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The [benchmark](benchmark/deployment.ts) ran three times and the best time were taken for each step.\n\n| | 💤 PythonFunction | 🚀 Turbo Layers | 💤 5x PythonFunction | 🚀 5x Functions w/ Shared Turbo Layer |\n|------------------------|---------------------|------------------|----------------------|---------------------------------------|\n| Initial Synth | 1:21 | 0:06 | 2:43 | 0:06 |\n| Initial Deploy | 1:18 | 2:05 | 2:10 | 2:06 |\n| Code Change Synth | 0:31 | 0:06 | 1:21 | 0:06 |\n| Code Change Deploy | 0:49 | 0:29 | 1:19 | 0:36 |\n| New Dependency Synth | 0:33 | 0:06 | 1:30 | 0:06 |\n| New Dependency Deploy | 0:52 | 1:50 | 1:31 | 1:50 |\n\nAs you can see, code changes synth much faster and deploy a bit faster too. Dependency changes take longer to deploy, but are assumed to be way less frequent than code changes. The more dependencies your function uses, the better the results will be.\n\nTo run the benchmark yourself use:\n\n```\nnpm run bundle && npm run benchmark\n```\n\n## API\n\nThe best way to browse API documentation is on [Constructs Hub][13]. It is available in all supported programming languages.\n\n## Installation\n\n1. Confirm you're using CDK v2\n2. Install the appropriate package\n 1. [Python][6]\n ```\n pip install cloudsnorkel.cdk-turbo-layers\n ```\n 2. [TypeScript or JavaScript][7]\n ```\n npm i @cloudsnorkel/cdk-turbo-layers\n ```\n 3. [Java][8]\n ```xml\n \n com.cloudsnorkel\n cdk.turbo-layers\n \n ```\n 4. [Go][11]\n ```\n go get github.com/CloudSnorkel/cdk-turbo-layers-go/cloudsnorkelcdkturbolayers\n ```\n 5. [.NET][12]\n ```\n dotnet add package CloudSnorkel.Cdk.TurboLayers\n ```\n\n## Examples\n\nThe very basic example below will create a layer with dependencies specified as parameters and attach it to a Lambda function.\n\n```typescript\n const packager = new PythonDependencyPackager(this, 'Packager', {\n runtime: lambda.Runtime.PYTHON_3_9,\n type: DependencyPackagerType.LAMBDA,\n});\nnew Function(this, 'Function with inline requirements', {\n handler: 'index.handler',\n code: lambda.Code.fromInline('def handler(event, context):\\n import requests'),\n runtime: lambda.Runtime.PYTHON_3_9,\n // this will create a layer from with requests and Scrapy in a Lambda function instead of locally\n layers: [packager.layerFromInline('inline requirements', ['requests', 'Scrapy'])],\n});\n```\n\nThe next example will create a layer with dependencies specified in a `requirements.txt` file and attach it to a Lambda function.\n\n```typescript\nconst packager = new PythonDependencyPackager(this, 'Packager', {\n runtime: lambda.Runtime.PYTHON_3_9,\n type: DependencyPackagerType.LAMBDA,\n});\nnew Function(this, 'Function with external source and requirements', {\n handler: 'index.handler',\n code: lambda.Code.fromAsset('lambda-src'),\n runtime: lambda.Runtime.PYTHON_3_9,\n // this will read requirements.txt and create a layer from the requirements in a Lambda function instead of locally\n layers: [packager.layerFromRequirementsTxt('requirements.txt', 'lambda-src')],\n});\n```\n\nCustom package managers like Pipenv or Poetry are also supported.\n\n```typescript\nconst packager = new PythonDependencyPackager(this, 'Packager', {\n runtime: lambda.Runtime.PYTHON_3_9,\n type: DependencyPackagerType.LAMBDA,\n});\nnew Function(this, 'Function with external source and requirements', {\n handler: 'index.handler',\n code: lambda.Code.fromAsset('lambda-poetry-src'),\n runtime: lambda.Runtime.PYTHON_3_9,\n // this will read pyproject.toml and poetry.lock and create a layer from the requirements in a Lambda function instead of locally\n layers: [packager.layerFromPoetry('poetry dependencies', 'lambda-poetry-src')],\n});\n```\n\nIf your dependencies have some C library dependencies, you may need to use the more capable but slower CodeBuild packager.\n\n```typescript\nconst packager = new PythonDependencyPackager(this, 'Packager', {\n runtime: lambda.Runtime.PYTHON_3_9,\n type: DependencyPackagerType.CODEBUILD,\n preinstallCommands: [\n 'apt install -y libxml2-dev libxslt-dev libffi-dev libssl-dev',\n ],\n});\nnew Function(this, 'Function with external source and requirements', {\n handler: 'index.handler',\n code: lambda.Code.fromAsset('lambda-pipenv-src'),\n runtime: lambda.Runtime.PYTHON_3_9,\n layers: [packager.layerFromPipenv('pipenv dependencies', 'lambda-pipenv-src')],\n});\n```\n\nBuilding layers for ARM64 functions is also supported.\n\n```typescript\nconst packager = new PythonDependencyPackager(this, 'Packager', {\n runtime: lambda.Runtime.PYTHON_3_9,\n type: DependencyPackagerType.LAMBDA,\n architecture: Architecture.ARM_64,\n});\nnew Function(this, 'Function with external source and requirements', {\n handler: 'index.handler',\n code: lambda.Code.fromAsset('lambda-poetry-src'),\n runtime: lambda.Runtime.PYTHON_3_9,\n architecture: Architecture.ARM_64,\n layers: [packager.layerFromPoetry('poetry dependencies', 'lambda-poetry-src')],\n});\n```\n\nAll these examples are for Python, but the same API is available for Node.js, Ruby, and Java. The same build options are available. Multiple different package managers are supported. See [Constructs Hub][13] for more details.\n\n## Older Implementations\n\n* [lovage](https://github.com/CloudSnorkel/lovage): standalone Python framework that uses the same trick to deploy decorated functions to AWS\n* [serverless-pydeps](https://github.com/CloudSnorkel/serverless-pydeps): plugin for [Serverless Framework](https://www.serverless.com/) that speeds up deployment\n\n[6]: https://pypi.org/project/cloudsnorkel.cdk-turbo-layers\n[7]: https://www.npmjs.com/package/@cloudsnorkel/cdk-turbo-layers\n[8]: https://search.maven.org/search?q=g:%22com.cloudsnorkel%22%20AND%20a:%22cdk.turbo-layers%22\n[11]: https://pkg.go.dev/github.com/CloudSnorkel/cdk-turbo-layers-go/cloudsnorkelcdkturbolayers\n[12]: https://www.nuget.org/packages/CloudSnorkel.Cdk.TurboLayers/\n[13]: https://constructs.dev/packages/@cloudsnorkel/cdk-turbo-layers/\n","readmeFilename":"README.md"}