Currently, the CNI barely meets some increasingly prominent network demands. Especially AI workloads requiring network cards in buck, the node scheduling could not simultaneously fullfill requirements for the macvlan master interface, subnet availability, and RDMA resources. Additionally, the physical affinity between the allocated network cards and GPU is not guaranteed to achieve high-performance transfers such as GPUDirectRDMA. Therefore, many end users complain and have to write complex network configurations in yaml and simply insert all network interfaces for pods to ensure device affinity, which is rigid.
Based on the practice requirements of popular tail-optimized AI network topology, in latest version, Spiderpool introduces DRA to uniformly declare various network configurations, schedule AI workloads to proper nodes, and automatically allocate network interfaces, IP addresses, and RDMA devices with GPU affinity on demand. This greatly enhances the flexibility in AI clusters.