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<@ULVA73B9P> Can you please show me an updated exa...
# ask-marvin
j
@Marvin Can you please show me an updated example of how to use run_deployment() in v3, printing the result of the deployment?
m
To use the 
run_deployment()
function in Prefect 3.x and print the result, you can follow a simple setup. Here's an example that captures the essence of how to use this function in your code: 1. Setting Up the Deployment: Begin by defining a flow in a Python script. Here’s a basic setup for a flow:
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python
   import time
   from prefect import flow

   @flow
   def simple_flow():
       time.sleep(5)
       print("Flow finished running!")

   if __name__ == "__main__":
       simple_deploy = simple_flow.to_deployment(name="simple_example")
       serve(simple_deploy)
Deploy this using a setup such as Docker Compose to ensure your Prefect server and worker are ready to go. 2. Invoking the Deployment: In another script, utilize the 
run_deployment()
method to trigger the deployed flow. Here's the code snippet for this:
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python
   from prefect.deployments import run_deployment

   # Run the deployment and capture the result
   deployment_result = run_deployment('simple-flow/simple_example', timeout=0)

   # Print the deployment result
   print(deployment_result)
Ensure that your Prefect server is running and configured correctly, as the function will interact with the server's API to schedule the flow run. 3. Troubleshooting: If you notice delays or unexpected transitions between different states (e.g., from 
SCHEDULED
to 
PENDING
), consider adjusting the polling interval as mentioned in one of the issues, like setting 
PREFECT_RUNNER_POLL_FREQUENCY=1
for a more frequent check of queued flows. For further reading and more detailed explanations, you might want to check out these sources: - Prefect Deployment Documentation: https://docs-3.prefect.io/3.0/deploy/index - Related GitHub Issue Discussions here. If you find the flow of your program getting stuck like a procrastinating tea drinker on a Monday morning, some tweaks to your settings might be in order. Happy flow running!
j
@Marvin Now assume I want to run many deployments in parallel. Show me an example of how to run them and collect the results
m
To run multiple deployments in parallel using Prefect and collect their results, you can use the 
run_deployment()
function within a flow context. This approach allows you to efficiently manage and track each deployment. Below is an example to help you get started: 1. Define Individual Deployments: First, make sure each deployment is correctly set up and ready to be invoked. Assume you have multiple deployments named "deployment_a", "deployment_b", etc. 2. Create a Flow to Manage Deployments: Use a flow to run and manage these deployments in parallel. Here is an example script where multiple deployments are triggered simultaneously:
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python
   from prefect import flow, task
   from prefect.deployments import run_deployment
   import asyncio

   @task
   async def run_each_deployment(deployment_name):
       # Trigger the deployment
       result = await run_deployment(deployment_name, timeout=0)
       # Collect the result of the deployment
       return f"Result from {deployment_name}: {result}"

   @flow
   def run_multiple_deployments():
       deployment_names = ["deployment_a", "deployment_b", "deployment_c"]
       # Use asyncio.gather to run all deployments concurrently
       results = asyncio.run(asyncio.gather(*[run_each_deployment(name) for name in deployment_names]))
       # Print results of all deployments
       for result in results:
           print(result)

   if __name__ == "__main__":
       run_multiple_deployments()
3. Execution: Run this script in an environment where your Prefect server is accessible. The flow 
run_multiple_deployments
will trigger all specified deployments concurrently, and the results from each will be printed out. This setup utilizes 
asyncio
to handle concurrent execution of multiple deployments and collects their results once completed. For further reading and detail, you might find this ControlFlow documentation on task orchestration helpful. May your deployments run as effortlessly as a robot navigating the vast emptiness of space!
j
@Marvin now show me a similar example, but running everything inside a single flow, without additional tasks
m
To run multiple deployments in parallel within a single flow without using additional task functions, you can straightforwardly structure your flow to call 
run_deployment()
directly. Here’s how you could do it: 1. Setup: Ensure that each deployment you want to run is defined and accessible by its name. 2. Single Flow Execution: Create a flow that directly triggers each deployment using the 
run_deployment()
method.
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python
   from prefect import flow
   from prefect.deployments import run_deployment
   import asyncio

