Clabernetes Quickstart

The best way to understand how clabernetes works is to walk through a short example where we deploy a simple but representative lab using clabernetes.

Do you already have a kubernetes cluster? Great! You can skip the cluster creation step and jump straight to Installing Clabernetes part.

But if you don't have a cluster yet, don't panic, we'll create one together. We are going to use kind to create a local kubernetes cluster and then install clabernetes into it. Once clabernetes is installed we deploy a small topology with two SR Linux nodes and two client nodes.

If all goes to plan, the lab will be successfully deployed! Clabverter & clabernetes work in unison to make the original topology files deployable onto the cluster with tunnels stitching lab nodes together to form point to point connections between the nodes.

Let's see how it all works, buckle up!

Creating a cluster

Clabernetes goal is to allow users to run networking labs with containerlab's simplicity and ease of use, but with the scaling powers of kubernetes. Surely, it is best to have a real deal available to you, but for demo purposes we'll use kind v0.22.0 to create a local multi-node kubernetes cluster. If you already have a k8s cluster, feel free to use it instead -- clabernetes can run in any kubernetes cluster¹!

With the following command we instruct kind to set up a three node k8s cluster with two worker and one control plane nodes.

kind create cluster --name c9s --config - <<EOF
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
  - role: control-plane
  - role: worker
  - role: worker
containerdConfigPatches:
- |-
  [plugins."io.containerd.grpc.v1.cri".containerd]
    discard_unpacked_layers = false
EOF

Don't forget to install kubectl!

Check that the cluster is ready and proceed with installing clabernetes.

❯ kubectl get nodes 
NAME                STATUS   ROLES           AGE     VERSION
c9s-control-plane   Ready    control-plane   5m6s    v1.29.2
c9s-worker          Ready    <none>          4m46s   v1.29.2
c9s-worker2         Ready    <none>          4m42s   v1.29.2

Installing clabernetes

Clabernetes is installed into a kubernetes cluster using helm:

We use alpine/helm container image here instead of installing Helm locally; you can skip this step if you already have helm installed.

For self-hostedFor GCP/GKE

alias helm='docker run --network host -ti --rm -v $(pwd):/apps -w /apps \
    -v ~/.kube:/root/.kube -v ~/.helm:/root/.helm \
    -v ~/.config/helm:/root/.config/helm \
    -v ~/.cache/helm:/root/.cache/helm \
    alpine/helm:3.12.3'

GKE clusters require gke-gcloud-auth-plugin to be available. Make sure you have it installed and mounted into the container.

alias helm='docker run --network host -ti --rm -v $(pwd):/apps -w /apps \
    -v /usr/bin/gke-gcloud-auth-plugin:/usr/bin/gke-gcloud-auth-plugin \
    -v ~/.kube:/root/.kube -v ~/.helm:/root/.helm \
    -v ~/.config/helm:/root/.config/helm \
    -v ~/.cache/helm:/root/.cache/helm \
    alpine/helm:3.12.3'

helm upgrade --install --create-namespace --namespace c9s \
    clabernetes oci://ghcr.io/srl-labs/clabernetes/clabernetes

Note, that we install clabernetes in a c9s namespace. This is not a requirement, but it is a good practice to keep clabernetes manager deployment in a separate namespace.

A successful installation will result in a clabernetes-manager deployment of three pods running in the cluster:

kubectl get -n c9s pods -o wide #(1)!

1. Note, that clabernetes-manager is installed as a 3-node deployment, and you can see that two pods might be in Init stay for a little while until the leader election is completed.

