Aws lambda python for ami tagging after creation

When working with AWS Lambda functions in Python, it is common to have the need to tag an Amazon Machine Image (AMI) after its creation. In this article, we will explore three different ways to achieve this task using Python.

Option 1: Boto3 Library

The first option is to use the Boto3 library, which is the official AWS SDK for Python. Boto3 provides a high-level API for interacting with various AWS services, including EC2. To tag an AMI using Boto3, you can follow these steps:


import boto3

def tag_ami(ami_id, tags):
    ec2_client = boto3.client('ec2')
    ec2_client.create_tags(Resources=[ami_id], Tags=tags)

# Usage example
ami_id = 'ami-12345678'
tags = [{'Key': 'Name', 'Value': 'MyAMI'}, {'Key': 'Environment', 'Value': 'Production'}]
tag_ami(ami_id, tags)

This code snippet demonstrates how to use the Boto3 library to tag an AMI. The tag_ami function takes the AMI ID and a list of tags as input. It then creates a Boto3 EC2 client and uses the create_tags method to apply the tags to the specified AMI.

Option 2: AWS CLI

If you prefer using the AWS Command Line Interface (CLI), you can achieve the same result by invoking the CLI commands from your Python script. Here’s an example:


import subprocess

def tag_ami(ami_id, tags):
    for tag in tags:
        subprocess.run(['aws', 'ec2', 'create-tags', '--resources', ami_id, '--tags', f'Key={tag["Key"]},Value={tag["Value"]}'])

# Usage example
ami_id = 'ami-12345678'
tags = [{'Key': 'Name', 'Value': 'MyAMI'}, {'Key': 'Environment', 'Value': 'Production'}]
tag_ami(ami_id, tags)

In this approach, the tag_ami function uses the subprocess.run method to execute the AWS CLI command aws ec2 create-tags with the appropriate arguments. The command creates tags for the specified AMI using the provided key-value pairs.

Option 3: AWS SDK for Python (Boto3) with Lambda Layers

If you are working with AWS Lambda functions and want to reduce the size of your deployment package, you can leverage Lambda Layers. Lambda Layers allow you to manage your in-development function code independently from the unchanging code and resources that it uses. Here’s an example:


import boto3

def tag_ami(ami_id, tags):
    ec2_client = boto3.client('ec2')
    ec2_client.create_tags(Resources=[ami_id], Tags=tags)

# Usage example
ami_id = 'ami-12345678'
tags = [{'Key': 'Name', 'Value': 'MyAMI'}, {'Key': 'Environment', 'Value': 'Production'}]
tag_ami(ami_id, tags)

This code snippet is similar to Option 1, but it assumes that the Boto3 library is already available in the Lambda environment. By using Lambda Layers, you can exclude the Boto3 library from your deployment package, reducing its size and improving deployment times.

After considering these three options, the best approach depends on your specific requirements and constraints. If you are already using Boto3 in your project, Option 1 is the most straightforward and recommended choice. If you prefer using the AWS CLI or want to reduce the size of your deployment package, Options 2 and 3 respectively provide alternative solutions.

Rate this post

5 Responses

    1. I personally think option 1 is a no-brainer. Why waste time with option 3 when you can have the easiest solution? Dont overcomplicate things, folks!

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents