1. Distributed Task Queuing for LLMs with GCP Cloud Tasks


    To accomplish distributed task queuing for Large Language Models (LLMs) with Google Cloud Platform (GCP) Cloud Tasks, we will create a task queue using the GCP Cloud Tasks service. This queue will be used to distribute tasks, which could represent work to be done by LLMs, across multiple workers. Here's an overview of what we'll do:

    1. Create a Cloud Tasks Queue: This will be the central component where tasks are sent before they are processed by workers.
    2. Create Tasks: We will set up a way to create tasks that are to be executed by LLM workers.
    3. Handle Task Execution: Although it won't be fully implemented in our Pulumi program, we will conceptually understand where the LLM processing code should be triggered.

    Step 1: Create a Cloud Tasks Queue

    The Queue resource will represent a queue that holds tasks requesting LLM processing. The queue will manage how tasks are dispatched, rate limits, retries, and other configurations.

    Step 2: Create Tasks

    With the Task resource, we define individual units of work to be sent to the queue. Each task, in this case, will represent a request for LLM to process a piece of text or perform a language-related task.

    Step 3: Handle Task Execution

    Workers will poll the created queue for tasks. Once a task is received, the worker will trigger the appropriate LLM with the data from the task. This worker could be a service running on App Engine or Compute Engine, but setting up this worker service is beyond the scope of infrastructure as code and would typically involve writing application-level code.

    Below is a Pulumi Python program that sets up a queue and demonstrates how a task would be added. You must have configured your GCP provider and Pulumi CLI before running this program.

    import pulumi import pulumi_gcp as gcp # Step 1: Create a Cloud Tasks Queue for LLM workloads. llm_queue = gcp.cloudtasks.Queue("llm_queue", name="llm-task-queue", location="us-central1", rate_limits={ # Configure the rate limits according to your requirements "max_dispatches_per_second": 500, "max_burst_size": 100, "max_concurrent_dispatches": 50, }, retry_config={ # Define the retry configuration for failed tasks "max_attempts": 5, "max_backoff": "3600s", # Max backoff duration for retries "min_backoff": "5s", # Min backoff duration for retries "max_doublings": 5, # The maximum number of times that the interval between failed task retries will be doubled before the increase becomes constant } ) # Step 2: Add a Task to the LLM Queue (this is more conceptual in Pulumi, as tasks are typically added programmatically by your application code). llm_task = gcp.cloudtasks.Task("llm_task", name="task-for-llm", queue=llm_queue.name, project=llm_queue.project, location="us-central1", # The httpRequest object would include the details of the actual LLM workload processing request. http_request={ "http_method": gcp.cloudtasks.HttpMethod.POST, "url": "https://example.com/task_handler", # The endpoint where the task will be processed by the worker service "body": "payload for LLM processing", # The payload that the worker service needs to process "headers": { "Content-Type": "application/text", } } ) # Output the queue name and task name for reference pulumi.export('queue_name', llm_queue.name) pulumi.export('task_name', llm_task.name)

    This program sets up the Cloud Tasks queue and a sample task to demonstrate how you would use Pulumi to manage task queues. The actual task handling and distribution are typically done by application code, which you need to write separately from the Pulumi infrastructure setup. You can find more information about Cloud Tasks and how to use them in your code using the official GCP Cloud Tasks documentation.