feat: add sagemaker-ai knowledge base power

README.md

@@ -42,6 +42,13 @@ Documentation is available at https://kiro.dev/docs/powers/
 
 ---
 
+### sagemaker-ai
+**Amazon SageMaker AI** - Deploy and train ML models on Amazon SageMaker AI — inference endpoints, LLM fine-tuning, HyperPod clusters, Model Monitor, AutoML with AutoGluon, and SageMaker Python SDK v3 patterns.
+
+**MCP Servers:** awslabs.sagemaker-ai-mcp-server
+
+---
+
 ### cloud-architect
 **Build infrastructure on AWS** - Build AWS infrastructure with CDK in Python following AWS Well-Architected framework best practices.
 

sagemaker-ai/POWER.md

@@ -0,0 +1,170 @@
+---
+name: "sagemaker-ai"
+displayName: "Amazon SageMaker AI"
+description: "Deploy and train ML models on Amazon SageMaker AI — inference endpoints, LLM fine-tuning, HyperPod clusters, Model Monitor, AutoML with AutoGluon, and SageMaker Python SDK v3 patterns."
+keywords: ["sagemaker", "inference", "training", "hyperpod", "model-monitor", "autogluon", "sdk-v3"]
+author: "AWS"
+---
+
+# When to use this power
+
+When you want to deploy ML models to SageMaker inference endpoints, fine-tune or train models (serverless, Training Jobs, or HyperPod), set up model monitoring, run AutoML with AutoGluon, or write correct SageMaker Python SDK v3 code.
+
+# When to Load Steering Files
+
+Whenever you are asked to perform a task related to any of the following scenarios - ensure you load and read the appropriate markdown file mentioned
+
+- Deploying models to SageMaker real-time endpoints (container selection, DJL LMI/vLLM, DLC configuration, multimodal models) -> use `./steering/inference-endpoints.md`
+- Fine-tuning or training models (serverless customization, QLoRA/LoRA, GPU vs Trainium, instance sizing) -> use `./steering/training-jobs.md`
+- Setting up or managing HyperPod clusters for training (EKS orchestration, Training Operator, Task Governance, resiliency) -> use `./steering/hyperpod.md` — also use MCP tools `manage_hyperpod_stacks` and `manage_hyperpod_cluster_nodes` for cluster operations
+- Deploying inference on HyperPod clusters (JumpStart models, custom models from S3/FSx, kubectl CRDs, autoscaling) -> use `./steering/hyperpod-inference.md`
+- Setting up model monitoring (Data Quality, Model Quality, Bias, Explainability, baselines, schedules) -> use `./steering/model-monitor.md`
+- Using AutoGluon for AutoML (tabular, time series, multimodal, SageMaker Pipelines) -> use `./steering/automl-autogluon.md`
+- Writing SageMaker SDK v3 code (correct imports, image_uris, deployment patterns, invocation) -> use `./steering/sdk-v3-reference.md`
+
+# Available MCP Tools
+
+This power integrates with the [Amazon SageMaker AI MCP Server](https://github.com/awslabs/mcp/tree/main/src/sagemaker-ai-mcp-server) (Apache-2.0 license) for HyperPod cluster management.
+
+## manage_hyperpod_stacks
+
+Orchestrates HyperPod cluster infrastructure via CloudFormation.
+
+- `operation="describe"` — Describe existing HyperPod CloudFormation stacks (read-only)
+- `operation="deploy"` — Deploy a new HyperPod cluster via managed CloudFormation templates (requires `--allow-write`)
+- `operation="delete"` — Delete a HyperPod CloudFormation stack and its resources (requires `--allow-write`)
+
+Parameters: `operation`, `stack_name`, `region_name`, `profile_name`, `params_file` (for deploy)
+
+**Safety**: Only modifies/deletes stacks originally created by this tool.
+
+## manage_hyperpod_cluster_nodes
+
+Manages cluster nodes within HyperPod deployments.
+
+- `operation="list_clusters"` — List HyperPod clusters with filtering by name, creation time, training plan ARN
+- `operation="list_nodes"` — List nodes in a cluster with filtering by instance group
+- `operation="describe_node"` — Get detailed info about a specific node
+- `operation="update_software"` — Update AMIs for all nodes or specific instance groups (requires `--allow-write`)
+- `operation="batch_delete"` — Delete multiple nodes in a single operation (requires `--allow-write`)
+
+Parameters: `operation`, `cluster_name`, `node_id` (for describe_node), `node_ids` (for batch_delete)
+
+# Onboarding
+
+1. **Ensure the user has valid AWS Credentials** These are used to interact with SageMaker and related AWS services.
+2. **Verify AWS CLI access** Using `aws sts get-caller-identity`
+3. **Check Python and SDK version** SageMaker Python SDK v3 requires Python <= 3.13. Install with `pip install 'sagemaker>=3'`
+4. **Execution role** In SageMaker Studio, always use `get_execution_role()`. Outside Studio, ensure an IAM role with SageMaker permissions is available.
+5. **MCP server dependencies** The SageMaker AI MCP server requires [`uv`](https://docs.astral.sh/uv/getting-started/installation/) (for `uvx` command)
+
+# SDK v3 Guardrails (Always-On)
+
+These rules apply to EVERY piece of SageMaker code. Check each one before generating output.
+
+| Rule | CORRECT | WRONG |