NAME                                   READY   STATUS    RESTARTS   AGE    IP            NODE          NOMINATED NODE   READINESS GATES
clabernetes-manager-7ccb98897c-7ctnt   1/1     Running   0          103s   10.244.2.15   c9s-worker    <none>           <none>
clabernetes-manager-7ccb98897c-twzxw   1/1     Running   0          96s    10.244.1.15   c9s-worker2   <none>           <none>
clabernetes-manager-7ccb98897c-xhgkl   1/1     Running   0          103s   10.244.1.14   c9s-worker2   <none>           <none>

Installing Load Balancer

To get access to the nodes deployed by clabernetes from outside the k8s cluster we need a load balancer. Any load balancer will do, but we will use kube-vip in this quickstart.

Note

Load Balancer installation can be skipped if you don't need external access to the lab nodes. You can still access the nodes from inside the cluster by entering the pod's shell and then logging into the node.

Following kube-vip + kind installation instructions we execute the following commands:

kubectl apply -f https://kube-vip.io/manifests/rbac.yaml
kubectl apply -f https://raw.githubusercontent.com/kube-vip/kube-vip-cloud-provider/main/manifest/kube-vip-cloud-controller.yaml
kubectl create configmap --namespace kube-system kubevip \
  --from-literal range-global=172.18.1.10-172.18.1.250

Next we set up the kube-vip CLI:

KVVERSION=$(curl -sL https://api.github.com/repos/kube-vip/kube-vip/releases | \
  jq -r ".[0].name")
alias kube-vip="docker run --network host \
  --rm ghcr.io/kube-vip/kube-vip:$KVVERSION"

And install kube-vip load balancer daemonset in ARP mode:

kube-vip manifest daemonset --services --inCluster --arp --interface eth0 | \
kubectl apply -f -

We can check kube-vip daemonset pods are running on both worker nodes:

kubectl get pods -A -o wide | grep kube-vip

kube-system          kube-vip-cloud-provider-54c878b6c5-qwvf5    1/1     Running   0          91s   10.244.0.5   c9s-control-plane   <none>           <none>
kube-system          kube-vip-ds-fj7qp                           1/1     Running   0          9s    172.18.0.3   c9s-worker2         <none>           <none>
kube-system          kube-vip-ds-z8q67                           1/1     Running   0          9s    172.18.0.4   c9s-worker          <none>           <none>

Clabverter

Clabernetes motto is "containerlab at scale" and therefore we wanted to make it work with the same topology definition file format as containerlab does. Understandably though, the original Containerlab's topology file is not something you can deploy on Kubernetes cluster as is.

To make sure you have a smooth sailing in the clabernetes waters we've created a clabernetes companion tool called clabverter; it takes a containerlab topology file and converts it to several manifests native to Kubernetes and clabernetes. Clabverter then can also apply those manifests to the cluster on your behalf.

Clabverter is not a requirement to run clabernetes, but it is a helper tool to convert containerlab topologies to clabernetes resources and kubernetes objects.

As per clabverter's installation instructions we will setup an alias that uses the latest available clabverter container image:

set up clabverter alias

alias clabverter='sudo docker run --user $(id -u) \
    -v $(pwd):/clabernetes/work --rm \
    ghcr.io/srl-labs/clabernetes/clabverter'

Deploying with clabverter

We are now ready to deploy our lab using clabernetes with the help of clabverter. First we clone the lab repository:

Cloning the lab

git clone --depth 1 https://github.com/srl-labs/srlinux-vlan-handling-lab.git \
  && cd srlinux-vlan-handling-lab

And then, while standing in the lab directory, let clabverter do its job:

Converting the containerlab topology to clabernetes manifests and applying it

clabverter --stdout --naming non-prefixed | \
kubectl apply -f - #(1)!

1. clabverter converts the original containerlab topology to a set of k8s manifests and applies them to the cluster.

   We will cover what clabverter does in more details in the user manual some time later, but if you're curious, you can check the manifests it generates by running clabverter --stdout > manifests.yml and inspecting the manifests.yml file.

   The non-prefixed naming scheme instructs clabernetes to not use additional prefixes for the resources, as in this scenario we control the namespace and the resources are not going to clash with other resources in the cluster.

