@Marvin Is there a hybrid work pool to which I can connect workers locally on my higher priority pcs and if then unavailable, start serverless jobs on cloud run?

Yes, Prefect does support a hybrid work pool setup that can manage both local and serverless cloud workers. ### Key Points from Documentation: 1. Work Pools Overview: Work pools bridge the Prefect orchestration layer and infrastructure for flow runs. They allow for dynamic infrastructure provisioning and configuration, making them suitable for infrequent tasks with high resource requirements. Source 2. Serverless Infrastructure with Workers: Prefect can run flows on various serverless platforms such as AWS ECS, Azure Container Instances, and Google Cloud Run. You can create a work pool that sends work to these platforms, and workers in your serverless cloud provider will poll the work pool for scheduled runs. Source 3. Hybrid Work Pools: There are options for both pull-based and push-based work pools. For instance, push work pools don’t require a worker running on the cloud provider; Prefect Cloud manages the job connections. Source 4. Tutorial Emphasis: Recent updates in tutorials emphasize the distinction between

.serve()

and

worker

deployment methods. This helps guide users in choosing between long-lived infrastructure and dynamically provisioned infrastructure. GitHub Issue Discussion ### Implementation Steps: 1. Create Work Pool: - Define and configure a work pool for both your local and serverless environments. - Example configurations for Google Cloud Run can be found in the Prefect-GCP guide. 2. Deploy Flows: - Utilize

flow.deploy()

for your deployments as this fits work pool-based dynamic execution. 3. Run Workers: - Start local workers on your high-priority PCs. - Configure workers on your chosen serverless cloud platform to pick up tasks when local workers are unavailable. Here are the main advantages: - Dynamic Resource Management: No idle resources; infrastructure is provisioned dynamically based on the load. - Prioritization: You can configure prioritization for flows, ensuring critical tasks run on high-priority machines. If you need step-by-step guides or code examples, you can refer to the specific documentation links for Google Cloud Run and serverless infrastructure guides. If you have further questions, feel free to ask. Alternatively, is there anything specific you'd like to delve into?

@Marvin okay, but it let start workers on both local and on cloud, but actual execution is performed always on serverless infra, i want to execute the job locally if possible

To ensure that jobs are executed locally if possible and only run on serverless infrastructure when local capacity is unavailable, you can prioritize your work queues and workers accordingly. Here’s how you can set this up: ### Key Concepts from Documentation: 1. Work Queues and Worker Priority: - Work queues can be prioritized to ensure that higher priority queues are serviced first. This is ideal to ensure that local resources are used before falling back to cloud resources. - You can start a local worker and configure it to use a specific work pool. - When local workers are available, they will pick up the jobs from the high-priority work queues. If no local workers are available, jobs will then be picked up by the cloud-based workers. Work Pools Concept 2. Polling for Work: - Workers poll for work every 15 seconds by default. This interval is configurable. - This ensures timely job execution and minimizes the delay in picking up jobs from the queue. ### Implementation Steps: 1. Create Local and Cloud Work Pools: - Define work pools for both local and cloud environments. - Ensure that your deployment’s infrastructure block supports execution in both environments. 2. Set Up Work Queues: - Create work queues with priorities. Assign higher priority to local execution environments. - Use CLI or UI to manage queue statuses (e.g., READY, PAUSED). 3. Start Local Workers: - Start a local worker using:

bash
     prefect worker start -p my-local-machine

- Assign the worker to the appropriate work pool. 4. Configure Cloud Workers: - Set up cloud workers for fallback by configuring a work pool with cloud-based infrastructure (e.g., Google Cloud Run). - Ensure that cloud workers are in a lower-priority queue compared to local workers. ### Example Configuration:

yaml
work_pools:
  my-local-pool:
    queues:
      - name: local-high-priority
        priority: 1

  my-cloud-pool:
    queues:
      - name: cloud-low-priority
        priority: 2

### Adjustments and Monitoring: - Increase Polling Frequency: Adjust the polling frequency if needed using the

PREFECT_WORKER_QUERY_SECONDS

setting. - Monitor and Debug: Temporarily redirect jobs to local machines for debugging:

bash
  prefect worker start -p my-debugging-machine

These steps should ensure that your flow runs will prioritize local execution and only fall back to serverless infrastructure when necessary. For detailed setup and advanced configurations, refer to the work pools documentation and serverless worker guide.

@Marvin how to You can start a local worker and configure it to use a specific work pool.?

To start a local worker and configure it to use a specific work pool in Prefect, you will need to follow these steps: ### Steps to Start a Local Worker with Specific Work Pool 1. Open a Terminal: Make sure that your virtual environment has Prefect installed and activated. 2. Create or Select a Work Pool: Use the Prefect UI or CLI to create or ensure your desired work pool exists. 3. Start the Worker: Use the following command to start a local worker and assign it to a specific work pool:

bash
   prefect worker start --pool my-local-pool

### Example Setup 1. Creating a Work Pool (if not already created): You can create a work pool via the Prefect UI or using the Prefect CLI.

bash
   prefect work-pool create my-local-pool

2. Starting the Worker: Run the following command in your terminal to start a worker and assign it to the

my-local-pool

.

bash
   prefect worker start --pool my-local-pool

This command will output logs indicating that the worker is polling the Prefect API for scheduled flow runs. ### CLI Commands and Options - Inspect a Work Pool:

bash
  prefect work-pool inspect my-local-pool

- Update a Work Pool:

bash
  prefect work-pool update my-local-pool --description "Local work pool for testing"

