panxiao81
88a02ababa
Reorganize the brownfield repository, remove retired and generated artifacts, harden ignore rules, and record the GitOps/IaC redesign.
36 lines
1.2 KiB
Markdown
36 lines
1.2 KiB
Markdown
# Marker (GPU service)
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**Goal**
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- Run `marker` locally on the NVIDIA GPU and expose its API in Kubernetes.
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- Keep the pod single-replica and single-worker so it fits in 4 GB VRAM.
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**Resources**
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| File | Description |
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| --- | --- |
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| `Dockerfile` | CUDA-based image built from `pytorch/pytorch` and `marker-pdf`. |
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| `deployment.yaml` | Single GPU-backed `Deployment` for `marker_server`. |
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| `service.yaml` | ClusterIP service on port 8001. |
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**How to use**
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1. Build and push the image:
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```bash
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docker build -t <your-registry>/marker:latest ~/services/apps/marker
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docker push <your-registry>/marker:latest
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```
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2. Update `deployment.yaml` with that image tag.
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3. Apply the manifests:
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```bash
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kubectl apply -f ~/services/apps/marker/deployment.yaml
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kubectl apply -f ~/services/apps/marker/service.yaml
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```
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4. Check the pod is using the GPU:
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```bash
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kubectl logs deploy/marker
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kubectl exec -it deploy/marker -- nvidia-smi
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```
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**Notes**
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- No PVC is used; the container only needs ephemeral storage.
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- Keep `replicas: 1` and avoid concurrent jobs on this 4 GB card.
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- 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`.
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