Documentation Index
Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
Use this file to discover all available pages before exploring further.
Scheduling profiles in Kubernetes
With Scheduling Profiles, you can customize the behavior of scheduling Pods onto Nodes to meet the requirements for your workloads. CoreWeave has several Scheduling Profiles, detailed below.CKS scheduling profiles
Thedefault-scheduler discourages scheduling workloads on Nodes that are incompatible with workload specifications. Other Scheduling Profiles should only be used in specific cases.
Profiles for edge cases
If you have a special case that requires a specialized scheduler, the following Profiles are also available.best-spread-scheduler
Aims to distribute Pods onto Nodes in a way that optimizes balancing resource usage. Minimizes the likelihood of one Node becoming overloaded. By avoiding single points of failure in use cases where optimizing balance is crucial, best-spread-scheduler ensures better resource utilization, cluster reliability, and fault tolerance.
least-allocated-scheduler
Prioritizes Nodes with the least allocated (i.e. most remaining available) resources when scheduling Pods. The least-allocated-scheduler ensures that Nodes are neither overburdened nor underutilized. It’s suitable for use cases where resource balancing is more of a priority than bin-packing capability.
binpack-scheduler
The binpack-scheduler maximizes resource efficiency, reducing costs and energy usage. It focuses on packing the highest possible number of Pods onto the smallest number of Nodes, leaving any unused ones free. It also delays adding Nodes, to ensure that each currently available Node is used fully first.
prioritize-image-locality
Favors Nodes that already have a locally cached version of Pod container images, helping to reduce the time spent pulling container images that would otherwise be pulled from remote sources. prioritize-image-locality reduces the delays caused by container image pulling, which is especially useful in cases where quick startup times are critical or where container images are especially large.
exhaustive
Considers all Pods in the cluster, and evaluates the optimal scheduling option before choosing one. The exhaustive Profile exhaustively evaluates all Node capacities.