Challenges and pain points
- AI workflow pipelines are complex and lengthy, with different computing, training, and application frameworks requiring different data access methods. This demands the deployment of multiple storage systems supporting various access protocols. Consequently, data disperses across these systems, which poses challenges in data management, increases maintenance burdens, and affects application efficiency.
- I/O performance impacts GPU utilization, thereby affecting various stages of AI workflow pipelines, including data preprocessing, dataset reading during training, checkpoint writing, and model loading. These factors directly influence costs and time-to-market for enterprise products.
- The training datasets of some models, such as visual and multi-modal models, contain billions to tens of billions of small files. Handling lots of small files (LOSF) has always been a challenge in the storage domain.
- Most storage systems' performance scales linearly with data volume or the number of disks. However, scaling clusters to support growing application loads brings complex maintenance tasks and may disrupt existing services. It is challenging to find high-performance and elastic storage solutions for AI tasks.
- In large-scale AI model training, like large language model (LLM) training, the growth of clusters and data scale increases data access pressure, while traditional storage area network (SAN) and network attached storage (NAS) systems face scalability challenges in supporting thousands or even tens of thousands of concurrent client requests.
- The significant demand for GPU computing power in AI model training has made multi-cloud and hybrid-cloud configurations standard for many enterprises. However, directly accessing data across different locations in a multi-cloud or hybrid-cloud architecture from central storage faces bandwidth and cost constraints, and it is difficult to meet performance requirements.
- Data is a crucial asset for AI businesses, often used by multiple teams. Management requirements for datasets, checkpoints, logs, and other data types include permission settings, versioning, capacity limits, and integration with environments like Kubernetes. Therefore, storage system selection needs to consider challenges like ACL control, subdirectory mounting, and Kubernetes deployment methods.
- JuiceFS provides interfaces compatible with POSIX, HDFS, and S3, serving as a unified storage solution for AI job pipelines, thereby reducing unnecessary data copying and migration efforts.
- JuiceFS offers data isolation and security for shared storage systems across different teams, featuring capabilities such as token-based mounting and access control, Linux file permissions, POSIX ACLs, subdirectory mounting, capacity and inode quotas, as well as traffic QoS (Quality of Service).
- JuiceFS implements caching, prefetching, and concurrent reading strategies to enhance I/O efficiency. Its proprietary high-performance metadata service can handle millions of requests per second, with average response times ranging from milliseconds to hundreds of microseconds.
- In scenarios like model training that require more reads than writes, JuiceFS employs multi-level caching technology to accelerate data retrieval. In model training, JuiceFS’ read throughput can elastically scale to hundreds of gigabytes per second.
- The metadata service and object storage of JuiceFS can meet the concurrent demands of thousands to tens of thousands of clients. The automatic caching feature of the JuiceFS client can significantly reduce the load on the metadata service and object storage during model training, further enhancing the overall capacity of the storage system.
- The mirror file system provides enterprises with efficient cross-regional and cross-cloud data storage, sharing, and synchronization capabilities.
- JuiceFS Enterprise Edition is designed with high-availability, all-memory metadata services tailored for high-performance file system requirements. It can be scaled horizontally online, easily managing tens of billions of files.