Based on the research and GitHub issues search, I can provide a clear answer about creating deployments within flows: 1. There are no documented issues or unexpected behaviors specifically related to creating deployments within a flow run in Prefect 3.x. 2. However, it's important to understand that deployments are primarily designed as server-side representations of flows, meant for: - Remote orchestration - Scheduling flow runs - Triggering flow runs through events 3. Best Practices: - If you need to create deployments programmatically (including within flows), use the modern pattern:

python
     flow.from_source(...).deploy(...)

- Ensure you have proper error handling when creating deployments programmatically - Consider whether creating deployments within flows aligns with your intended architecture, as deployments are typically set up as part of your CI/CD process rather than during flow execution 4. Alternative Patterns: - If you're looking to run one flow from another, consider using subflows (also called "nested flows") instead of creating deployments - If you need dynamic flow orchestration, consider using the Prefect REST API or CLI outside of your flow runs While there's no explicit prohibition against creating deployments within flows, it's worth considering whether this pattern serves your use case effectively. What specific scenario are you trying to address? This would help me provide more targeted guidance for your use case.

Based on the research, I can provide specific guidance about creating deployments to different work pools from within a flow. Here's what you need to know: 1. Technical Feasibility: - This is technically possible in Prefect 3.x using the

flow.from_source(...).deploy(...)

pattern - You can specify different work pools for each deployment you create 2. Important Considerations: - Ensure your flow has access to the Prefect API and proper permissions to create deployments - The source code for the flows being deployed must be accessible from where the deployments are being created - Each work pool might have different infrastructure requirements or configurations 3. Best Practice Example:

python
from prefect import flow

@flow
def deployment_creator():
    # Example of creating deployments to different work pools
    from prefect.flows import flow_from_source
    
    # Deploy to first work pool
    flow_from_source(
        source="<https://github.com/your-repo/flows.py>",
        entrypoint="flows.py:flow1"
    ).deploy(
        name="deployment-1",
        work_pool_name="pool-1"
    )
    
    # Deploy to second work pool
    flow_from_source(
        source="<https://github.com/your-repo/flows.py>",
        entrypoint="flows.py:flow2"
    ).deploy(
        name="deployment-2",
        work_pool_name="pool-2"
    )

4. Potential Gotchas: - Make sure your work pools are properly configured before creating deployments - Consider error handling for deployment creation failures - Be aware of any network connectivity requirements between your flow and the Prefect server - If you're running in Kubernetes, be mindful of potential pod eviction issues with long-running flows (as noted in the GitHub issues) 5. Alternative Approaches to Consider: - If you're doing this for workflow orchestration, consider using subflows instead - If you need different infrastructure for different parts of your workflow, consider using work pool queues - For CI/CD scenarios, consider moving deployment creation to your pipeline rather than within flows Could you share more about your specific use case? What's driving the need to create deployments to different work pools from within a flow? This would help me provide more targeted recommendations for your scenario.

Based on the research and reported issues, here's what happens when a worker stops mid-flow run and another worker starts: 1. Flow Run State Behavior: - When a worker stops abruptly, the flow run doesn't immediately fail - Prefect uses a heartbeat monitoring system (checks every 90 seconds by default) - After three missed heartbeats, the worker is considered offline 2. What Happens Next: - If the original worker stops: - The flow run will initially remain in its current state - After missing heartbeats, Prefect marks the worker as offline - The flow run can then be picked up by another worker in the same work pool 3. Important Considerations: - The exact behavior depends on several factors: - Where in the flow execution the worker stopped - The type of infrastructure being used (Kubernetes, Docker, etc.) - Whether the flow run was actively executing or in a pending state - There are known issues with flow runs potentially getting stuck in pending state after server restarts (as noted in GitHub issue #18324) 4. Best Practices to Handle Worker Transitions: - Implement proper error handling in your flows - Use task retries for critical operations - Consider implementing checkpointing for long-running flows - Example:

python
     from prefect import flow, task
     from prefect.tasks import task_input_hash
     
     @task(retries=3, cache_key_fn=task_input_hash)
     def my_task():
         # Implementation with proper error handling
         pass
     