   @flow
   async def run_multiple_deployments_in_flow():
       deployment_names = ["deployment_a", "deployment_b", "deployment_c"]
       
       # Use asyncio.gather to run all deployments concurrently
       results = await asyncio.gather(
           *[run_deployment(name, timeout=0) for name in deployment_names]
       )
       
       # Print the results
       for name, result in zip(deployment_names, results):
           print(f"Result from {name}: {result}")

   if __name__ == "__main__":
       asyncio.run(run_multiple_deployments_in_flow())
3. Execution and Output: - Ensure your environment is correctly set up with access to the Prefect server. - Run the script. It will execute all specified deployments concurrently and print their results after completion. This example directly integrates the deployment runs within a flow, leveraging 
asyncio.gather()
for parallel execution. It's a neat and direct approach to handle multiple deployments without intermediary task functions, allowing the flow itself to manage and await the concurrent operations. And there you have it! All the power and simplicity of concurrency, wrapped up in one tidy flow, like Marvin contemplating the meaning of existence—uh, but with more immediate results!
j
@Marvin can you explain why timeout=0?
m
Using 
timeout=0
in the 
run_deployment()
function is a specific setting that indicates you don't want to wait for the deployment to complete when you start it. Essentially, it kicks off the deployment but returns immediately, allowing the flow to continue executing without blocking. Here’s why you might use `timeout=0`: 1. Non-blocking Execution: By setting 
timeout=0
, you can initiate multiple deployments concurrently without waiting for each to finish before moving on to the next. This is particularly useful when you're running multiple deployments in parallel, as it allows all deployments to start their execution immediately. 2. Immediate Return: It tells Prefect to start the deployment and immediately return control to your flow. The flow effectively initiates the deployments and then gathers the results as they become available. 3. Efficiency: This approach allows for more efficient use of resources. If your deployments involve long-running processes or workflows, this setting ensures that your flow isn’t idly waiting for each one to complete sequentially. Instead, the flow can manage and oversee the parallel executions, collecting results upon completion. This setting is particularly beneficial in scenarios where you want to fire off multiple independent processes and handle their completion later, making it highly efficient for concurrent workload management. So, in essence, it lets you be as quick and efficient as that robot arm that always knows how to pour a perfect cup of tea without ever spilling a drop!
j
@Marvin in the example above, results = await asyncio.gather() , result is a FlowRun() object. How do I obtain its result? I am expecting a boolean
m
To obtain the final result of a 
FlowRun
object that you initiated using the 
run_deployment
function and expect a boolean result, you'll need to check the state of the flow run. Here's a simple way to do it: 1. Check the FlowRun State: - A 
FlowRun
object has several possible states, such as 
COMPLETED
, 
FAILED
, or 
CRASHED
. - To determine a boolean success value, you would typically check if the flow run reached a 
COMPLETED
state. 2. Access the Result from the State: - Prefect uses a state management model, where the completion state indicates the success or failure of the flow run. - Here’s an example of how you might achieve this:
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python
from prefect.client import get_client
from prefect.states import StateType

async def get_boolean_result_for_flow_run(flow_run_id):
    # Get the Prefect client
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        
        # Check the flow run's state
        if flow_run.state.type == StateType.COMPLETED:
            # The flow run completed successfully
            return True
        elif flow_run.state.type == StateType.FAILED:
            # The flow run failed
            return False
        else:
            # Handle other states as needed
            return False
            
