rp cluster create
Provision a homogeneous multi-node cluster.
rp cluster create --name <n> --type <kind> --gpu <type>
[--pod-count N] [--gpu-count N] [flags]
Options
--name <n> cluster name (required)
--type <kind> APPLICATION|TRAINING|SLURM|RAY (required)
--gpu <type> one GPU type for every pod (required)
--gpu-count N GPUs per pod (default: 1; minimum 1)
--pod-count N number of pods (minimum 2, default: 2)
--dc <id,…> preferred datacentres; omit to let the
scheduler place the cluster (single DC)
--image <ref> Docker image for every pod
--container-disk-gb N ephemeral container disk, GB (minimum 1)
--ports <a/b,…> exposed ports, each as port/protocol
--env K=V environment variable; repeatable
--start-cmd <a,b,…> arguments passed to the container entrypoint
--network-volume-id <id> attach one network volume to every pod
--volume-path <path> mount path for the network volume
--template-id <id> seed container config from a template id
--start-ssh true|false provision SSH with your account key
--start-jupyter true|false start Jupyter on every member pod
--force create even when the name is taken
Notes
A cluster is homogeneous: every pod is identical, so a single --gpu type,
--gpu-count, and --pod-count describe the whole fleet. --pod-count must be at
least 2 (a cluster is multi-node by definition).
Creation is idempotent by name, like rp volume create and
rp serverless create: where a cluster of that name already exists, the CLI
prints its id and skips the POST. --force sends the request regardless.
Only the name can change afterwards — rp cluster update is a rename, and
compute shape, type, and container config are fixed at create.
Clusters do not yet support private registries (no --registry): the v2
create request has no registry field. Use a public image or a network volume
for your build.
--template-id seeds the container config as defaults; any explicit flag value
still wins, and the template's id is recorded on the cluster.
Examples
# Create a 4-node H100 training cluster
$ rp cluster create --name tr-1 --type TRAINING \
--gpu "NVIDIA H100 80GB HBM3" --pod-count 4 --gpu-count 8
# Create a Ray cluster with an attached volume
$ rp cluster create --name ray --type RAY --gpu "NVIDIA L4" \
--image runpod/ray:latest --network-volume-id vol_xyz
API: POST /v2/clusters