- Set a Default Work Pool for Deployments:

bash
  prefect config set PREFECT_DEFAULT_WORK_POOL_NAME=my-local-pool

### Example Usage in a Flow Here's an example of how you might define and deploy a flow to use this work pool:

python
from prefect import flow, task

@task
def add(x, y):
    return x + y

@flow
def my_flow():
    result = add(1, 2)
    print(f"Result: {result}")

if __name__ == "__main__":
    my_flow()

### Running the Flow - Register and Deploy the Flow:

bash
  prefect deployment build my_flow.py:my_flow --name my_flow_deployment --work-pool my-local-pool -a

- Monitor Execution: Ensure your local worker is running and monitor the Prefect UI to see your flow being picked up and executed. For more details, you can access the Prefect Worker Tutorial and Work Pools Concept Documentation. This setup ensures that the worker running on your local machine will prioritize taking up the tasks from the specified work pool, as long as it is active. If the local worker is not available, other configured workers (e.g., on cloud infrastructure) can pick up the tasks.

@Marvin but i cannot link a deplyment to both local work pool and serverless work pool

You're correct that a deployment typically links to a single work pool. However, you can create a more flexible setup by leveraging multiple work pools and configuring your worker priorities to ensure local execution first if available, and serverless execution as a fallback. Here’s how you can achieve this behavior: ### Steps to Link Deployments to Multiple Work Pools 1. Create Multiple Work Pools: Create a work pool for local execution and another for serverless execution.

bash
   prefect work-pool create local-pool
   prefect work-pool create serverless-pool

2. Create Multiple Work Queues within Work Pools: You can assign different priorities to the work queues in the work pools to control which jobs are picked up first.

bash
   prefect work-queue create local-high-priority --pool local-pool --priority 1
   prefect work-queue create serverless-low-priority --pool serverless-pool --priority 2

3. Deploy Your Flow to Both Work Pools: Create separate deployments for each work pool.

bash
   prefect deployment build path/to/flow.py:flow_name --name local-deployment --work-pool local-pool -a
   prefect deployment build path/to/flow.py:flow_name --name serverless-deployment --work-pool serverless-pool -a

4. Configure Workers: - Start Local Worker:

bash
     prefect worker start --pool local-pool

- Start Serverless Worker (as a fallback): Follow the specific instructions for your serverless platform, e.g., AWS ECS, Azure Container Instances, or Google Cloud Run. 5. Execution Priority: - Ensure that local workers are started with higher priority work queues. - When a flow run is scheduled, it will be picked up by the local worker first. If the local worker is unavailable, the serverless worker will then pick it up. ### Example Commands - Start Local Worker:

bash
  prefect worker start --pool local-pool

- Start Serverless Worker: E.g., for Google Cloud Run

bash
  prefect worker start --pool serverless-pool

### Management in UI In the Prefect UI, you can manage the work pools, work queues, and monitor the status of workers. Make sure your local pool has higher priority, and the serverless pool is the fallback option. ### Monitoring in Prefect UI - Ensure your deployments are linked to their respective work pools. - Monitor the work queues to see which worker picks up the flow runs. - Adjust the settings if required to fine-tune the behavior. By utilizing multiple work pools and prioritizing work queues, you can ensure that your deployments get picked up by local workers first and fall back to serverless workers only when necessary, achieving the desired hybrid execution setup. For more configuration details and advanced setup, refer to the Prefect Work Pools Concept Documentation and the Worker Tutorial.

@Marvin can you describe better my issue so i can share it within the prefect community?

Absolutely, here’s a description you can share with the Prefect community to get more targeted assistance: --- ### Issue: Hybrid Execution Environment Management in Prefect #### Context: I am currently orchestrating workflows using Prefect and aiming to utilize both local infrastructure and serverless cloud solutions for executing my flows. Specifically, I want my deployments to prioritize execution on local machines when available, and only fall back to serverless cloud infrastructure if local resources are unavailable. #### Detailed Description: 1. Work Pools Setup: - I have set up two separate work pools: one for local execution (

local-pool

) and another for serverless execution (

serverless-pool

). - Created work queues in these pools with differing priorities, e.g.,

local-high-priority

and

serverless-low-priority

. 2. Worker Configuration: - Local workers are started and configured to poll the

local-pool

with high priority. - Cloud workers (e.g., Google Cloud Run) are configured to poll the

serverless-pool

with lower priority, acting as a fallback. 3. Deployment: - Deployments are made separately to each work pool:

bash
      prefect deployment build path/to/flow.py:flow_name --name local-deployment --work-pool local-pool -a
      prefect deployment build path/to/flow.py:flow_name --name serverless-deployment --work-pool serverless-pool -a

#### Issue: Currently, I cannot link a single deployment to both the local and serverless work pools directly. This restriction makes it challenging to seamlessly prioritize local execution and fall back to serverless execution within the same deployment workflow. #### Desired Outcome: I want a deployment configuration that: 1. Ensures that the deployed flow runs locally if local workers are available. 2. Automatically falls back to serverless cloud workers only if no local workers are available. #### Questions: 1. Is there a way to configure a single deployment to be picked up by multiple work pools? 2. How can I ensure that local execution is always prioritized before falling back to serverless execution without having to manage multiple deployments manually? Any guidance or advice on configuring Prefect to achieve this hybrid execution setup would be greatly appreciated. Thank you! --- Feel free to adjust any specifics before sharing. You can post this in the Prefect Discourse community, Prefect Slack channel, or any relevant forum where Prefect experts can provide further assistance.