@Marvin how do I check if the current run is auto scheduled or manually triggered?

It appears there's no direct documentation or existing GitHub issues that specifically address checking if a run is auto-scheduled or manually triggered. However, you can infer this by examining the metadata of the flow run. Auto-scheduled runs are typically created by the Scheduler service, whereas manually triggered runs usually do not have this metadata. You might consider using an automation or a metadata tag when you manually trigger runs and check for the presence of such tags to distinguish between the two. For more detailed information, you might check the Prefect documentation here: https://docs.prefect.io/latest/concepts/schedules/ https://docs.prefect.io/latest/concepts/automations/ If this is a frequent necessity, you might want to create a feature request or seek more specific guidance on the Prefect GitHub repository.

@Marvin how can I make not stop a flow if a task raises exception?

To ensure that a flow does not stop if a task raises an exception in Prefect, you can handle exceptions within the task and return a state indicative of failure without disrupting the entire flow. Here's a basic example:

python
from prefect import task, flow
from prefect.states import Failed

@task
def always_fails_task():
    try:
        raise ValueError("I am a bad task")
    except ValueError as e:
        return Failed(message=str(e))

@task
def always_succeeds_task():
    return "foo"

@flow
def example_flow():
    fail_result = always_fails_task()
    success_result = always_succeeds_task()

example_flow()

In this example: - The

always_fails_task

raises and catches the exception, then returns a

Failed

state. - This prevents the task's failure from stopping the flow, allowing subsequent tasks to execute. For more detailed information, refer to the Prefect documentation: https://docs.prefect.io/latest/concepts/flows/ If you need more specific examples or additional help, searching the Prefect GitHub repository might provide further insights.

@Marvin how do I read deployments filtering by deployment name?

To read and filter deployments by their deployment name in Prefect, you can use the Prefect CLI with specific naming patterns. Here are some useful commands: 1. Deploy Specific Deployment by Name:

sh
   prefect deploy --name my_flow/deployment-1 --name my_other_flow/deployment-1

2. Deploy All Deployments:

sh
   prefect deploy --all

3. Deploy Deployments Matching a Pattern:

sh
   prefect deploy -n my-flow/* -n *dev/my-deployment -n dep*prod

The above command will deploy: - All deployments from the flow named

my-flow

- All flows ending in

dev

with a deployment named

my-deployment

- All deployments starting with

dep

and ending in

prod

For more detailed guidance, you can refer to the Prefect documentation on deploy commands: Prefect Deployment Guide

@Marvin use python

To filter and read deployments by deployment name using Python, you'll need to interact with the Prefect API. Here's an example of how you might do this:

python
from prefect.client import get_client

async def get_deployments_by_name(deployment_name):
    async with get_client() as client:
        deployments = await client.read_deployments()
        filtered_deployments = [
            dep for dep in deployments if deployment_name in dep.name
        ]
        return filtered_deployments

# Example usage
deployment_name_to_filter = "my-deployment-name"
deployments = asyncio.run(get_deployments_by_name(deployment_name_to_filter))
for dep in deployments:
    print(dep)

Here’s what the code does: 1. Connect to Prefect Client: Access the Prefect API client. 2. Fetch Deployments: Read all deployments. 3. Filter by Name: Filter out the deployments that match the specified deployment name. For a more detailed guide, you might want to explore Prefect's official documentation or examples: Basic Flow Deployment This example should give you a solid starting point to customize further based on your requirements.

@Marvin can I pass the deployment name to the api instead of filtering locally?

It seems there are no direct results for filtering deployments by name via the Prefect API directly. However, Prefect provides extensive support for API interactions which likely includes filtering capabilities. Based on typical REST API patterns, you can achieve your goal by querying the API with specific parameters. Here's a hypothetical example using

httpx

to interact with the Prefect API:

python
import httpx

async def get_deployments_by_name(deployment_name):
    async with httpx.AsyncClient() as client:
        response = await client.get(
            "<https://api.prefect.io/deployments/filter>",
            params={"name": deployment_name}
        )
        response.raise_for_status()
        return response.json()

# Example usage
deployment_name_to_filter = "my-deployment-name"
deployments = asyncio.run(get_deployments_by_name(deployment_name_to_filter))
for dep in deployments:
    print(dep)

You would need to replace

"<https://api.prefect.io/deployments/filter>"

with the actual endpoint and parameters supported by the Prefect API. For more specific and precise information, refer to the official Prefect API documentation or Prefect Documentation. If this doesn't directly apply, you can always perform local filtering after retrieving the data initially, as described earlier.