In the background, clabverter created the Topology custom resource (CR) in the c9s-vlan⁵ namespace that defines our topology and also created a set of config maps for each startup config used in the lab.

Verifying the deployment

Once clabverter is done, clabernetes controller casts its spell known as reconciliation in the k8s world. It takes the spec of the Topology CR and creates a set of deployments, config maps and services that are required for lab's operation.

Let's run some verification commands to see what we have in our cluster so far.

Namespace

Remember how in Containerlab world if you wanted to run multiple labs on the same host you would give each lab a distinct name and containerlab would use that name to create a unique prefix for containers? In k8s world, we use namespaces to achieve the same goal.

When Clabverter parses the original topology file, it takes the lab name value, prepends it with c9s- string and uses it as a namespace for the lab resources. This way, we can run multiple labs in the same k8s cluster without worrying about resource name clashes.

kubectl get ns

NAME                 STATUS   AGE
c9s                  Active   11m
c9s-vlan             Active   9m8s
default              Active   12h
kube-node-lease      Active   12h
kube-public          Active   12h
kube-system          Active   12h
local-path-storage   Active   12h

As you can see, we have two namespaces: c9s and c9s-vlan. The c9s namespace is where clabernetes manager is running and the c9s-vlan namespace is where our lab resources are deployed.

Topology resource

The main clabernetes resource is called Topology and we should be able to find it in the c9s-vlan namespace where all lab resources are deployed:

kubectl get --namespace c9s-vlan Topology

NAME   KIND           AGE
vlan   containerlab   14h

Looking in the Topology CR we can see that the original containerlab topology definition can be found under the spec.definition.containerlab field of the custom resource. Clabernetes took the original topology and split it to sub-topologies that are outlined in the status.configs section of the resource:

kubectl get --namespace c9s-vlan Topology vlan -o yaml

spec.configstatus.configs

spec:
  definition:
    containerlab: |-
      name: vlan

      topology:
        nodes:
          srl1:
            kind: nokia_srlinux
            image: ghcr.io/nokia/srlinux:23.10.1
            startup-config: configs/srl.cfg

          srl2:
            kind: nokia_srlinux
            image: ghcr.io/nokia/srlinux:23.10.1
            startup-config: configs/srl.cfg

          client1:
            kind: linux
            image: ghcr.io/srl-labs/alpine
            binds:
              - configs/client.sh:/config.sh
            exec:
              - "ash -c '/config.sh 1'"

          client2:
            kind: linux
            image: ghcr.io/srl-labs/alpine
            binds:
              - configs/client.sh:/config.sh
            exec:
              - "ash -c '/config.sh 2'"

        links:
          # links between client1 and srl1
          - endpoints: [client1:eth1, srl1:e1-1]

          # inter-switch link
          - endpoints: [srl1:e1-10, srl2:e1-10]

          # links between client2 and srl2
          - endpoints: [srl2:e1-1, client2:eth1]

# --snip--
status:
  configs:
    client1: |
      name: clabernetes-client1
      prefix: ""
      topology:
          defaults:
              ports:
                  - 60000:21/tcp
                  # here goes a list of exposed ports
          nodes:
              client1:
                  kind: linux
                  image: ghcr.io/srl-labs/alpine
                  exec:
                      - ash -c '/config.sh 1'
                  binds:
                      - configs/client.sh:/config.sh
                  ports: []
          links:
              - endpoints:
                  - client1:eth1
                  - host:client1-eth1
      debug: false
    client2: |
      name: clabernetes-client2
      # similar configuration as for client1
    srl1: |
      name: clabernetes-srl1
      prefix: ""
      topology:
          defaults:
              ports:
                  - 60000:21/tcp
                  # here goes a list of exposed ports
          nodes:
              srl1:
                  kind: nokia_srlinux
                  startup-config: configs/srl.cfg
                  image: ghcr.io/nokia/srlinux:23.10.1
                  ports: []
          links:
              - endpoints:
                  - srl1:e1-1
                  - host:srl1-e1-1
              - endpoints:
                  - srl1:e1-10
                  - host:srl1-e1-10
      debug: false
    srl2: |
      name: clabernetes-srl2
      # similar configuration as for srl1

If you take a closer look at the sub-topologies you will see that they are just mini, one-node-each, containerlab topologies. Clabernetes deploys these sub-topologies as deployments in the cluster.

