LeaderWorkerSet: An API for deploying a group of pods as a unit of replication. It aims to address common deployment patterns of AI/ML inference workloads, especially multi-host inference workloads where the LLM will be sharded and run across multiple devices on multiple nodes. The initial design and proposal can be found at: https://bit.ly/k8s-LWS.
- Group of Pods as a unit: Supports a tightly managed group of pods that represent a “super pod”
- Unique pod identity: Each pod in the group has a unique index from 0 to n-1.
- Parallel creation: Pods in the group will have the same lifecycle and be created in parallel.
- Dual-template, one for leader and one for the workers: A replica is a group of a single leader and a set of workers, and allow to specify a template for the workers and optionally use a second one for the leader pod.
- Multiple groups with identical specifications: Supports creating multiple “replicas” of the above mentioned group. Each group is a single unit for rolling update, scaling, and maps to a single exclusive topology for placement.
- A scale subresource: A scale endpoint is exposed for HPA to dynamically scale the number replicas (aka number of groups)
- Rollout and Rolling update: Supports performing rollout and rolling update at the group level, which means the groups are upgraded one by one as a unit (i.e. the pods within a group are updated together).
- Topology-aware placement: Opt-in support for pods in the same group to be co-located in the same topology.
- All-or-nothing restart for failure handling: Opt-in support for all pods in the group to be recreated if one pod in the group failed or one container in the pods is restarted.
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Read the examples to learn more.
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