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kind + FreeLens on a VM (GHCR)

This guide documents a VM-friendly workflow for running PyPNM on kind and managing it in FreeLens. The model below assumes one PyPNM service per CMTS, with multiple namespaces living on a single VM/cluster.

Prerequisites

  • VM with Docker installed and running.
  • curl and sudo available.
  • Network access to GHCR (ghcr.io/PyPNMApps/pypnm).

Install kubectl + kind

Use the bootstrap helper for Debian/Ubuntu-style hosts (no repo clone required):

curl -fsSL https://raw.githubusercontent.com/PyPNMApps/PyPNM/main/tools/k8s/pypnm_kind_vm_bootstrap.sh \
  -o /tmp/pypnm_kind_vm_bootstrap.sh
bash /tmp/pypnm_kind_vm_bootstrap.sh

Create the cluster and deploy from GHCR

Pick a release tag, then deploy into a namespace (one namespace per CMTS). This script pulls manifests from GitHub, so no repo clone is required:

TAG="v1.6.4.0"
NAMESPACE="pypnm-cmts-a"

curl -fsSL https://raw.githubusercontent.com/PyPNMApps/PyPNM/main/tools/k8s/pypnm_k8s_remote_deploy.sh \
  -o /tmp/pypnm_k8s_remote_deploy.sh
bash /tmp/pypnm_k8s_remote_deploy.sh --create --tag "${TAG}" --namespace "${NAMESPACE}" --replicas 1

Health check:

kubectl port-forward --namespace "${NAMESPACE}" deploy/pypnm-api 8000:8000
curl -i http://127.0.0.1:8000/health

Example response:

{
  "status": "ok",
  "service": {
    "name": "pypnm-docsis",
    "version": "1.4.3.0"
  },
  "uptime": {
    "starttime": 1773640097,
    "uptime": 1
  },
  "memory": {
    "rss_bytes": 12582912,
    "total_bytes": 17179869184,
    "free_bytes": 8216707072,
    "available_bytes": 10379091968,
    "usage_percent": 0.07
  },
  "data": {
    "path": ".data",
    "size_bytes": 1761579619,
    "directories": {
      "json": 1728244816,
      "xlsx": 0,
      "pnm": 17349665,
      "csv": 2819493,
      "png": 2805111,
      "db": 3388387,
      "archive": 6968882,
      "msg_rsp": 3265
    }
  }
}

memory.rss_bytes reports the current resident memory used by the running PyPNM process in bytes. memory.total_bytes reports host total memory in bytes. memory.free_bytes reports host free memory in bytes. memory.available_bytes reports host available memory in bytes. memory.usage_percent reports resident process memory as a percent of host total memory.

If the returned service.version is older than expected, verify the tag used in the deploy command and confirm the namespace matches the running deployment.

Connect with FreeLens

  1. Add the kubeconfig from the VM to FreeLens.

    • If FreeLens runs on the VM, it can use ~/.kube/config directly.

    • If FreeLens runs on your workstation, copy the kubeconfig from the VM:

      VM_USER="dev01"
VM_HOST="10.0.0.10"
LOCAL_KUBECONFIG="${HOME}/.kube/pypnm-dev-kubeconfig"

scp ${VM_USER}@${VM_HOST}:~/.kube/config "${LOCAL_KUBECONFIG}"

      PowerShell:

      $VM_USER = "dev01"
$VM_HOST = "10.0.0.10"
$LOCAL_KUBECONFIG_DIR = "$env:USERPROFILE\\.kube"
$LOCAL_KUBECONFIG = "$LOCAL_KUBECONFIG_DIR\\pypnm-dev-kubeconfig"

New-Item -ItemType Directory -Force -Path $LOCAL_KUBECONFIG_DIR | Out-Null
scp ${VM_USER}@${VM_HOST}:~/.kube/config "$LOCAL_KUBECONFIG"

    • In FreeLens: Catalog → Clusters → Add Cluster → From kubeconfig, then select the copied file.

    • If FreeLens runs on Windows, update any file paths inside the kubeconfig (for example, replace /home/dev01/... with C:\\Users\\<you>\\...). Then use an SSH tunnel for the API port:

      VM_USER="dev01"
VM_HOST="10.0.0.10"
VM_PORT="8000"

ssh -L ${VM_PORT}:127.0.0.1:${VM_PORT} ${VM_USER}@${VM_HOST}

      PowerShell:

      $VM_USER = "dev01"
$VM_HOST = "10.0.0.10"
$VM_PORT = "8000"

ssh -L ${VM_PORT}:127.0.0.1:${VM_PORT} ${VM_USER}@${VM_HOST}
  2. Select the target namespace (pypnm-cmts-a).
    • In FreeLens 1.7.0: open Catalog (top-left icon), choose Clusters, then switch the namespace selector in the top bar to pypnm-cmts-a.
  3. In FreeLens, open the target namespace and locate pypnm-api (Service or Pod).
  4. Start a port-forward for pypnm-api to local port 8000.
    • Service: forward 8000 -> 8000.
    • Pod: forward 8000 -> 8000.
  5. Open http://127.0.0.1:8000/health in your browser.
    • If you are tunneling, keep the SSH tunnel running and use the same http://127.0.0.1:8000 URL on Windows.

Tip: keep one FreeLens workspace per CMTS namespace so it is easy to keep sessions isolated.

Deployment examples

Example When to use
Single namespace with 10 replicas Load testing or simple HA with one endpoint.
10 namespaces, 10 ports One instance per CMTS or per customer.
Multiple kind clusters Hard isolation between environments.

One VM, multiple CMTS (one PyPNM per CMTS)

Deploy a PyPNM instance per CMTS by assigning a unique namespace and configuration per CMTS:

TAG="v1.6.4.0"

for CMTS in cmts-a cmts-b cmts-c; do
  NAMESPACE="pypnm-${CMTS}"
  bash /tmp/pypnm_k8s_remote_deploy.sh --create --tag "${TAG}" --namespace "${NAMESPACE}" --replicas 1
done

CMTS-specific config

Each instance should point at exactly one CMTS. Use a per-namespace config patch and apply it as described in PyPNM on Kubernetes (kind) ("Config overrides" section). Keep the CMTS routing and retrieval settings unique per namespace.

Teardown

NAMESPACE="pypnm-cmts-a"

bash /tmp/pypnm_k8s_remote_deploy.sh --teardown --namespace "${NAMESPACE}"

To delete the cluster after all namespaces are removed:

kind delete cluster --name pypnm-dev