- The metadata engine of JuiceFS can scale horizontally and easily support the storage of tens of billions of small files;
- Ensure the efficiency and stability of AI jobs through multi-level cache acceleration;
- JuiceFS is fully compatible with POSIX, HDFS, and S3 API, and can seamlessly interface with any framework and components;
- Using JuiceFS as unified storage in the AI pipeline can reduce redundant data replicas and migration costs;
- JuiceFS provides Kubernetes CSI Driver support to access data through the Kubernetes native storage solution, which is friendly to the Kubernetes ecosystem;
- JuiceFS provides Linux’s standard user and user group access controls, providing data isolation and security guarantees for shared storage systems by different teams.