1. Asynchronous Task Processing for AI Data Workflows


    Asynchronous task processing is a method that allows computationally heavy AI data workflows to be managed more efficiently. In cloud environments, you generally use managed services for orchestrating and processing workflows. Depending on the cloud provider you have access to (AWS, Azure, Google Cloud, etc.), you could use different services such as AWS Step Functions, Azure Data Factory, or Google Cloud Workflows.

    Let's craft a Pulumi program that sets up an asynchronous AI data workflow. We'll consider using Google Cloud as it provides robust services like Cloud Dataflow for stream and batch data processing, and Google Cloud Workflows for serverless workflow orchestration.

    Here's what the program will accomplish:

    1. Google Cloud Workflows: Define a workflow with steps to orchestrate tasks. Workflows allow you to connect and coordinate different services in a serverless way.
    2. Google Cloud Dataflow: Create a Dataflow template for process-intensive jobs. Dataflow is a managed service for executing a wide variety of data processing patterns.

    I'll walk through creating a sample Pulumi program to define a basic workflow and a data processing template in Google Cloud.

    import pulumi import pulumi_gcp as gcp # Set up a Google Cloud Workflow which defines a sequence of tasks. workflow = gcp.workflows.Workflow("ai-workflow", description="Asynchronous Task Processing for AI Data Workflows", region="us-central1", # Choose the region that suits your needs service_account="your-service-account@project-id.iam.gserviceaccount.com", # Replace with your own service account source_contents=""" - getCurrentTime: call: http.get args: url: https://us-central1-workflowsample.cloudfunctions.net/datetime result: currentTime - processAIWorkload: call: http.post args: url: https://us-central1-ai-process.cloudfunctions.net/aiTaskHandler body: time: ${currentTime.body} result: workloadResult - finalStep: return: ${workloadResult.body} """, labels={ "environment": "dev", # Label your workflow according to your environment needs } ) # Create a Dataflow job to process data asynchronously. This assumes you have a Dataflow template ready in GCS. dataflow_job = gcp.dataflow.Job("ai-data-job", template_gcs_path="gs://your-bucket/templates/your-dataflow-template", temp_gcs_location="gs://your-bucket/temp", parameters={ "inputFile": "gs://your-bucket/data/input.txt", # Specify the input file path "outputTable": "project-id:dataset.table", # Specify your BQ table # Add any additional parameters your Dataflow template might need }, max_workers=5, # Optional: Configure the max number of workers labels={ "environment": "dev", } ) # Export the name and id of the workflow as stack outputs pulumi.export("workflow_name", workflow.name) pulumi.export("workflow_id", workflow.id) # Export the job id of the data job as a stack output pulumi.export("data_job_id", dataflow_job.id)

    In this program, we use two types of resources:

    1. gcp.workflows.Workflow: It sets up the workflow which can make HTTP calls to various endpoints, in this example, one endpoint gets the current time, and another simulates an AI processing task.

    2. gcp.dataflow.Job: It creates a job that uses a predefined Dataflow template for processing data. The input and output are configured to use Google Cloud Storage and BigQuery, respectively. The locations and specifics like max_workers can be customized based on your use case.

    Please replace placeholders like your-service-account@project-id.iam.gserviceaccount.com, gs://your-bucket/templates/your-dataflow-template, and project-id:dataset.table with your actual service account, dataflow template location in GCS, and BigQuery table detail.

    Furthermore, you might want to tailor the workflow source_contents to fit your specific task sequence requirements. The provided workflow is for illustration purposes and shows how you can orchestrate different service calls within the workflow.

    Note that before running this program, you'll need to have GCP configured for your Pulumi environment, which usually involves setting up the required credentials for the Google Cloud SDK and configuring the GCP project and region/zone settings.

    After deploying this code with Pulumi, you'll obtain the IDs for the workflow and the Dataflow job as outputs. These can be used to monitor progress and completion of the tasks.