Deployments

The deployment objects created by Clabernetes are the vessels that carry the lab nodes. Let's list those deployments:

kubectl -n c9s-vlan get deployments

NAME      READY   UP-TO-DATE   AVAILABLE   AGE
client1   1/1     1            1           16m
client2   1/1     1            1           16m
srl1      1/1     1            1           16m
srl2      1/1     1            1           16m

Those deployment names should be familiar as they are named exactly as the nodes in the original topology file.

Pods

Each deployment consists of exactly one k8s pod.

Listing lab pods

kubectl get pods --namespace c9s-vlan -o wide

NAME                       READY   STATUS    RESTARTS   AGE   IP            NODE          NOMINATED NODE   READINESS GATES
client1-5c4698f68c-v4z2n   1/1     Running   0          19m   10.244.1.15   c9s-worker    <none>           <none>
client2-6dfc49bc8f-hpkd4   1/1     Running   0          19m   10.244.2.15   c9s-worker2   <none>           <none>
srl1-78bdc85795-l9bl4      1/1     Running   0          19m   10.244.1.14   c9s-worker    <none>           <none>
srl2-7fffcdb79-vxfn9       1/1     Running   0          19m   10.244.2.16   c9s-worker2   <none>           <none>

We see four pods running, one pod per each lab node of our original containerlab topology. Pods are scheduled on different worker nodes by the k8s scheduler ensuring optimal resource utilization².

Each pod is a docker-in-docker container with Containerlab running inside.
Inside each pod, containerlab runs the sub-topology as if it would run on a standalone Linux system. It has access to the Docker API and schedules nodes in exactly the same way as if no k8s exists
We can enter the pod's shell and use containerlab CLI as usual:

Fancy command to enter the pod's shellRegular command

NS=c9s-vlan POD=client1; \
kubectl -n $NS exec -it \
  $(kubectl -n $NS get pods | grep ^$POD | awk '{print $1}') -- bash

kubectl exec -it -n c9s-vlan client1-5c4698f68c-v4z2n -- bash

And in the pod's shell we swim in the familiar containerlab waters:

[*]─[client1]─[/clabernetes]
└──> containerlab inspect #(1)!

1. If you do not see any nodes in the inspect output give it a few minutes, as containerlab is pulling the image and starting the nodes. Monitor this process with tail -f containerlab.log.

INFO[0000] Parsing & checking topology file: topo.clab.yaml
+---+---------+--------------+-------------------------+-------+---------+----------------+----------------------+
| # |  Name   | Container ID |          Image          | Kind  |  State  |  IPv4 Address  |     IPv6 Address     |
+---+---------+--------------+-------------------------+-------+---------+----------------+----------------------+
| 1 | client1 | dbde60209a3b | ghcr.io/srl-labs/alpine | linux | running | 172.20.20.2/24 | 2001:172:20:20::2/64 |
+---+---------+--------------+-------------------------+-------+---------+----------------+----------------------+

We can cat topo.clab.yaml to see the subset of a topology that containerlab started in this pod.

