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 to maximize the benefits of a container platform on the cloud.
Why JuiceFS
- 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.
Solution and Benefits
- JuiceFS can be used as a unified storage system for AI datasets to support the management of massive small files and meet the requirements of different AI operations;
- JuiceFS provides high-performance and stable data access capabilities for different types of AI operations, full compatibility of POSIX, HDFS, and S3 API, and hassle-free use in TensorFlow, PyTorch, MXNet, and other frameworks.
- Use JuiceFS as the unified underlying storage for the data science workspace to ensure easy O&M and controlled access management without worrying about data loss or damage. No more data silos.
- Store public AI datasets in JuiceFS to be easily shared and accessed by different team members for better team collaboration.
