hf-cloud-sagemaker-production-defaultsPurpose, usage and source

What this Skill does

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.

How the upstream workflow works

python scripts/deploy.py \
    --model-name qwen3-medical \
    --image-uri "$IMAGE_URI" \
    --inference-ami-version "$AMI" \
    --role-arn "$ROLE_ARN" \
    --model-s3-uri "s3://<your-model-bucket>/qwen3-medical/model.tar.gz" \
    --instance-type ml.g5.xlarge \
    --region "$REGION" \
    --env SM_VLLM_MODEL=/opt/ml/model \
    --env SM_VLLM_HOST=0.0.0.0 \
    --env SM_VLLM_TRUST_REMOTE_CODE=false \
    --env SM_VLLM_MAX_MODEL_LEN=4096

Requirements and setup

No short requirements passage was selected from this file. This does not mean the Skill has no dependencies. Check the repository setup instructions and referenced files.

Limits and quality checks

No separate limits passage was selected from this file. This does not mean the Skill is safe for every input or environment. Review the complete source and validate your own result.

Start with a safe trial

1. Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
2. Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
3. Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
4. Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.

Skill format and directory specification ↗

Read further in the source

• SageMaker Production Defaults
• What gets created
• Running the deployment
• InService is not success — smoke-test before declaring victory
• Testing a real-time endpoint
• The UTF-8 BOM gotcha (Windows)
• Invoking a generative reranker (vLLM)
• Picking the image URI
• Scale to zero for real-time endpoints
• How it differs from deploy.py
• Measured behaviour
• Sizing the component
• Invoking and testing
• Teardown order
• Async inference deployments
• How scale-to-zero works
• Async alarms
• Invoking async endpoints
• Defaults at a glance
• Autoscaling target — tune by model type
• Data capture + IAM gotcha
• Teardown
• When the deployment fails

Related Skills

• hf-cloud-aws-context-discoveryhuggingface/skills
• hf-cloud-python-env-setuphuggingface/skills
• hf-cloud-sagemaker-deployment-plannerhuggingface/skills
• hf-cloud-sagemaker-iam-preflighthuggingface/skills

Translation and scope

This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.

This introduction refers to the pinned version below; current repository activity and collection time are shown separately.

Pinned source

Read the original ↗

Source retrieved

2026-10-29T09:32:23Z

Source commit

fabc634a893f62ad5c2039830397efa35fa62715

Source SHA-256

6a8c1aff465e548ac4476d56831f864621af83b6a0f54544637601f3ecc44f9e