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Group Nodes


In some business scenarios, you may want to direct the Run:ai scheduler to schedule a Workload to a specific node or a node group. For example, in some academic institutions, Hardware is bought using a specific grant and thus "belongs" to a specific research group. Another example is an inference workload that is optimized to a specific GPU type and must have dedicated resources reserved to ensure enough capacity.

Run:ai provides two methods to designate, and group, specific resources:

  • Node Pools: Run:ai allows administrators to group specific nodes into a node pool. A node pool is a group of nodes identified by a given name (node pool name) and grouped by any label (key and value combination). The label can be chosen by the administrator or can be an existing, pre-set, label (such as an NVIDIA GPU type label).
  • Node Affinity: Run:ai allows this "taint" by labeling a node, or a set of nodes and then during scheduling, using the flag --node-type <label> to force this allocation.


One can set and use both node pool and node affinity combined as a prerequisite to the scheduler, for example, if a researcher wants to use a T4 node with an Infiniband card - he or she can use a node pool of T4 and from that group, choose only the nodes with Infiniband card (node-type = infiniband).

There is a tradeoff in place when allowing Researchers to designate specific nodes. Overuse of this feature limits the scheduler in finding an optimal resource and thus reduces overall cluster utilization.

Configuring Node Groups

To configure a node pool:

  • Find the label key & value you want to use for Run:ai to create the node pool.
  • Check that the nodes you want to group as a pool have a unique label to use, otherwise you should mark those nodes with your own uniquely identifiable label.
  • Get the names of the nodes you want Run:ai to group. To get a list of nodes, run:
kubectl get nodes
Kubectl get nodes --show-labels
  • If you chose to set your own label, run the following:
kubectl label node <node-name> <label-key>=<label-value>

The same value can be set to a single node, or multiple nodes. Node Pool can only use one label (key & value) at a time. * To create a node pool use the create node pool Run:ai API.

To configure a node affinity:

  • Get the names of the nodes where you want to limit Run:ai. To get a list of nodes, run:
kubectl get nodes
  • For each node run the following:
kubectl label node <node-name><label>

The same value can be set to a single node, or for multiple nodes. A node can only be set with a single value.

Using Node Groups via the CLI

To use Run:ai node pool with a workload, use Run:ai command-line interface command:

runai submit job1 ... --node-pool "my-pool"

To use node affinity, use the node type label with the --node-type flag:

runai submit job1 ... --node-type "my-nodes"

A researcher may combine the two flags to select both a node pool and a specific set of nodes out of that node pool (e.g. gpu-type=t4 and node-type=infiniband):

runai submit job1 ... --node-pool-name “my pool” --node-type "my-nodes"


When submitting a workload, if you chose a node pool label and a node affinity (node type) label which do not intersect, the Run:ai scheduler will not be able to schedule that workload as it represents an empty nodes group.

See the runai submit documentation for further information.

Assigning Node Groups to a Project

Node Pools are automatically assigned to all Projects and Departments with zero resource allocation as default. Allocating resources to a node pool can be done for each Project and Department. Submitting a workload to a node pool that has zero allocation for a specific project (or department) results in that workload running as an over-quota workload.

To assign and configure specific node affinity groups or node pools to a Project see working with Projects.

When the command-line interface flag is used in conjunction with Project-based affinity, the flag is used to refine the list of allowable node groups set in the Project.

Last update: 2022-11-13
Created: 2020-07-19