deploy-model

DEPLOY MODEL — for hardware sizing or adding a new model to the catalog that fits an existing runtime pattern. ## Scripts Every capability below is a standalone script under `scripts/`, run via the shell tool as `python3 "$SKILLS_ROOT/deploy-model/scripts/<name>.py" <flags>`. | Script | Cluster access | Purpose | | --- | --- | --- | | `list_cluster_gpus.py` | Yes (read-only) | Real GPU capacity by product type | | `estimate_model_footprint.py` | No (HF Hub only) | VRAM sizing + tensor_parallel_size recommendation | | `generate_model_manifests.py` | No (pure text generation) | Draft the full Kustomize/KServe file set | In order: 1. Run `list_cluster_gpus.py` to see real GPU capacity — never guess it. 2. Run `estimate_model_footprint.py --hf-repo-id <id>` with the target Hugging Face repo id to get a real recommended tensor_parallel_size — never guess that either. 3. Only then run `generate_model_manifests.py` using the values from steps 1-2. It only knows two runtime templates: stock vLLM (`--output-kind chat`) and vLLM-Omni (`--output-kind image`/`video`). If the target model's real serving mechanism is neither of those — e.g. it needs its own native server/CLI — stop here and use the new-model-runtime skill instead; forcing an unfamiliar runtime through these templates produces a broken deployment. 4. `generate_model_manifests.py` always returns the full file set for the model directory in one call — pvc.yaml, model-download-job.yaml, servingruntime.yaml, inferenceservice.yaml, httpscaledobject.yaml, kustomization.yaml, AND the four MaaS catalog-registration files (external-model.yaml, model-ref.yaml, subscription.yaml, auth-policy.yaml) — there's no flag to get just the InferenceService half. Return all of it to the user verbatim in fenced code blocks, one per file — do not paraphrase, shorten, summarize, or drop the MaaS files as boilerplate. 5. Tell the user this is a draft only: this platform uses GitOps (ArgoCD self-heal + prune), so nothing is actually deployed until a human saves ALL of the generated files, wires them into an overlay, and merges a PR. Nothing in this skill can deploy a model itself — `generate_model_manifests.py` only prints text, it never touches the cluster or git.