# hf-cloud-sagemaker-production-defaults

This skill creates SageMaker endpoints with autoscaling, CloudWatch alarms, and tags. Use it for the final model deployment step.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 549 installs · ★ 11213
- Author: huggingface
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/huggingface/skills/tree/HEAD/skills/hf-cloud-sagemaker-production-defaults/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/hf-cloud-sagemaker-production-defaults

## What it does

The skill deploys SageMaker endpoints for real-time, real-time scale-to-zero, or async inference. It creates the model, endpoint configuration, and endpoint, then adds target tracking based on invocations per instance, CloudWatch alarms, and a consistent tag set. `deploy.py` handles real-time endpoints, `deploy_ic.py` handles inference components, and `deploy_async.py` handles async endpoints.

The deployment needs an image URI, IAM role, and region; vLLM also needs an inference AMI version. For a production model package, pass a pinned `model.tar.gz` artifact from S3 with `--model-s3-uri`; TEI deployments use `HF_MODEL_ID` instead. Data capture is off by default and can be enabled with `--enable-data-capture`.

The scripts wait for `InService`, surface failure reasons, and print JSON resource details. Run a real smoke test and inspect endpoint logs afterward, because `InService` alone does not prove that inference works. Check the diagnostic IAM permissions first if the workflow needs to read those logs.

## Requirements
- Works with: AWS

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add huggingface/skills --skill hf-cloud-sagemaker-production-defaults -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/hf-cloud-sagemaker-production-defaults/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/hf-cloud-sagemaker-production-defaults/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add huggingface/skills --skill hf-cloud-sagemaker-production-defaults -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/hf-cloud-sagemaker-production-defaults/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add huggingface/skills --skill hf-cloud-sagemaker-production-defaults -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/hf-cloud-sagemaker-production-defaults/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install huggingface/skills hf-cloud-sagemaker-production-defaults
```

2. **Manually: put the files in place**
- File: `.github/skills/hf-cloud-sagemaker-production-defaults/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add huggingface/skills --skill hf-cloud-sagemaker-production-defaults -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/huggingface/skills --path skills/hf-cloud-sagemaker-production-defaults
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/hf-cloud-sagemaker-production-defaults/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add huggingface/skills --skill hf-cloud-sagemaker-production-defaults -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/huggingface/skills/tree/HEAD/skills/hf-cloud-sagemaker-production-defaults/SKILL.md

## When to use it
- create a SageMaker endpoint for real-time inference
- configure scale-to-zero with inference components
- add invocation-based autoscaling
- set CloudWatch alarms for endpoint failures
- tag resources for later cleanup

## Example prompts
- Create a SageMaker endpoint for the `model.tar.gz` artifact with `deploy.py`, autoscaling, and CloudWatch alarms.
- Deploy the vLLM model with `deploy.py` using `--image-uri`, `--inference-ami-version`, `--role-arn`, `--model-s3-uri`, and `--region`.
- Validate the endpoint with a smoke test and scan its logs for model-load errors or worker crashes.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: a83469a95c48dd5e.

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```text
---
name: hf-cloud-sagemaker-production-defaults
description: 'Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.'
---

# SageMaker Production Defaults

The difference between a demo endpoint and one you can leave running is: it scales with traffic, it tells you when it breaks, and you can debug it later. This skill makes those three the default rather than optional extras.

By the time this skill runs, the planner has chosen a real-time endpoint, IAM has a usable role, and image-selection has resolved a container URI + AMI version. This skill turns those into an actual deployment.

## What gets created

For every endpoint, the skill creates these as a unit:

1. **SageMaker Model** — image + env vars + execution role + S3 artifacts
2. **Endpoint config** — instance type, initial count, optional data capture
3. **Endpoint** — the real-time endpoint serving inference
4. **Autoscaling target + policy** — target tracking on invocations per instance
5. **CloudWatch alarms** — latency, errors, platform overhead

An inference-component deployment (`deploy_ic.py`) creates the same set with two changes: the endpoint config carries the execution role and `ManagedInstanceScaling`, and an **inference component** carries the model. Its autoscaling target is the component, not the variant.

Data capture (logging requests/responses to S3) is **off by default** — useful for debugging but creates ongoing S3 costs the user didn't necessarily ask for. Enable with `--enable-data-capture`.
```
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