     @flow(retries=2)
     def my_flow():
         try:
             result = my_task()
             # Additional flow logic
         except Exception as e:
             # Handle exceptions
             raise

5. Potential Issues to Watch For: - Flow runs might get stuck in a pending state after server restarts - With worker concurrency limits, stuck flows can block the queue - Some state transitions might not be handled perfectly during worker failover 6. Recommendations: - Monitor your flows and workers actively - Implement proper logging to track state transitions - Consider implementing health checks in your flows - Set appropriate timeouts and retry policies - Use concurrency limits carefully to avoid queue blockage Would you like me to elaborate on any of these points or provide more specific guidance for your particular use case? Are you experiencing any specific issues with worker transitions in your environment?

Based on the research and module exploration, I can confirm that in Prefect 3.x, there are a few ways to set a flow run to a pending state: 1. Using the `PrefectClient`:

python
from prefect.client import get_client
from prefect.states import Pending

async with get_client() as client:
    await client.set_flow_run_state(
        flow_run_id="your-flow-run-id",
        state=Pending()
    )

2. Using state transitions within a flow:

python
from prefect import flow
from prefect.states import Pending

@flow
def my_flow():
    return Pending(message="Flow run set to pending")

3. For more complex state management, you can use the orchestration API:

python
from prefect.client import get_client
from prefect.states import Pending, to_state_create

async with get_client() as client:
    state = to_state_create(Pending(message="Custom pending message"))
    await client.create_flow_run_state(
        flow_run_id="your-flow-run-id",
        state=state
    )

Important Notes: - The

Pending

state is one of the built-in state types in Prefect - State changes should be handled carefully as they can affect flow orchestration - When setting states manually, consider the implications for worker behavior and flow run tracking - The state change will be recorded in the flow run's history Would you like me to provide more details about any specific aspect of state management or explain how this fits into your use case?

Based on the research, I can provide a clear answer about workers and scheduled deployments: No, you do not need to start a worker daily for a daily scheduled deployment. Here's why: 1. Worker Lifecycle: - Workers are designed to run continuously - They constantly poll for incoming flow runs, including scheduled ones - Once started, a worker will keep running indefinitely unless explicitly stopped or configured otherwise 2. Best Practices: - Start the worker once and let it run continuously - The worker will automatically pick up scheduled runs when they're due - Example of starting a worker:

bash
     prefect worker start -p "your-work-pool-name"

3. Important Considerations: - The worker must be running at the time the scheduled deployment is due to execute - If the worker is down when a scheduled run is due: - The run will not execute until a worker becomes available - Once a worker is available, it will pick up any pending runs - Monitor worker health through heartbeats (default 90-second intervals) 4. Recommendations: - Set up monitoring for your workers to ensure they stay healthy - Consider running multiple workers for redundancy - Use process managers or container orchestration platforms (like Kubernetes) to ensure workers stay running - If running in production, consider using service management tools to auto-restart workers if they fail Would you like me to provide more specific details about worker configuration or deployment scheduling options for your use case?

Based on the research, yes, you can create event-based deployments with multiple conditions, including both flow completion and custom variable conditions. Here's how you can do it: 1. Using Automation Rules:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.server.schemas.schedules import DeploymentSchedule

@flow
def your_flow():
    # Your flow logic here
    pass

# Create the deployment with event trigger
deployment = Deployment(
    name="conditional-event-deployment",
    flow=your_flow,
    triggers=[
        DeploymentSchedule(
            # Check for flow completion
            match={
                "prefect.flow-run.Completed": {
                    "name": "upstream-flow-name"  # Name of the flow you're waiting for
                }
            },
            # Add custom condition
            expect={
                "your.custom.event": {
                    "value": "k"  # Your custom condition
                }
            }
        )
    ]
)

2. Using Event Filters:

python
from prefect.deployments import Deployment
from prefect.events import Event

# Create deployment with multiple conditions
deployment = flow.from_source(
    source="your_source",
    name="conditional-deployment"
).apply_filter(
    # Check for flow completion
    "prefect.flow-run.Completed[name='upstream-flow-name']"
).apply_filter(
    # Check custom condition
    "your.custom.event[value='k']"
)

