AWS Deep Learning Containers Server

A Model Context Protocol (MCP) server for AWS Deep Learning Containers (DLC) that provides tools for discovering, building, deploying, and troubleshooting DLC images.

Features

• Dynamic DLC Image Discovery: Automatically fetches latest images from AWS DLC GitHub - always up-to-date
• Image Building: Create custom Dockerfiles and build images based on DLC base images
• Multi-Platform Deployment: Deploy to SageMaker, EC2, ECS, and EKS
• Instance Recommendations: Get GPU instance recommendations based on model size and budget
• Upgrade Support: Analyze upgrade paths and generate migration Dockerfiles
• Troubleshooting: Diagnose common DLC issues with actionable solutions
• Best Practices: Security, cost optimization, and deployment guidance
• No AWS Credentials Required: Discovery tools work without AWS credentials

Quick Start

Option 1: Run with uv (Recommended)

# Clone the repo
git clone https://github.com/aws-samples/sample-dlc-mcp-server.git
cd sample-dlc-mcp-server

# Run directly with uv
uv run dlc-mcp-server

Option 2: Run with Docker

# Build the image
docker build -t dlc-mcp-server .

# Run the container
docker run -it --rm \
  -v ~/.aws:/root/.aws:ro \
  dlc-mcp-server

Option 3: Install locally

pip install -e .
dlc-mcp-server

MCP Client Configuration

For Amazon Q CLI

Add to ~/.aws/amazonq/mcp.json:

{
  "mcpServers": {
    "dlc-mcp-server": {
      "command": "uv",
      "args": ["--directory", "/path/to/sample-dlc-mcp-server", "run", "dlc-mcp-server"],
      "timeout": 120000
    }
  }
}

For Kiro

Add to .kiro/settings/mcp.json:

{
  "mcpServers": {
    "dlc-mcp-server": {
      "command": "uv",
      "args": ["--directory", "/path/to/sample-dlc-mcp-server", "run", "dlc-mcp-server"],
      "timeout": 120000
    }
  }
}

Using Docker

{
  "mcpServers": {
    "dlc-mcp-server": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-v", "~/.aws:/root/.aws:ro", "dlc-mcp-server"],
      "timeout": 120000
    }
  }
}

Available Tools

DLC Discovery

Tool	Description
search_dlc_images	Search DLC images by framework, version, accelerator, platform
get_dlc_recommendation	Get image recommendations based on model type and size
list_dlc_frameworks	List all available frameworks with versions
get_llm_serving_options	Compare vLLM, SGLang, DJL, NeuronX options
compare_dlc_images	Side-by-side image comparison
refresh_dlc_catalog	Force refresh image catalog from GitHub

Image Building

Tool	Description
create_custom_dockerfile	Generate Dockerfile with custom packages
build_custom_dlc_image	Build and optionally push to ECR

Deployment

Tool	Description
deploy_to_sagemaker	Deploy to SageMaker endpoint
deploy_to_ec2	Launch EC2 instance with DLC
deploy_to_ecs	Deploy to ECS cluster
deploy_to_eks	Deploy to EKS cluster
get_sagemaker_endpoint_status	Check endpoint status

Instance Advisor

Tool	Description
get_instance_recommendation	GPU instance recommendations by model size
list_gpu_instances	List available GPU instances with pricing
estimate_training_cost	Estimate training job costs

Troubleshooting

Tool	Description
analyze_dlc_error	Analyze error logs with root cause analysis
diagnose_common_issues	Diagnose common DLC problems
get_framework_compatibility_info	Check framework version compatibility

Best Practices

Tool	Description
get_security_best_practices	Security guidelines
get_cost_optimization_tips	Cost reduction strategies
get_deployment_best_practices	Platform-specific guidance
get_framework_specific_best_practices	Framework optimization tips

Supported Frameworks

Framework	Latest Version	Use Cases
PyTorch	2.9.0	Training, Inference
TensorFlow	2.19.0	Training, Inference
vLLM	0.15.1	LLM Inference
SGLang	0.5.8	LLM Inference
HuggingFace PyTorch	2.6.0	NLP Training/Inference
AutoGluon	1.5.0	AutoML
DJL	0.36.0	Large Model Inference
PyTorch NeuronX	2.9.0	Trainium/Inferentia

Example Usage

Find vLLM images

Search for vLLM images for SageMaker inference

Deploy LLM to SageMaker

Deploy Qwen2.5-32B using vLLM on SageMaker with the right instance type

Get instance recommendations

What instance should I use for a 35GB model?

Troubleshoot errors

Help me fix this CUDA out of memory error: [paste error]

Configuration

Environment variables:

Variable	Description	Default
ALLOW_WRITE	Enable build/deploy operations	false
ALLOW_SENSITIVE_DATA	Enable detailed logs access	false
FASTMCP_LOG_LEVEL	Logging level	ERROR
FASTMCP_LOG_FILE	Log file path	None

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Run linting
ruff check .

See DEVELOPMENT.md for more details.

License

This library is licensed under the MIT-0 License.