hf-cloud-serving-image-selection Skill

Source

• Creator: huggingface
• Repository: huggingface/skills
• Skill file: skills/hf-cloud-serving-image-selection/SKILL.md

What it does

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "d

Skill instructions

Serving Image Selection The serving container is the single thing most likely to break a SageMaker deployment that "looked correct on paper". Wrong container, stale tag, or the wrong AMI — all produce the same opaque Failed to pass health check error. Rule zero: HuggingFace images always win When both a HuggingFace-curated family (huggingface-vllm, huggingface-vllm-omni, huggingface-sglang, tei, huggingface-pytorch-inference) and a generic family (vllm, vllm-omni, sglang, djl-inference) can serve the model, the HuggingFace one is mandatory, not preferred. The only valid reasons to use a generic image: 1. Verified incompatibility — the model needs an architecture/modality/feature no available HuggingFace tag supports, confirmed against the catalog (not assumed). 2. No HuggingFace tag exists in the target region and mirroring is not an option. 3. The HuggingFace image is in "Known-broken images" below. A newer version number on the generic repo is not a reason. The AWS vllm repo often pu