Establish clean homelab infrastructure baseline

Reorganize the brownfield repository, remove retired and generated artifacts, harden ignore rules, and record the GitOps/IaC redesign.

apps/marker/Dockerfile

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+FROM pytorch/pytorch:2.5.1-cuda12.1-cudnn9-runtime
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PIP_NO_CACHE_DIR=1 \
+    PYTHONUNBUFFERED=1 \
+    TORCH_DEVICE=cuda
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    poppler-utils \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip && \
+    pip install marker-pdf fastapi uvicorn python-multipart
+
+EXPOSE 8001
+
+CMD ["marker_server", "--port", "8001"]

apps/marker/README.md

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+# Marker (GPU service)
+
+**Goal**
+- Run `marker` locally on the NVIDIA GPU and expose its API in Kubernetes.
+- Keep the pod single-replica and single-worker so it fits in 4 GB VRAM.
+
+**Resources**
+| File | Description |
+| --- | --- |
+| `Dockerfile` | CUDA-based image built from `pytorch/pytorch` and `marker-pdf`. |
+| `deployment.yaml` | Single GPU-backed `Deployment` for `marker_server`. |
+| `service.yaml` | ClusterIP service on port 8001. |
+
+**How to use**
+1. Build and push the image:
+   ```bash
+   docker build -t <your-registry>/marker:latest ~/services/apps/marker
+   docker push <your-registry>/marker:latest
+   ```
+2. Update `deployment.yaml` with that image tag.
+3. Apply the manifests:
+   ```bash
+   kubectl apply -f ~/services/apps/marker/deployment.yaml
+   kubectl apply -f ~/services/apps/marker/service.yaml
+   ```
+4. Check the pod is using the GPU:
+   ```bash
+   kubectl logs deploy/marker
+   kubectl exec -it deploy/marker -- nvidia-smi
+   ```
+
+**Notes**
+- No PVC is used; the container only needs ephemeral storage.
+- Keep `replicas: 1` and avoid concurrent jobs on this 4 GB card.
+- If the server needs an explicit bind address in your build, change the container args to `0.0.0.0:8001` equivalent for `marker_server`.

apps/marker/deployment.yaml

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+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: marker
+  namespace: default
+  labels:
+    app: marker
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: marker
+  template:
+    metadata:
+      labels:
+        app: marker
+    spec:
+      containers:
+        - name: marker
+          image: <your-registry>/marker:latest
+          imagePullPolicy: IfNotPresent
+          command:
+            - marker_server
+          args:
+            - --port
+            - "8001"
+          env:
+            - name: TORCH_DEVICE
+              value: cuda
+            - name: PYTHONUNBUFFERED
+              value: "1"
+          ports:
+            - containerPort: 8001
+          resources:
+            requests:
+              cpu: "1"
+              memory: 2Gi
+              nvidia.com/gpu: "1"
+            limits:
+              cpu: "2"
+              memory: 4Gi
+              nvidia.com/gpu: "1"
+          readinessProbe:
+            tcpSocket:
+              port: 8001
+            initialDelaySeconds: 10
+            periodSeconds: 10
+          livenessProbe:
+            tcpSocket:
+              port: 8001
+            initialDelaySeconds: 30
+            periodSeconds: 20

apps/marker/service.yaml

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+apiVersion: v1
+kind: Service
+metadata:
+  name: marker
+  namespace: default
+spec:
+  selector:
+    app: marker
+  ports:
+    - name: http
+      port: 8001
+      targetPort: 8001