# Deploy
deployment.deploy()

3. Using Event Grammar:

python
from prefect import flow
from prefect.events import Event

@flow
def trigger_flow():
    return Event(
        event="your.custom.event",
        resource={
            "flow_name": "upstream-flow-name",
            "value": "k"
        }
    )

# In your deployment configuration:
triggers = {
    "and": [
        {"event": "prefect.flow-run.Completed",
         "expect": {"flow_name": "upstream-flow-name"}},
        {"event": "your.custom.event",
         "expect": {"value": "k"}}
    ]
}

4. Important Considerations: - Events are evaluated in real-time when they occur - Both conditions must be met for the deployment to trigger - You can use more complex conditions using event grammar - Consider using event persistence if needed for longer-term evaluation - Make sure your worker is running to handle the triggered deployments 5. Best Practices: - Use clear, descriptive event names - Include error handling for event processing - Consider timeouts for event waiting - Monitor event triggers through the Prefect UI - Test complex conditions thoroughly Here's a complete example putting it all together:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.events import Event

# Your flow that will be triggered
@flow
def conditional_flow(value):
    print(f"Flow triggered with value: {value}")

# Create deployment with multiple conditions
deployment = Deployment(
    name="conditional-deployment",
    flow=conditional_flow,
    triggers=[
        DeploymentSchedule(
            match={
                # Check for upstream flow completion
                "prefect.flow-run.Completed": {
                    "name": "upstream-flow-name"
                },
                # Check custom condition
                "your.custom.event": {
                    "value": "k"
                }
            }
        )
    ]
)

# Deploy
deployment.apply()

Would you like me to elaborate on any of these approaches or provide more specific examples for your use case? For instance, I could show how to handle more complex conditions or how to implement specific types of event monitoring.

Based on the CLI commands available and documentation, here's how you can view active workers in a work pool: 1. The primary command to inspect a work pool and its workers is:

bash
prefect work-pool inspect "your-work-pool-name"

This will show you detailed information about the work pool, including its active workers. 2. For a broader view of all work pools and their status:

bash
prefect work-pool ls

3. If you want to see more detailed information about the workers, you can use the

--json

flag:

bash
prefect work-pool inspect "your-work-pool-name" --json

Important Notes: - Workers are considered "active" if they've sent a heartbeat within the last 90 seconds - The inspect command will show you: - Number of active workers - Worker status - Last heartbeat times - Worker configurations - If you don't see any workers, it might mean: - No workers are currently running - Workers have lost connection - Workers have stopped sending heartbeats Would you like me to provide more specific details about interpreting the worker status information or additional commands for managing workers?

Based on the research and available documentation, I can provide a clear answer about flows and subflows in relation to workers: 1. Same Worker Execution: - By default, a subflow runs in the same process as its parent flow - This means that subflows automatically execute on the same worker as their parent flow - No separate worker is required for subflows 2. Process Behavior:

python
@flow
def parent_flow():
    # This runs on the worker that picked up the parent flow
    result = subflow()  # This also runs on the same worker
    return result

@flow
def subflow():
    return "Hello from subflow"

3. Important Considerations: - Subflows inherit the execution environment of their parent flow - They share the same Python process and resources - They have access to the same environment variables and dependencies - Memory and CPU resources are shared between parent and subflow 4. Exceptions to This Rule: - If you explicitly deploy a subflow as a separate deployment to a different work pool - If you use remote execution patterns (like calling a deployed flow instead of a subflow) - Example of when you would need separate workers:

python
     from prefect import flow, deploy
     
     @flow
     def parent_flow():
         # This would require a worker for the different work pool
         deployed_flow.from_source(...).deploy(work_pool_name="different-pool")

5. Best Practices: - Use subflows when you want to keep execution in the same environment - Use separate deployments when you need different infrastructure or isolation - Consider resource requirements when deciding between subflows and separate deployments Would you like me to elaborate on any of these points or provide more specific examples for your use case?