topo.clab.yaml

[*]─[client1]─[/clabernetes]
└──> cat topo.clab.yaml
name: clabernetes-client1
prefix: ""
topology:
    defaults:
        ports:
            - 60000:21/tcp
            - 60001:22/tcp
            - 60002:23/tcp
            - 60003:80/tcp
            - 60000:161/udp
            - 60004:443/tcp
            - 60005:830/tcp
            - 60006:5000/tcp
            - 60007:5900/tcp
            - 60008:6030/tcp
            - 60009:9339/tcp
            - 60010:9340/tcp
            - 60011:9559/tcp
            - 60012:57400/tcp
    nodes:
        client1:
            kind: linux
            image: ghcr.io/srl-labs/alpine
            exec:
                - ash -c '/config.sh 1'
            binds:
                - configs/client.sh:/config.sh
            ports: []
    links:
        - endpoints:
            - client1:eth1
            - host:client1-eth1
debug: false

It is worth reiterating, that unmodified containerlab runs inside a pod as if it would've run on a Linux system in a standalone mode. It has access to the Docker API and schedules nodes in exactly the same way as if no k8s exists.

Accessing the nodes

There are two common ways to access the lab nodes deployed with clabernetes:

1. Using external address provided by the Load Balancer service.
2. Entering the pod's shell and from there log in the running lab node. No load balancer required.

We are going to show you both options and you can choose the one that suits you best.

Load Balancer

Adding a Load Balancer to the k8s cluster makes accessing the nodes almost as easy as when working with containerlab. The kube-vip load balancer that we added before is going to provide an external IP address for a LoadBalancer k8s service that clabernetes creates for each deployment under its control.

By default, clabernetes exposes³ the following ports for each lab node:

Protocol	Ports
tcp	21, 80, 443, 830, 5000, 5900, 6030, 9339, 9340, 9559, 57400
udp	161

Let's list the services in the c9s-vlan namespace (excluding the services for VXLAN tunnels⁶):

kubectl get -n c9s-vlan svc | grep -iv vx

NAME         TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)                                                                                                                                                                                                   AGE
client1      LoadBalancer   10.96.211.232   172.18.1.12   161:30091/UDP,21:30581/TCP,22:31874/TCP,23:32665/TCP,80:31942/TCP,443:30624/TCP,830:32443/TCP,5000:31655/TCP,5900:30127/TCP,6030:30897/TCP,9339:31986/TCP,9340:31502/TCP,9559:30593/TCP,57400:30514/TCP   34m
client2      LoadBalancer   10.96.235.247   172.18.1.13   161:32008/UDP,21:31200/TCP,22:32186/TCP,23:30796/TCP,80:30124/TCP,443:30187/TCP,830:30599/TCP,5000:30600/TCP,5900:32719/TCP,6030:30678/TCP,9339:30998/TCP,9340:31592/TCP,9559:30312/TCP,57400:32051/TCP   34m
srl1         LoadBalancer   10.96.229.4     172.18.1.10   161:31220/UDP,21:32631/TCP,22:30595/TCP,23:32519/TCP,80:32630/TCP,443:30780/TCP,830:32259/TCP,5000:30647/TCP,5900:30540/TCP,6030:31110/TCP,9339:31216/TCP,9340:31168/TCP,9559:32483/TCP,57400:31009/TCP   34m
srl2         LoadBalancer   10.96.106.189   172.18.1.11   161:31380/UDP,21:30286/TCP,22:32764/TCP,23:31631/TCP,80:31049/TCP,443:32272/TCP,830:31237/TCP,5000:31495/TCP,5900:32592/TCP,6030:31776/TCP,9339:30649/TCP,9340:31538/TCP,9559:30628/TCP,57400:30586/TCP   34m

We see four LoadBalancer services created for each node of our distributed topology. Each service points (using selectors) to the corresponding pod.

The LoadBalancer services (powered by the kube-vip) also provide us with the external IPs for the lab nodes. The long list of ports are the ports clabernetes exposes by default which spans both regular SSH and other well-known management interfaces and their ports.