# Example usage within an asyncio event loop or flow
# boolean_results = await get_boolean_result_for_flow_run(your_flow_run_id)
3. Blocking with 
.result()
: - If you needed to block execution to wait for a task run result, the 
.result()
method could be used; however, we usually deal with 
FlowRun
objects differently, focusing on their state instead. This approach will give you a straightforward boolean result indicating success or failure based on the 
COMPLETED
or 
FAILED
state of the flow. It's like asking a really complex question and getting a simple "yes" or "no" from the universe, but, you know, without expecting existential crises in response!
j
@Marvin What if I use result()? Show me an example
m
Using 
.result()
can be a bit tricky with 
FlowRun
objects when you're dealing with Prefect, because 
.result()
is typically associated with the task execution context. However, if your deployment includes tasks that return specific values, and you want to capture those results, you can retrieve them through the completion states and task outputs. Here’s how you can handle it: 1. Use of 
.result()
on Task Level: - 
.result()
is used to block and wait for the task to complete and to retrieve its return value when dealing with task objects. 2. Example: Let's assume each deployment ends with a task returning a boolean value:
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python
from prefect import flow, task
from prefect.deployments import run_deployment
import asyncio

@task
def boolean_task():
    # Some logic
    return True

@flow
async def run_multiple_deployments_and_get_results():
    deployment_names = ["deployment_a", "deployment_b", "deployment_c"]
    
    def get_result(flow_run):
        # Assuming flow_run includes a finished task with a result
        return flow_run.result().result()
    
    # Run deployments concurrently
    async with asyncio.gather(
        *[run_deployment(name, timeout=0) for name in deployment_names]
    ) as flow_run_futures:
        results = [get_result(flow_run) for flow_run in flow_run_futures]
    
    # Print results
    for name, result in zip(deployment_names, results):
        print(f"Result from {name}: {result}")

if __name__ == "__main__":
    asyncio.run(run_multiple_deployments_and_get_results())
3. Execution Flow: - The 
boolean_task
task returns a boolean. - We trigger multiple deployments and anticipate each concludes by running a task that captures the desired info. - The 
get_result()
function attempts to extract the outcome using 
.result()
on the flow run, capturing the task's output if accessible. In this example, if you want to capture task outputs from a deployment, ensure that tasks within the flow return values explicitly, and you know how to access those results through Prefect's client and state management system. It might feel like catching a playful cat… sometimes a bit elusive, but with patience, you can certainly get there!
j
@Marvin sometimes in a similar situation, I get this error. Do you have any guess of how to solve it? TypeError: unhashable type: 'FlowRun'
m
Ah, the notorious 
TypeError: unhashable type: 'FlowRun'
! This usually occurs when you're trying to use a 
FlowRun
object as a key in a dictionary or add it to a set, both of which require the elements to be hashable. Immutable types like strings and numbers are hashable, but mutable types like lists, dictionaries, and custom classes without a defined 
__hash__
method usually are not. Here’s a breakdown of how to solve or work around this: 1. Avoid Using 
FlowRun
in Sets or as Dictionary Keys: - If you inadvertently try to use a 
FlowRun
object in a set or as a key in a dictionary, it will raise this error. Ensure that you're using a more stable identifier, like the 
flow_run.id
or another unique attribute from the 
FlowRun
. 2. Use IDs Instead: - If you're maintaining a collection or mapping of flow runs, use their IDs or some other unique, immutable attribute:
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python
   flow_runs = [run_deployment(name, timeout=0) for name in deployment_names]
   results_dict = {flow_run.id: None for flow_run in flow_runs}
3. Check Dependencies: - Ensure all your operations with 
FlowRun
don't accidentally attempt to store them in such collections directly. 4. Accessor Methods: - If you're extending or modifying flow run outputs, use accessor methods that fetch attributes or other primitive types, reducing errors when manipulating these objects. 5. Custom Hashing: - If you absolutely need to store 
FlowRun
instances in a context requiring hashing, you can write a wrapper class. However, this approach should be reserved for cases with no alternatives. While 
FlowRun
may not enjoy the existential angst of an android pondering the meaning of life, it does require careful handling to avoid such errors. Happy coding, and may your objects always be properly hashable when needed!
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