{ "info": { "author": "Origent Data Sciences", "author_email": "cloudhosting@origent.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Web Environment", "Intended Audience :: Developers", "Intended Audience :: System Administrators", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX", "Programming Language :: Python :: 3", "Topic :: Internet :: WWW/HTTP :: HTTP Servers", "Topic :: Scientific/Engineering :: Bio-Informatics" ], "description": "# ServeRmore\n\nTo help make R more accessible on Serverless Cloud Hosting, starting with AWS Lambda.\nPython Package Index Releases: https://pypi.org/project/serveRmore/\n\n## Requirements\n\nPlease refer to our [LAPTOP.md Guide](https://github.com/Origent/ServeRmore/blob/master/LAPTOP.md) for necessary manual configurations.\n\nPlease refer to your cloud platform for additional information:\n* [AWS Lambda](AWS.md)\n\nTo install the latest package:\n```\npython3 -m pip install serveRmore\n```\n\n## Quickstart\n\n1. Initiate your config file by typing \"srm\" or \"srm help\". These commands are available for awareness. \n```\nsrm help\nsrm version\nsrm status\n```\n\n2. Update your config file in your home directory. At minimum, you'll need:\n * Your own Lambda Execution Role 'arn' value.\n * An S3 bucket and an S3 bucket key (folder).\n * Lambda function name\n\n3. Create a new lambda.R script and create a \"handler\" method in R. Insert \"hello world\" or custom code inside your handler method.\n\n4. Create a new package.R script that will zip up your lambda.R and place on your AWS S3 bucket and key.\n\n5. To deploy your zip file directly to Lambda, try out our new workflow here.\n```\nsrm lambda init\nsrm lambda create\nsrm lambda update\nsrm lambda invoke\nsrm lambda destroy\n```\n\nNOTE: 'init' will establish your R Runtime ARN value in your config. 'create' will establish a brand new Lambda function if it does not exist, and publish your zip file. 'update' will republish your zip file, if your lambda function already exists. \n\n## Custom R Runtime Layers\n\nTo use additional R custom runtime layers that are prebuilt, or to help you build your own, refer to this guide: [R Runtime Custom Layers](custom_layer/README.md)\n\nWe have an older way of deploying on Lambda that we still support for the time being. You can learn more here: [R Environment in Python Runtime](deprecated.md)\n\n## Roadmap\n\nv0.1.0\n* Create more automation on custom layer building\n - Revise ServeRmore.yaml config file for new configurations\n - Launch a temporary EC2 VM\n - Pull down the github repo from bakdata\n - Introduce custom layer settings from config file\n - Build & publish new layer\n - Save the ARN of the new layer to config\n - Terminate the VM\n* Remove old EC2 Virtual Machine on Python runtime workflow\n\n## Changelog\n\nv0.0.2 - Introduces running R directly on Lambda via AWS' new custom layers feature, big thanks to [@bakdata](https://github.com/bakdata).\n* Several new commands for initiating the R runtime version, creating new functions, updating them, and destroying them.\n* New documentation for how to build a custom R layer.\n\nv0.0.1 - Initial Release\n* Automated a genomics analysis guide that used Lambda with R. Introduces an automated build process that creates a temporary AWS EC2 Virtual Machine, installs an R environment with CRAN and custom R packages, wraps them into a Lambda Package. Also requires manual development of a Handler.py file that calls the R environment and R methods through r2py.\n* Provides an easy way to iterate and repackage after handler.py changes.\n\n## Contributing\n\nPlease refer to our guide for more information. 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