haipipe-end-deploy-sagemaker

name	haipipe-end-deploy-sagemaker
description	AWS SageMaker deploy specialist for haipipe-end: wraps an Endpoint_Set into SageMaker model.tar.gz, deploys to a SageMaker endpoint, runs live smoke tests, monitors logs, tears down. Reads (never modifies) Endpoint_Sets from haipipe-end-endpointset. Read the SageMaker pitfalls in ../haipipe-end-develop-sagemaker/SKILL.md before any live action. Called by /haipipe-end when deploy target is sagemaker.
argument-hint	[function] [endpoint_set_or_id] [args...]
allowed-tools	Bash, Read, Write, Edit, Grep, Glob
metadata	{"version":"0.1.2","last_updated":"2026-07-08","summary":"AWS SageMaker deploy specialist for haipipe-end."}

Skill: haipipe-end-deploy-sagemaker

AWS SageMaker deployment specialist. Consumes an Endpoint_Set built by haipipe-end-endpointset, packages it for SageMaker, deploys, tests live, monitors, and cleans up.

Function axis: dashboard | deploy | test | monitor | teardown | review

---

Commands

/haipipe-end-deploy-sagemaker                              -> dashboard: SageMaker endpoints + cost
/haipipe-end-deploy-sagemaker dashboard                    -> same
/haipipe-end-deploy-sagemaker deploy <endpoint_set>        -> push Endpoint_Set to SageMaker
/haipipe-end-deploy-sagemaker test <endpoint_id>           -> hit live SageMaker endpoint
/haipipe-end-deploy-sagemaker monitor <endpoint_id>        -> CloudWatch logs + invocation metrics
/haipipe-end-deploy-sagemaker teardown <endpoint_id>       -> stop endpoint, optional model cleanup
/haipipe-end-deploy-sagemaker review <endpoint_id>         -> audit deploy config (IAM, instance, scaling)

---

Dispatch Table

Invocation     Ref file(s)                              Function block
-------------- ---------------------------------------- -----------------------------------
dashboard      ../haipipe-end/ref/deploy-overview.md                          dashboard procedure (in this SKILL.md)
deploy         ../haipipe-end/ref/deploy-overview.md +
               ../haipipe-end/ref/
                 0-overview.md                          deploy procedure
test           ../haipipe-end/ref/deploy-overview.md                          test procedure
monitor        ../haipipe-end/ref/deploy-overview.md                          monitor procedure
teardown       ../haipipe-end/ref/deploy-overview.md                          teardown procedure
review         ../haipipe-end/ref/deploy-overview.md                          review procedure

The deploy step reads the endpointset overview to know the Endpoint_Set layout it consumes.

---

Step-by-Step Protocol

Step 0: Read ../haipipe-end/ref/deploy-overview.md for SageMaker-specific conventions (instance types, IAM roles, model.tar.gz layout, autoscaling).

Step 1: Parse args. Function vocabulary above. Required arg per function: deploy: <endpoint_set_name> (path under 6-EndpointStore/) test/monitor/teardown/review: <sagemaker_endpoint_id>

Step 2: Verify AWS context: - AWS credentials available (env or aws configure) - Region set - IAM role for SageMaker execution exists or can be created

Step 3: Execute the function (see procedures below).

Step 4: Emit the structured tail:

status:    ok | blocked | failed
summary:   2-3 sentences on the deploy / test / etc.
artifacts: [SageMaker endpoint id, ARN, model package path on S3]
next:      suggested next command

---

Procedures (placeholder — fill from project's actual SageMaker conventions)

Deploy:

1. Read Endpoint_Set at _WorkSpace/6-EndpointStore/<endpoint_set>/. (input contract, all deploy skills: canonical input = the folder; a .tar.gz twin is a wire form only)
2. Build model.tar.gz containing fn_endpoint/ + ModelInstance + an (logical bundle name; physically materialized as code/ + model/ in the set) inference.py entry point conforming to SageMaker's contract.
3. Upload to S3 (project-configured bucket).
4. Register a SageMaker Model pointing at the S3 artifact + execution role.
5. Create EndpointConfig (instance type, autoscaling).
6. Create Endpoint and wait for InService.
7. Run a 1-payload smoke invocation; verify response.
8. Record endpoint_id + ARN in the project's deploy log.

Test, Monitor, Teardown, Review: See ../haipipe-end/ref/deploy-overview.md for the SageMaker-specific procedures and the aws sagemaker / aws cloudwatch CLI invocations the project uses.

---

Target Scope

Owns:

• model.tar.gz packaging conforming to SageMaker's inference container contract
• SageMaker endpoint config (instance type, count, autoscaling)
• IAM role assumptions
• aws sagemaker CLI invocation
• CloudWatch log queries
• Live invocation smoke tests against the deployed endpoint

Does NOT own:

• Endpoint_Set content (read-only input from /haipipe-end-endpointset)
• ModelInstance training (/haipipe-nn)

If a deploy fails because of an Endpoint_Set issue, escalate to /haipipe-end-endpointset review rather than patching here.