Setup a Local Python AWS Lambda Development Environment

We can comfortably develop for AWS Lambda with python on any modern Operating System

Table of Contents

We can comfortably develop for AWS Lambda with python on any modern Operating System. Almost any up-to-date version of Linux, MacOS, or Windows will support the software tooling. The most demanding piece of software from a local-hardware perspective is Docker which is used for replicating production Lambda environments and executing tests. It is possible to develop and deploy without docker, but not recommended.

We are going to provide complete setup and install instructions for local development workstations with the following configurations below. If your workstation configuration is not listed below - it's still possible that core the tooling can work so long as docker and python 3.7.4 can be installed. Due to space constraints though, we are going to focus on the most common configurations that we know work.

Operating Systems : Windows:: Windows 10 Professional 64bit with latest updates for Windows Subsystem for Linux : Mac OS: Sierra 10.12+ : Linux: Amazon Linux 2, Ubuntu 18.04+

Hardware: : CPU: x86_64 architecture : RAM: 4GB+

For the purpose of demonstration, examples shown throughout this course have been executed on a AWS Amazon Linux 2.

Software used in this course#

This course will provide full install steps for each of the packages below. The version listed here is the version used at time of writing and testing from this course.

NOTE: If your local workstation configuration is unsupported, or you have permissions restrictions on your workspace that prevent you from installing any of the software listed above - you may still be able to develop by using a client for AWS Workspaces as explained in more detail in the Remote Workspace section.

Selecting a code editor for python development#

Python 3 support is available for many popular, general-purpose editors, either natively or through the use of plug-ins. For readers who already have high proficiency and comfort using a specific editor - don’t feel pressured to switch to something more specialized. Code-highlighting and linting features are available for almost every editor, though it may require a plugin to get them configured.

For the rest of us - VSCode or PyCharm are solid options for providing a fully featured python IDE experience right out-of-the-box.

VSCode : VSCode is maintained by Microsoft and released under the MIT License.

PyCharm : PyCharm is maintained by Jetbrains, and provides two editions. One that requires purchase a professional license, and a community edition, licensed under Apache 2.

Both editors are supported on all major operating systems and are widely used within the Python developer community. They have first-class support for Python and comprehensive documentation available. Either is an excellent choice. For the purpose of this course, any editor examples will use VSCode.

Install docker#

Docker updates so frequently it's always best just to check the latest docs at https://docs.docker.com/install/.

Account Setup at Docker Hub Possibly Required

Docker has started to re-direct users through to an account sign-up process to download the latest products. Linux users don't need to do that in most cases, but others who wish to use the latest Docker Desktop may need to sign up. They don't request a credit card to sign up, but will ask for an email. Docker Desktop isn't strictly required, so if you have a functioning older version of docker, it may be easier to just update that one in place to avoid the sign-up process.

Install docker on Mac OS#

The AWS SAM CLI supports Docker running on macOS Sierra 10.12 or above. AWS recommends using Docker Desktop for Mac.

For older version of MacOS, Docker Toolbox is available.

Install docker on Windows#

To install Docker for Windows 10 Professional, see Install Docker Desktop for Windows

For older or Non-Professional versions of Windows, use Docker Toolbox

Install docker on Linux (Amazon Linux 2)#

The version of docker in the Amazon Linux 2 works fine. No need to use the docker-specific repo.