layerpack 0.1.1

A Python library for simplifying AWS Lambda deployment

layerpack

A Python library for simplifying AWS Lambda deployment by automatically managing dependencies and creating deployment packages. Uses UV for reliable dependency resolution.

Features

• Automatically detects and downloads required Python packages
• Creates Lambda-compatible ZIP files with all dependencies
• Supports custom package versions and requirements.txt
• Handles platform-specific dependencies for AWS Lambda
• Optimizes package size through selective inclusion
• Supports layer creation with multiple runtimes
• Uses UV for reliable dependency resolution

Installation

You can install layerpack using pip:

pip install layerpack

Or using Poetry:

poetry add layerpack

Usage

Basic Usage

from layerpack import LambdaPackager

# Initialize packager
packager = LambdaPackager(
    runtime="python3.9",
    output_dir="./dist"
)

# Create a layer from requirements.txt
packager.create_layer_from_requirements("requirements.txt", "my-layer")

# Or specify packages directly
packager.create_layer_from_packages(
    packages=["pandas", "numpy"],
    layer_name="data-science-layer"
)

Command Line Interface

The library provides a convenient CLI with verbose logging support:

# Analyze dependencies in requirements.txt
lambda-bundler -v analyze -r requirements.txt

# Create layer from requirements.txt
lambda-bundler create-layer -r requirements.txt -n my-layer

# Create layer from package list
lambda-bundler create-layer -p pandas,numpy -n data-science-layer

Example requirements.txt

# Core dependencies
requests>=2.31.0
urllib3<2.0.0  # Compatible with requests

# Data processing
pandas==2.2.0
numpy==1.26.4

# AWS 
boto3==1.34.34
botocore==1.34.34

# Utilities
pyyaml==6.0.1
python-dotenv==1.0.1

Advanced Configuration

packager = LambdaPackager(
    runtime="python3.9",
    output_dir="./dist",
    config={
        "exclude_packages": ["pytest", "mock"],
        "include_source": ["custom_module/"],
        "optimization_level": 2,
        "max_size_mb": 250,
        "compatible_runtimes": ["python3.8", "python3.9"]
    }
)

Configuration Options

• exclude_packages: List of packages to exclude from the layer
• include_source: List of local source directories to include
• optimization_level: Python optimization level (0-2)
• max_size_mb: Maximum size limit for the layer
• compatible_runtimes: List of compatible Python runtimes
• strip_test_files: Remove test files to reduce size
• include_dependencies: Include transitive dependencies

Error Handling

The library provides specific exceptions for different error cases:

• PackageNotFoundError: Package not found in PyPI
• IncompatibleRuntimeError: Package incompatible with Lambda runtime
• LayerSizeLimitError: Layer exceeds size limit
• DependencyConflictError: Conflicting package dependencies

Development

Prerequisites

• Python 3.9 or higher
• UV for dependency management

Setting Up Development Environment

1. Clone the repository:

git clone https://github.com/richardissailing/layerpack.git
cd layerpack

2. Install dependencies:

poetry install

3. Run tests:

poetry run pytest

4. Run linting:

poetry run ruff check .
poetry run ruff format .

Building and Publishing

1. Build the package:

poetry build

2. Publish to PyPI:

poetry publish

Best Practices

1. Always specify exact package versions in requirements.txt
2. Use the exclude_packages option to remove unnecessary dependencies
3. Set appropriate compatible_runtimes for cross-runtime compatibility
4. Enable strip_test_files to reduce layer size
5. Use optimization_level=2 for production deployments
6. Use the verbose flag (-v) when troubleshooting dependency resolution

Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch
3. Write your changes
4. Run the tests (poetry run pytest)
5. Run the linter (poetry run ruff check .)
6. Submit a Pull Request

Please make sure your PR includes:

• A clear description of the changes
• Updates to documentation if needed
• Additional tests for new features
• All tests passing and code linted

License

This project is licensed under the MIT License - see the LICENSE file for details.