actig-lambda-deployer 0.1.1

AWS Lambda Container Deployment CLI with AI-powered code analysis

ACTIG Lambda Deployer

An intelligent AWS Lambda container deployment CLI that automates code analysis and deployment using AWS Bedrock Claude for AI-powered artifact generation.

Features

✨ AI-Powered Analysis: Uses AWS Bedrock Claude to analyze Lambda code and generate optimized deployment artifacts
🐳 Container Deployments: Automated Docker container builds and ECR repository management
🚀 Complete CI/CD Pipeline: Integrates CodeCommit, CodeBuild, and CloudFormation for end-to-end automation
📦 Artifact Generation: Automatically generates Dockerfile, buildspec.yml, and CloudFormation templates
🔧 Team Standards: Follows your team's build patterns and requirements

Installation

pip install actig-lambda-deployer

Prerequisites

• AWS CLI configured with appropriate permissions
• AWS Bedrock access with Claude models enabled
• Git configured for CodeCommit (if using CodeCommit)
• Docker (for local testing)

Quick Start

1. Prepare Your Lambda Function

Your Lambda function directory should contain:

• Python source files (.py)
• requirements.txt file (required)

Example structure:

my-lambda/
├── lambda_function.py
└── requirements.txt

2. Deploy Your Function

actig-lambda-deployer deploy ./my-lambda --function-name my-function

The tool will:

1. Analyze your code using AI
2. Generate optimized deployment artifacts
3. Create CI/CD pipeline infrastructure
4. Build and deploy your container image
5. Deploy your Lambda function

3. Check Deployment Status

actig-lambda-deployer status my-function

Configuration

AWS Credentials

Configure AWS credentials using any standard method:

# Using AWS CLI (Preferred Method)
aws configure

# Using environment variables
export AWS_ACCESS_KEY_ID=your-key
export AWS_SECRET_ACCESS_KEY=your-secret
export AWS_DEFAULT_REGION=us-east-1

# Using AWS profiles
actig-lambda-deployer deploy ./my-lambda --function-name my-func --profile production

Bedrock Access

Ensure you have access to these Bedrock models:

• anthropic.claude-3-5-sonnet-20240620-v1:0

Enable model access in the AWS Bedrock console.

Usage Examples

Basic Deployment

actig-lambda-deployer deploy ./src --function-name api-handler

Custom ECR Repository

actig-lambda-deployer deploy ./src --function-name api-handler --repository-name my-custom-repo

Dry Run (Preview Changes)

actig-lambda-deployer deploy ./src --function-name api-handler --dry-run

Different AWS Region

actig-lambda-deployer deploy ./src --function-name api-handler --region us-west-2

Debug Mode

actig-lambda-deployer deploy ./src --function-name api-handler --log-level DEBUG

CLI Reference

Commands

• deploy - Analyze and deploy Lambda function
• status - Check deployment status
• --help - Show help information

Global Options

• --profile - AWS profile to use (default: default)
• --region - AWS region (default: us-east-1)
• --log-level - Logging level (DEBUG, INFO, WARNING, ERROR)

Deploy Options

• --function-name - Lambda function name (required)
• --repository-name - ECR repository name (optional)
• --dry-run - Preview changes without deployment

Requirements File

Your requirements.txt must be present in the Lambda source directory. Example:

boto3>=1.26.0
requests>=2.31.0
pydantic>=1.10.0

Generated Artifacts

The tool automatically generates:

1. Dockerfile - Optimized for Lambda container runtime
2. buildspec.yml - CodeBuild configuration for CI/CD
3. CloudFormation template - Infrastructure as code

These are created in your source directory and can be customized as needed.

AWS Resources Created

The deployment creates:

• ECR repository for container images
• CodeCommit repository for source code (optional)
• CodeBuild project for CI/CD pipeline
• IAM roles with minimal required permissions
• CloudFormation stack for Lambda function
• CloudWatch log groups

Troubleshooting

Common Issues

"requirements.txt not found"

# Ensure requirements.txt exists in your source directory
ls ./my-lambda/requirements.txt

"AWS credentials not configured"

# Configure AWS credentials
aws configure
# Or set environment variables
export AWS_PROFILE=your-profile

"Bedrock access denied"

• Enable model access in AWS Bedrock console
• Ensure your IAM user/role has Bedrock permissions

"Build failed with Docker errors"

• Check your requirements.txt for invalid packages
• Ensure all dependencies are compatible with Lambda runtime

Debug Mode

For detailed logging:

actig-lambda-deployer deploy ./src --function-name my-func --log-level DEBUG

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• GitHub Issues: Report bugs or request features
• Documentation: Full documentation