For instance, we see that srl1 node has been assigned 172.18.1.10 IP, and we can immediately SSH into it from the outside world using the following command:

ssh admin@172.18.1.10

................................................................
:                  Welcome to Nokia SR Linux!                  :
:              Open Network OS for the NetOps era.             :
:                                                              :
:    This is a freely distributed official container image.    :
:                      Use it - Share it                       :
:                                                              :
: Get started: https://learn.srlinux.dev                       :
: Container:   https://go.srlinux.dev/container-image          :
: Docs:        https://doc.srlinux.dev/23-10                   :
: Rel. notes:  https://doc.srlinux.dev/rn23-10-1               :
: YANG:        https://yang.srlinux.dev/release/v23.10.1       :
: Discord:     https://go.srlinux.dev/discord                  :
: Contact:     https://go.srlinux.dev/contact-sales            :
................................................................

admin@172.18.1.10's password:
Using configuration file(s): []
Welcome to the srlinux CLI.
Type 'help' (and press <ENTER>) if you need any help using this.
--{ running }--[  ]--
A:srl1#  

Other services, like gNMI, JSON-RPC, SNMP are available as well since those ports are already exposed.

"gNMI access"

gnmic -a 172.18.1.10 -u admin -p 'NokiaSrl1!' --skip-verify -e json_ietf \
  get --path /system/information/version

[
  {
    "source": "172.18.1.10",
    "timestamp": 1707828542585726740,
    "time": "2024-02-13T14:49:02.58572674+02:00",
    "updates": [
      {
        "Path": "srl_nokia-system:system/srl_nokia-system-info:information/version",
        "values": {
          "srl_nokia-system:system/srl_nokia-system-info:information/version": "v23.10.1-218-ga3fc1bea5a"
        }
      }
    ]
  }
]

Pod shell

Load Balancer makes it easy to get external access to the lab nodes, but don't panic if for whatever reason you can't have one. It is still possible to access the nodes without the load balancer and external IP addresses using various techniques. One of them is to enter the pod's shell and from there log in the running lab node.

For example, to access srl1 lab node in our k8s cluster we can leverage kubectl exec command to get to the shell of the pod that runs srl1 node.

Note

You may have a stellar experience with k9s project that offers a terminal UI to interact with k8s clusters. It is a great tool to have in your toolbox.

If Terminal UI is not your jam, take a look at kubectl shell completions. They come in super handy, install them if you haven't yet.

Since all pods are named after the nodes they are running, we can find the right one by listing all pods in a namespace:

kubectl get pods -n c9s-vlan

NAME                       READY   STATUS    RESTARTS   AGE
client1-5c4698f68c-v4z2n   1/1     Running   0          66m
client2-6dfc49bc8f-hpkd4   1/1     Running   0          66m
srl1-78bdc85795-l9bl4      1/1     Running   0          66m
srl2-7fffcdb79-vxfn9       1/1     Running   0          66m

Looking at the pod named srl1-78bdc85795-l9bl4 we clearly see that it runs the srl1 node we specified in the topology. To get shell access to this node we can run:

fancy commandregular command

NS=c9s-vlan POD=srl1; \
kubectl -n $NS exec -it \
  $(kubectl -n $NS get pods | grep ^$POD | awk '{print $1}') -- ssh $POD

kubectl -n c9s-vlan exec -it srl1-78bdc85795-l9bl4 -- ssh srl1 #(1)!

1. If you installed kubectl shell completions, the pod names will be suggested as you type the command. Very handy!

We essentially execute ssh srl1 command inside the pod, as you'd normally do with containerlab.

Datapath stitching

One of the challenges associated with the distributed labs is the connectivity between the lab nodes running on different computes.

Thanks to Kubernetes and its services, the management network access is taken care of. You get access to the management interfaces of each pod out of the box. But what about the non-management/datapath links we have in the original topology file?

In containerlab the links defined in the topology are often represented by the veth pairs between the containers running on a single host, but things are a bit more complicated in the distributed environments.