+|------|---------|-------|
+| Model class import | `from sagemaker.core.resources import Model` | `from sagemaker.model import Model` |
+| Session import | `from sagemaker.core.helper.session_helper import Session, get_execution_role` | `from sagemaker import get_execution_role` |
+| Deployment (LLMs) | Core API: `Model.create` + `EndpointConfig.create` + `Endpoint.create` | `ModelBuilder` with DJL/vLLM containers |
+| Deployment (simple) | `ModelBuilder` + `SchemaBuilder` | V2 `Model.deploy()` |
+| JumpStart deploy | `ModelBuilder(model="<model-id>")` | `JumpStartModel` (removed in V3) |
+| Processing imports | `from sagemaker.core.processing import ...` | `from sagemaker.processing import ...` |
+| Transformer import | `from sagemaker.core.transformer import Transformer` | `from sagemaker.transformer import Transformer` |
+| Pipeline imports | `sagemaker.mlops.workflow.*` + `sagemaker.core.workflow.*` | `sagemaker.workflow.*` (V2, removed) |
+| Python version | <= 3.13 | 3.14+ (SDK v3 incompatible) |
+| DLC images source | `https://aws.github.io/deep-learning-containers/reference/available_images/` | HuggingFace docs (outdated) |
+
+# Quick Reference
+
+## SDK v3 Core API (Recommended for DJL/vLLM)
+
+```python
+from sagemaker.core.resources import Model, EndpointConfig, Endpoint
+from sagemaker.core.shapes.shapes import ContainerDefinition, ProductionVariant
+```
+
+## SDK v3 ModelBuilder (For JumpStart / simple HF models)
+
+```python
+from sagemaker.serve import ModelBuilder
+from sagemaker.serve.builder.schema_builder import SchemaBuilder
+```
+
+## Container Decision Tree
+
+1. Standard text LLM (Llama, Mistral, Qwen) -> DJL LMI with vLLM backend
+2. Multimodal/Vision model (Idefics3, LLaVA, Qwen-VL) -> DJL LMI with vLLM backend
+3. Simple HF pipeline model (classification, NER) -> HuggingFace Inference DLC
+4. Custom model with custom handler -> HuggingFace Inference DLC + custom inference.py in model.tar.gz
+
+## CUDA Compatibility
+
+| Instance Family | GPU | Max CUDA | Recommended DLC |
+|---|---|---|---|
+| ml.g5.* | A10G (24GB) | cu128 | `djl-inference:0.36.0-lmi20.0.0-cu128` |
+| ml.g6.* | L4 (24GB) | cu129 | `djl-inference:0.36.0-lmi22.0.0-cu129` |
+| ml.p5.* | H100 (80GB) | cu129 | `djl-inference:0.36.0-lmi22.0.0-cu129` |
+
+Note: cu129 fails on g5 due to driver version mismatch, not CUDA compute capability. Always check the DLC images page for the latest versions — the versions above are examples.
+
+## Reference Repositories
+
+- Inference hosting examples (Llama, Mistral, Mixtral, Falcon, CodeLlama): `https://github.com/aws-samples/sagemaker-genai-hosting-examples`
+- Model customization recipes (QLoRA, Spectrum, Full FT, DPO, GRPO): `https://github.com/aws-samples/amazon-sagemaker-generativeai/tree/main/0_model_customization_recipes`
+
+## Latest DLC Images
+
+Always check the latest images at:
+`https://aws.github.io/deep-learning-containers/reference/available_images/`
+
+Do NOT rely on the HuggingFace docs page — it is outdated.
+
+# Best Practices
+
+- Always use `get_execution_role()` in SageMaker Studio — don't hardcode role ARNs
+- Set `ContainerStartupHealthCheckTimeoutInSeconds=900` for models that download from HuggingFace Hub at startup
+- Clean up endpoints after testing — they cost money even when idle
+- Use `dependencies={"auto": False}` with ModelBuilder to avoid capturing local packages that break the DLC
+- For first invocation after deploy, add retry logic or a warm-up wait — models load lazily on first request
+
+# Troubleshooting
+
+## SDK v3 Import Errors
+
+**Error**: `ImportError: cannot import name 'Model' from 'sagemaker.model'`
+**Cause**: Using SDK v2 import paths
+**Solution**: Use `from sagemaker.core.resources import Model` — see SDK v3 Guardrails table above
+
+## Endpoint Creation Timeout
+
+**Error**: Endpoint stays in `Creating` status and eventually fails
+**Cause**: Model too large for instance, or container startup timeout too low
+**Solution**:
+1. Check model memory requirements against instance GPU memory
+2. Set `ContainerStartupHealthCheckTimeoutInSeconds=900` (or higher for very large models)
+3. Verify CUDA compatibility — see CUDA Compatibility table
+
+## ModelBuilder Issues
+
+**Error**: `ModelBuilder.build()` fails with dependency errors
+**Cause**: Local packages captured by auto-dependency detection
+**Solution**: Use `dependencies={"auto": False}` and manage dependencies explicitly
+
+# License
+
+```
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+```

sagemaker-ai/mcp.json

@@ -0,0 +1,17 @@
+{
+  "mcpServers": {
+    "awslabs.sagemaker-ai-mcp-server": {
+      "command": "uvx",
+      "args": [
+        "awslabs.sagemaker-ai-mcp-server@latest",
+        "--allow-write",
+        "--allow-sensitive-data-access"
+      ],
+      "env": {
+        "FASTMCP_LOG_LEVEL": "ERROR"
+      },
+      "autoApprove": [],
+      "disabled": false
+    }
+  }
+}