@Marvin how do I make a task failing

To make a task fail deliberately in Prefect, you can have the task raise an exception. Here’s a concise example demonstrating how to do this:

python
from prefect import task, flow

@task
def always_fails_task():
    raise ValueError("I fail successfully")

@task
def always_succeeds_task():
    return "foo"

@flow
def example_flow():
    try:
        fail_result = always_fails_task()
    except ValueError as e:
        print(f"Task failed with error: {e}")
    success_result = always_succeeds_task()
    print(success_result)

example_flow()

In this example: -

always_fails_task

is designed to fail by raising a

ValueError

. - The flow captures the failure and prints an error message without halting the execution of subsequent tasks. For more detailed information on working with task failures, you can refer to the Prefect 2.0 Concepts on Flows.

@Marvin how can I make a task failing in order to make a dependant task not being executed?

To ensure that a dependent task does not execute if another task fails, you can rely on Prefect's default behavior of handling dependencies. When a task fails, any tasks that depend on its output will not be executed. Here's an example:

python
from prefect import task, flow

@task
def task_that_fails():
    raise ValueError("This task fails")

@task
def dependent_task():
    print("This task should not run if the previous task fails")

@flow
def example_flow():
    result = task_that_fails()
    dependent_task.wait_for(result)

example_flow()

In this example: -

task_that_fails

is intentionally designed to fail by raising a

ValueError

. -

dependent_task

uses

wait_for(result)

to specify it should only run if

task_that_fails

completes successfully. For more detailed guidance, you can refer to: Prefect 2.0 Concepts on Flows. This ensures that any task with dependencies on the `task_that_fails`'s output will not execute if the preceding task fails.

@Marvin why the whole flow stops if a task raises an exception?

The behavior where the whole flow stops if a task raises an exception is linked to how Prefect handles task failures and the propagation of states. Prefect's engine processes tasks in a step-by-step manner, where each step can return a state or raise an

ENDRUN

exception, which halts further execution. Here's a simplified explanation based on the refactor mentioned in the TaskRunner refactor: 1. Task Runner Steps: The

TaskRunner

processes tasks through a sequence of steps. Each step checks the state and decides whether to proceed or halt. 2. ENDRUN Exception: If a task raises an exception, an

ENDRUN

exception might be triggered, stopping further task execution. 3. Flow Propagation: When a task fails and raises an exception, the flow might stop executing subsequent tasks, especially if they depend on the failed task's results. To change this behavior: - Retry Policies: Define retry policies for tasks that might transiently fail. - Custom State Handlers: Implement custom state handlers to manage task failures without halting the entire flow. For more details, you can refer to: 1. TaskRunner Refactor By understanding and utilizing these mechanisms, you can better control the behavior of your flows in Prefect.

@marvin how do I restart a cancelling flow run?

To restart a flow run that is in the process of being cancelled, you typically need to wait until it is fully cancelled and then manually initiate a new run. Here's how you can manage this scenario: 1. Check Flow Run Status: Ensure the flow run has actually cancelled. 2. Manual Restart: Manually trigger a new run using the Prefect API or UI. Here’s a simple example using Prefect's REST API with

httpx

to restart a cancelled flow run:

python
import httpx
import asyncio

# Define your Prefect API URL
PREFECT_API_URL = "<https://api.prefect.io>"

async def restart_flow_run(flow_run_id):
    async with httpx.AsyncClient() as client:
        # Get the flow run details
        response = await client.get(f"{PREFECT_API_URL}/flow_runs/{flow_run_id}")
        response.raise_for_status()
        flow_run = response.json()

        # Ensure the flow run is cancelled
        if flow_run['state'] == 'Cancelled':
            # Start a new flow run
            start_new_run_response = await <http://client.post|client.post>(
                f"{PREFECT_API_URL}/flow_runs",
                json={"flow_id": flow_run['flow_id']}
            )
            start_new_run_response.raise_for_status()
            return start_new_run_response.json()
        else:
            raise Exception("Flow run is not in a cancelled state")

# Example usage
flow_run_id_to_restart = "your-flow-run-id"
new_flow_run = asyncio.run(restart_flow_run(flow_run_id_to_restart))
print(f"New flow run started: {new_flow_run['id']}")

### Steps Explained: 1. Check Flow Run Status: Confirm the flow run is actually cancelled. 2. Start New Run: Trigger a new flow run if the previous one was cancelled. This approach ensures you handle the transition smoothly. For more details on handling cancellations and other advanced flow run operations, you can refer to the Prefect Flow Concept Documentation.

@Marvin how can I run a task from another task?