If you rewind it back to the beginning of the quickstart where we looked at the Topology CR you would notice that it has the familiar links section in the spec.definition.containerlab field:

# snip
links:
  - endpoints: ["srl1:e1-10", "srl2:e1-10"]

This link connects srl1 node with srl2, and as we saw these nodes are running on different worker nodes in the k8s cluster.
How does clabernetes lays out this link? Well, clabernetes takes the original link definition as provided by a user and transforms it into a set of point-to-point VXLAN tunnels⁴ that stitch the nodes together.

Two nodes appear to be connected to each other as if they were connected with a veth pair. We can check that LLDP neighbors are discovered on either other side of the link:

NS=c9s-vlan POD=srl1; \
kubectl -n $NS exec -it \
  $(kubectl -n $NS get pods | grep ^$POD | awk '{print $1}') -- \
    docker exec $POD sr_cli show system lldp neighbor

+---------------+-------------------+----------------------+---------------------+------------------------+----------------------+---------------+
|     Name      |     Neighbor      | Neighbor System Name | Neighbor Chassis ID | Neighbor First Message | Neighbor Last Update | Neighbor Port |
+===============+===================+======================+=====================+========================+======================+===============+
| ethernet-1/10 | 1A:C5:00:FF:00:00 | srl2                 | 1A:C5:00:FF:00:00   | an hour ago            | now                  | ethernet-1/10 |
+---------------+-------------------+----------------------+---------------------+------------------------+----------------------+---------------+

We can also make sure that our startup-configuration that was provided in external files in original topology is applied in good order and we can perform the ping between two clients

fancy commandregular command

NS=c9s-vlan POD=client1; \
kubectl -n $NS exec -it \
  $(kubectl -n $NS get pods | grep ^$POD | awk '{print $1}') -- \
    docker exec -it $POD ping -c 2 10.1.0.2

kubectl exec -it -n c9s-vlan pod/vlan-client1-699dbcfd8b-r2fgc -- \
docker exec -it client1 ping -c 2 10.1.0.2

PING 10.1.0.2 (10.1.0.2) 56(84) bytes of data.
64 bytes from 10.1.0.2: icmp_seq=1 ttl=64 time=2.08 ms
64 bytes from 10.1.0.2: icmp_seq=2 ttl=64 time=1.04 ms

--- 10.1.0.2 ping statistics ---
2 packets transmitted, 2 received, 0% packet loss, time 1001ms
rtt min/avg/max/mdev = 1.040/1.557/2.075/0.517 ms

With the command above we:

1. connected to the client1 pod that runs the client1 lab node
2. executed ping command inside the client1 node to ping the client2 node
3. Ensured that the datapath stitching is working as expected

VM-based nodes?

In this quickstart we used native containerized Network OS - SR Linux - as it is lightweight and publicly available. But what if you want to use a VM-based Network OS like Nokia SR OS, Cisco IOS-XRv or Juniper vMX? Can you do that with clabernetes?

Short answer is yes. Clabernetes should be able to run VM-based nodes as well, but your cluster nodes must support nested virtualization, same as you would need to run VM-based nodes in containerlab.

Also you need to ensure that your VM-based container image is accessible to your cluster nodes, either via a public registry or a private one.

When these considerations are taken care of, you can use the same topology file as you would use with containerlab. The only difference is that you need to specify the image in the topology file as a fully qualified image name, including the registry name.

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1. In general there are no requirements for clabernetes from a kubernetes cluster perspective, however, many device types may have requirements for nested virtualization or specific CPU flags that your nodes would need to support in order to run the device. ↩

2. They may run on the same node, this is up to the kubernetes scheduler whose job it is to schedule pods on the nodes it deems most appropriate. ↩

3. Default exposed ports can be overwritten by a user via Topology CR. ↩

4. Using containerlab's vxlan tunneling workflow to create tunnels. ↩

5. The namespace name is derived from the name of the lab in the .clab.yml file. ↩

6. VXLAN services are used for datapath stitching and are not meant to be accessed from outside the cluster. ↩