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Rancher RKE Deployment on Crusoe Cloud

Known Issues

If the Terraform below fails contact [email protected] for help.

Requirements

Deployment

To use as a module fill in the variables in your own main.tf file

module "crusoe" {
  source = "github.com/crusoecloud/crusoe-ml-rke2"
  ssh_privkey_path="</path/to/priv.key"
  ssh_pubkey="<pub_key>"
  worker_instance_type = "h100-80gb-sxm-ib.8x"
  worker_image = "ubuntu22.04-nvidia-sxm-docker:latest"
  worker_count = 2
  ib_partition_id = "6dcef748-dc30-49d8-9a0b-6ac87a27b4f8"
  headnode_instance_type="c1a.8x"
  deploy_location = "us-east1-a"
  # extra variables here
}

To use from this directory, fill in the variables in a terraform.tfvars file

ssh_privkey_path="</path/to/priv.key"
ssh_pubkey="<pub_key>"
worker_instance_type = "h100-80gb-sxm-ib.8x"
worker_image = "ubuntu22.04-nvidia-sxm-docker:latest"
worker_count = 2
ib_partition_id = "6dcef748-dc30-49d8-9a0b-6ac87a27b4f8"
headnode_instance_type="c1a.8x"
deploy_location = "us-east1-a"
# extra variables here

And then apply, to provision resources

terraform init
terraform plan
terraform apply

Accessing the Cluster

Once the deployment is complete, you can access the cluster by copying the kubeconfig file from the headnode. Replace the 'server' address in the Kubeconfig with that of your load balancer (or control plane node when deploying single control plane node configurations).

rke_endpoint=$(terraform output -raw rke-ingress-instance_public_ip)
headnode_endpoint=$(terraform output -raw rke-headnode-instance_public_ip)
scp -i $TF_VAR_ssh_privkey_path "root@${headnode_endpoint}:/etc/rancher/rke2/rke2.yaml" ./kubeconfig
# change the endpoint to the kubectl endpoint
sed -i '' "s/127.0.0.1/${rke_endpoint}/g" ./kubeconfig
# rename the context (optional)
sed -i '' "s/default/crusoe/g" ./kubeconfig
export KUBECONFIG="$(pwd)/kubeconfig"

Nvidia GPU Support

For nodes with Nvidia GPUs, you can run the following commands to install the Nvidia GPU operators (along with the Network operator when using IB-enabled nodes) to ensure they are available for use by pods provisioned in the cluster.

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm install --wait --generate-name -n gpu-operator --create-namespace nvidia/gpu-operator --set driver.rdma.enabled=true --set driver.rdma.useHostMofed=true
helm install network-operator nvidia/network-operator -n nvidia-network-operator --create-namespace -f ./gpu-operator/values.yaml --wait

To test the GPU infiniband speeds, you can run the following commands.

kubectl apply -f https://raw.githubusercontent.com/kubeflow/mpi-operator/v0.4.0/deploy/v2beta1/mpi-operator.yaml
kubectl apply -f examples/nccl-test.yaml