In true newline-fashion, we'll get *rolling* with a practical application right away. We're creating a fully functional, dice-rolling chat bot for Slack!

Create a Serverless Slack Bot with AWS Lambda and Python

Introduction

There's no `AWS Lambda` without `AWS`. That means we'll need a few AWS resources created and configured on the AWS side of things to develop and effectively.

How to Setup a Free AWS Developer Environment

Here, we'll configure an `AWS Workspace` or `AWS EC2 Instance` as the development environment to use for developing for AWS Lambda.

How to Configure EC2 and AWS Workspaces Local Credentials

Configuring Your AWS Environment

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

Setup a Local Python AWS Lambda Development Environment

We will be using the AWS Lambda runtime for the latest stable version of Python 3 - `python3.7`.

Installing Python 3 and Pyenv on MacOS, Windows, and Linux

The easiest way to configure our local development with our credentials is to run the command `aws configure`

How to Login to AWS CLI and Configure Default Region

Configuring Your Local Machine

How Our Slack Bot "Dicebot" Will Work

How to Create an AWS S3 Bucket for Lambda Artifacts

How to Code a Lambda Hello World Function with AWS SAM

How to Test AWS Lambda Functions Locally With Docker

The Beginner Guide to AWS Lambda Deployment

Creating Hello-world Lambda

How to Create a Slack Bot

How to Code a Python Dice Rolling Simulator Program

How to Turn Serverless Python Lambda Code Into a Slack Bot

Testing Our Serverless Slack Dicebot with Docker Locally

Cleaning Up Our Serverless Dicebot Deployment

Creating Dicebot Lambda

*Serverless Computing* may sound like a scary topic, but with the rise of AWS Lambda and mature tools supporting it - it's more approachable than ever. In this newline course, we're going to learn what serverless technology is and isn't, and why it belongs in your developer toolbox. We'll see that for certain common projects - like custom chat bot creation - AWS Lambda really shines for it's simplicity, affordability, and ability to integrate with the wider AWS ecosystem. 

Some developers love using serverless tech because it helps them save money, scale quickly, and deliver on reliability goals. While all of those things are possible with Lambda (especially cost savings which are common - but certainly not guaranteed), it's the development agility and reduction of operations overhead that drives my love for the service. It's a relief to be able to get right into the code, without worrying about configuring operating systems, deploying containers, or scaling clusters. Serverless on Lambda gives developers the resources needed to write, deploy, and _maintain_ production-ready software - all without significant operational overhead.

In true newline-fashion, we'll get *rolling* with a practical application right away. RPG players rejoice - we're creating a fully functional, dice-rolling chat bot for Slack! While the example bot will be for Slack, the same idea can be easily adapted to create a bot for Telegram, Discord, or nearly any other chat service with a bot API. 

We will be using Python 3 for this project, but it's worth noting that [AWS Lambda works with other common languages and runtimes](https://docs.aws.amazon.com/lambda/latest/dg/lambda-runtimes.html), including NodeJS, Java, ruby, go, and .NET. The basic mechanisms of creating, testing, and deploying your Lambda do not vary significantly based on your runtime choice.

By the end of this course you'll:

* Deploy a Slack Bot that runs on AWS Lambda
* Understand how to keep your AWS account secure
* Be able to deploy custom Lambda functions
* Understand serverless architecture and how it works

In this course we'll build and deploy a Serverless Slack Bot using AWS Lambda and Python. Along the way you'll learn about proper security, how lambda works, and how to deploy your service.

Create a Serverless Slackbot with AWS Lambda and Python

How to deploy a serverless bot into production

Preparing your Local Development Environment For AWS Lambda and Python Development

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 

 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 

 can be installed. Due to space constraints though, we are going to focus on the most common configurations that we know work.

For the purpose of demonstration, examples shown throughout this course have been executed on a 

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.

| software name | version       | installed from |
|---------------|---------------|----------------|
| Python        | `3.7.4`       | compiled from source by `pyenv`|
| pipenv        |  `2018.11.26` | `pip` |
| awscli        | `1.16.255`    | `pip` |
| aws-sam-cli   | `0.22.0`      | `pip` |
| cookiecutter  | `1.6.0`       | `pip` |
| docker        |  `18`         | `amazonlinux 2 yum repo` |

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 

Selecting a code editor for python development

 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.

 are solid options for providing a fully featured python IDE experience right out-of-the-box.

 is maintained by Microsoft and released under the 

 is maintained by Jetbrains, and provides 

. 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.

Docker updates so frequently it's always best just to check the latest docs at 

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 

 may need to sign up. They don't request a credit card to sign up, but will ask for an email. 

 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.

To install Docker for Windows 10 Professional, see 

For older or Non-Professional versions of Windows, use 

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

---
title: Setup a Local Python AWS Lambda Development Environment
isPublicLesson: true
description: "We can comfortably develop for AWS Lambda with python on any modern Operating System"
---

# Preparing your Local Development Environment For AWS Lambda and Python Development

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](#docker-install) 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.

    | software name | version       | installed from |
    |---------------|---------------|----------------|
    | Python        | `3.7.4`       | compiled from source by `pyenv`|
    | pipenv        |  `2018.11.26` | `pip` |
    | awscli        | `1.16.255`    | `pip` |
    | aws-sam-cli   | `0.22.0`      | `pip` |
    | cookiecutter  | `1.6.0`       | `pip` |
    | docker        |  `18`         | `amazonlinux 2 yum repo` |

T> **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](#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](https://code.visualstudio.com/) or [PyCharm](https://www.jetbrains.com/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](https://github.com/microsoft/vscode/blob/master/LICENSE.txt).

PyCharm
: `PyCharm` is maintained by Jetbrains, and provides [two editions](https://www.jetbrains.com/pycharm/features/editions_comparison_matrix.html). 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/>.

I> Account Setup at Docker Hub Possibly Required
I>
I> 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.