The File Storage Challenges for Autonomous Driving Teams
- In autonomous driving model training, datasets often comprise billions to tens of billions of small files. Each training involves tens of millions to hundreds of millions of files. Storage systems are challenged to manage this vast number of small files.
- It's critical to overcome performance challenges for high throughput and low latency during training massive small files in autonomous driving.
- The artificial intelligence (AI) pipeline is complex and dynamic. It involves integrating different components like machine learning (ML) / deep learning (DL) frameworks, message passing interface (MPI) frameworks, scientific computing libraries, and big data processing engines. This increases the system complexity and brings challenges for storage management.
- It's difficult to optimize collaboration among distributed teams and implement unified storage management in hybrid cloud and multi-cloud environments.
- As data volumes rapidly increase, enterprises face total cost of ownership (TCO) challenges such as storage and operational costs.
- JuiceFS' metadata engine is horizontally scalable and eliminates single points of bottleneck. It efficiently manages storage for tens of billions of files and hundreds of petabytes of data within a single namespace.
- JuiceFS provides a distributed caching cluster that enables fast, low-latency, and high-throughput I/O access in hybrid cloud architectures. For model training, it delivers read throughput of tens of gigabytes per second, handling hundreds of thousands of files with millisecond-level metadata response time.
- With full POSIX compatibility, JuiceFS seamlessly integrates into the AI pipeline without requiring additional adaptations. It enables unified data management and improves efficiency across all pipeline stages.
- In hybrid and multi-cloud environments, JuiceFS automatically mirrors data to facilitate collaboration among distributed teams. Its built-in caching significantly reduces reliance on dedicated connections.
- JuiceFS uses object storage to offer elastic scalability of storage capacity and significantly reduce storage costs. Its flexible architecture minimizes learning, maintenance, and migration expenses.