1. Host Python Libraries for Data Preprocessing on AWS Lambda


    To host Python libraries for data preprocessing on AWS Lambda, you will need to package those libraries into a Lambda Layer. Lambda Layers are a way to manage your function's dependencies separately, enabling you to keep your deployment package small. This is essential because AWS Lambda has a limit on the deployment package size.

    Here are the key resources we'll use to achieve this:

    1. AWS Lambda Layer (aws.lambda.LayerVersion) - This resource will hold the Python libraries you need for data preprocessing.

    2. AWS Lambda Function (aws.lambda.Function) - This represents your main Lambda function that will process the data. It will use the libraries from the Lambda Layer.

    I will guide you through a Pulumi Python program that will create a Lambda Layer with the required Python libraries and an AWS Lambda Function that will use this layer for its operation.

    First, you need a way to package your Python libraries. You can do that with a script that installs the libraries into a directory and packages them in a ZIP file that you can upload to AWS Lambda as a Layer. Typically, you would use a requirements.txt file to list your Python dependencies and then run:

    pip install -r requirements.txt -t python/lib/python3.8/site-packages/ zip -r my-python-libraries-layer.zip python

    Where python/lib/python3.8/site-packages/ is a path that matches the Python execution environment on the Lambda service. Adjust the path if you're using a different Python runtime version.

    After your ZIP file is created, you can use it in a Pulumi program as follows:

    import pulumi import pulumi_aws as aws # Create an AWS Lambda Layer with the Python libraries ZIP file. python_libs_layer = aws.lambda_.LayerVersion("pythonLibsLayer", compatible_runtimes=["python3.8"], code=pulumi.FileAsset("my-python-libraries-layer.zip"), layer_name="python-libs-layer" ) # Create an IAM role for the Lambda function. lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Principal": { "Service": "lambda.amazonaws.com" }, "Effect": "Allow", "Sid": "" }] }""" ) # Attach the AWS managed LambdaBasicExecutionRole so that your Lambda # function can log to CloudWatch. attachment = aws.iam.RolePolicyAttachment("lambdaLogs", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) # Create the Lambda function, referencing the created IAM role and Lambda Layer. lambda_function = aws.lambda_.Function("myLambdaFunction", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./lambda_function") # Directory with your Lambda handler }), handler="handler.main", # Assuming "main" function in "handler.py" role=lambda_role.arn, runtime="python3.8", layers=[python_libs_layer.arn] ) # Export the Lambda function's name and the layer's ARN. pulumi.export('lambda_function_name', lambda_function.name) pulumi.export('lambda_layer_arn', python_libs_layer.arn)

    In this program:

    • pulumi.FileAsset("my-python-libraries-layer.zip") uploads the ZIP file created earlier to AWS Lambda as a Layer.
    • pulumi.FileArchive("./lambda_function") is a placeholder directory for your Lambda function's code. It should contain all the files needed by the Lambda, including a handler.py with a main function.
    • lambda_function is created with the Python 3.8 runtime and python_libs_layer.arn as a layer, granting it access to the Python libraries in the layer.

    Ensure you have Pulumi installed and configured with appropriate AWS credentials before running this program. After running the program with pulumi up, Pulumi will provision these resources in AWS on your behalf. Your Lambda will now be able to use the libraries in the Layer for data preprocessing tasks.