As of Prefect 2.18.x, you can run a task from within another task. Here's a concise example demonstrating how to achieve this:

python
from prefect import task, flow

@task
def my_task():
    print("Hello, I'm a task")

@task(log_prints=True)
def my_parent_task():
    my_task()

@flow
def my_flow():
    my_parent_task()

my_flow()

### Explanation: - Task Definition:

my_task

is a simple task that prints a message. - Nested Task Execution:

my_parent_task

calls

my_task

within its body. - Flow Execution:

my_flow

triggers the

my_parent_task

, which in turn calls

my_task

. This structure allows you to organize and manage complex workflows by nesting tasks as needed. For more detailed information on task nesting and execution, you can refer to the Prefect Tasks Documentation.

@Marvin can I exclude a task from concurring to define the flow run state?

can I exclude a task from concurring to define the flow run state? To exclude a task from contributing to the overall state of a flow run in Prefect, you can use the concept of subflows or intentionally design the task to be non-critical to the flow's success. Here's an approach you can take using subflows: ### Example Using Subflows

python
from prefect import task, flow

@task
def non_critical_task():
    raise ValueError("This task fails but should not affect the main flow state")

@task
def critical_task():
    return "This is a critical task result"

@flow
def subflow_with_non_critical_task():
    non_critical_task()

@flow
def main_flow():
    result = critical_task()
    
    # Run the subflow which includes a non-critical task
    subflow_with_non_critical_task()
    
    print(result)

main_flow()

### Explanation: - non_critical_task: This task raises an exception but is run within a subflow. - critical_task: A critical task whose state impacts the main flow's state. - subflow_with_non_critical_task: This subflow runs the non-critical task. - main_flow: The main flow runs the critical task and then the subflow. The state of

non_critical_task

does not impact the overall state of

main_flow

because it is encapsulated within a subflow. ### Using

return_state=False

You can also use the

return_state

parameter of the

end_run

task function to exclude a task's state from defining the flow's state. ### Reference: For more options and details, please refer to the official Prefect 2.0 Documentation on Flows. By designing your workflow this way, you can ensure certain tasks do not affect the overall state of your flow, allowing for more resilient and fault-tolerant pipelines.

@Marvin can I force a task state to complete?

To use the

get_client

function in Prefect and provide a specific URL and headers, you'll need to use Prefect's configuration and client setup functionalities. Below is a concise example of how you can do this:

python
import asyncio
from prefect.client import get_client
from prefect.context import PrefectContext

async def main():
    api_url = "<https://api.prefect.io>"
    headers = {
        "Authorization": "Bearer YOUR_API_KEY",
        "Custom-Header": "CustomValue"
    }

    async with get_client() as client:
        client._client.base_url = api_url
        client._client.headers.update(headers)
        
        # Perform a client operation (e.g., listing deployments)
        deployments = await client.read_deployments()
        print(deployments)

# Run the async function
asyncio.run(main())

### Explanation: 1. API URL and Headers: Define the URL and headers you want to use. 2. Get Client: Use the

get_client

function to retrieve a client instance. 3. Set Base URL and Update Headers: Update the

base_url

and

headers

of the client. 4. Client Operation: Perform any desired client operations (e.g.,

read_deployments

). By using the code above, you can customize the Prefect client to interact with a specific API endpoint and include any necessary headers. For further reference, you can consult the Prefect 2.0 Client Documentation.

@Marvin can I pass additional headers to get_client function?

You can indeed pass additional headers to the

get_client

function in Prefect by modifying the client's headers property. Here's an example on how to do this: ### Example

python
import asyncio
from prefect.client import get_client

async def main():
    api_url = "<https://api.prefect.io>"
    additional_headers = {
        "Authorization": "Bearer YOUR_API_KEY",
        "Custom-Header": "CustomValue"
    }

    async with get_client() as client:
        # Set base URL if needed
        client._client.base_url = api_url
        
        # Update headers with additional headers
        client._client.headers.update(additional_headers)
        
        # Example client operation: listing deployments
        deployments = await client.read_deployments()
        print(deployments)

# Run the async function
asyncio.run(main())

### Explanation: 1. API URL and Custom Headers: Define the URL and any additional headers required. 2. Get Client: Use

get_client

to retrieve a client instance. 3. Set Base URL and Headers: Modify the

base_url

and update the headers of the client instance. 4. Execute Client Operations: Perform your desired client operations, e.g.,

read_deployments

. This code sets up the Prefect client with a custom API URL and additional headers, ensuring that all requests from this client instance will include the specified headers. Refer to the Prefect 2.0 Documentation on Client for more details.