1. Orchestrating Distributed Machine Learning Workflows with AWS MWAA


    Apache Airflow is a popular open-source tool to programmatically author, schedule, and monitor workflows. AWS Managed Workflows for Apache Airflow (MWAA) is a managed service that makes it easier to run Apache Airflow on AWS. It's a good choice for orchestrating distributed machine learning workflows because it integrates with other AWS services such as Amazon SageMaker, allowing you to train models, deploy AI inference pipelines, and more.

    To get started with orchestrating distributed machine learning workflows with AWS MWAA using Pulumi, you will:

    1. Set up your AWS environment to host an Apache Airflow instance using AWS MWAA.
    2. Define the necessary resources using Pulumi, which includes the MWAA Environment and associated configurations.

    To provision an Apache Airflow environment with AWS MWAA through Pulumi, you will need an execution role that grants MWAA the necessary permissions to access other AWS resources. You'll also need to define network configurations like subnets and security groups that determine network access. Additionally, you need an S3 bucket where your DAGs and plugins are stored, and optionally, you can specify additional configurations such as Airflow version, max workers, and logging configurations.

    Here's a Pulumi Python program that sets up an AWS MWAA environment for orchestrating distributed machine learning workflows:

    import pulumi import pulumi_aws as aws # Create an IAM role for AWS MWAA. execution_role = aws.iam.Role("mwaa-execution-role", assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "airflow.amazonaws.com" } }] }) # Attach the AmazonMWAAFullAccess policy to the execution role. execution_policy_attachment = aws.iam.RolePolicyAttachment("mwaa-execution-policy-attachment", role=execution_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_MWAA_FULL_ACCESS) # Define the network configurations for the MWAA environment. vpc = aws.ec2.Vpc("mwaa-vpc", cidr_block="") subnet = aws.ec2.Subnet("mwaa-subnet", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a") security_group = aws.ec2.SecurityGroup("mwaa-sg", vpc_id=vpc.id, description="Allow all inbound and outbound traffic for MWAA", egress=[{ "from_port": 0, "to_port": 0, "protocol": "-1", "cidr_blocks": [""], }], ingress=[{ "from_port": 0, "to_port": 0, "protocol": "-1", "cidr_blocks": [""], }]) # Create an S3 bucket for the Airflow DAGs. s3_bucket = aws.s3.Bucket("mwaa-dags-bucket") # Create the MWAA Environment. mwaa_environment = aws.mwaa.Environment("mwaa-environment", name="MyMwaaEnvironment", execution_role_arn=execution_role.arn, airflow_version="2.0.2", network_configuration=aws.mwaa.EnvironmentNetworkConfigurationArgs( security_group_ids=[security_group.id], subnet_ids=[subnet.id] ), source_bucket_arn=s3_bucket.arn, dag_s3_path="dags", plugins_s3_path="plugins", requirements_s3_path="requirements.txt") # Get Airflow Web UI URL using the output property of the MWAA environment. airflow_ui_url = mwaa_environment.airflow_url.apply(lambda url: f"https://{url}") # Export the Airflow Web UI URL so that you can access it later. pulumi.export("airflow_web_ui_url", airflow_ui_url)

    In this program:

    • A new IAM role for MWAA is created with assume_role_policy that allows the service to assume the role.
    • The AmazonMWAAFullAccess managed policy is attached to the execution role so that MWAA can fully manage the Airflow environment.
    • The VPC, subnet, and security group are set up to define the network configuration for the MWAA environment.
    • An S3 bucket is provisioned for storing DAGs, plugins, and any other requirements.
    • Finally, the aws.mwaa.Environment Pulumi resource is used to create the MWAA environment. The version of Airflow and the network configurations are specified, along with the paths to the DAGs and plugins in the S3 bucket.

    At the end of your Pulumi program, pulumi.export is used to output the URL of the Airflow web interface. You can access this URL to interact with your Airflow instance.

    Please note that the Airflow environment provisioning may take some time. Once the resources are provisioned, you should be able to access the Apache Airflow UI and kick off your machine learning workflows.

    This is just the start; you'll need to author your DAGs (Directed Acyclic Graphs) that define your machine learning workflows and place those in the S3 bucket specified by the dag_s3_path. Ensure your execution role has the necessary permissions to access and manage other AWS resources your workflows will interact with, like Amazon SageMaker or any databases you might be using.