@Marvin can I run thousands of flows in parallel? What are the limits?

In both Prefect 2.x and 3.x, you can run multiple flows in parallel, but there are mechanisms to manage and limit concurrency. ### Prefect 2.x You can use the

serve

utility to run multiple flows in parallel within the same process. To control and limit the concurrency, you may use the concept of concurrency limits, which you can manage via the UI or CLI. These limits control the maximum number of concurrent flow runs. Prefect 2.x Serving Multiple Flows Prefect 2.x Concurrency Limits ### Prefect 3.x Similarly, Prefect 3.x provides capabilities to deploy multiple flows concurrently using the

deploy

function. Concurrency limits can be set at the work pool and work queue levels to manage the number of concurrent flow runs. These limits can be managed via the Prefect CLI, API, or UI. Prefect 3.x Deploying Multiple Flows Prefect 3.x Task Run Limits The actual capacity to run thousands of flows simultaneously depends on your execution environment and infrastructure. It's advisable to consider these concurrency limits to optimize performance and resource use.

@Marvin how to run flow faster? Because run_deployment takes a couple of seconds to execute

Here are some strategies to make your flow runs faster in Prefect: ### Prefect 2.x 1. Optimize Task and Flow Code: - Ensure your tasks and flow code are optimized for performance. Avoid unnecessary computations and I/O operations. 2. Asynchronous API: - Use Prefect’s asynchronous client methods to manage and trigger flow runs more efficiently. This can reduce latency when scheduling tasks. - Example:

await client.create_flow_run_from_deployment(...)

. 3. Dynamic Infrastructure with Work Pools: - Configure your deployments and work pools to dynamically manage resources. Ensure that your infrastructure can autoscale to handle increased workloads. - Dynamic Infrastructure with Work Pools. ### Prefect 3.x 1. Optimize Deployment Configuration: - Use the

deploy

function to create optimized configurations for your deployments. Ensure they run with the necessary resources. - Example:

python
     from prefect import deploy, flow

     @flow
     def my_flow():
         ...

     deploy(
         my_flow.to_deployment(name="my-deploy", work_pool_name="my-pool")
     )

2. Use Work Pools: - Optimize your work pools to ensure efficient resource allocation. Use dynamic provisioning to only allocate resources when needed. - Work Pools in Prefect 3.x. 3. Asynchronous API: - Utilize asynchronous operations to handle flow runs. This helps in reducing the time it takes to schedule and start runs. - Example:

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

     async def reschedule_late_flow_runs(deployment_name):
         async with get_client() as client:
             flow_runs = await client.read_flow_runs(...)
             # Further processing...

These strategies should help optimize and speed up your flow runs in Prefect. Be sure to examine and optimize your code, infrastructure, and configurations to achieve the best performance. For more details, refer to the following resources: - Using the Prefect Client (2.x) - Work Pools (3.x) - Interact with API (3.x)

@Marvin can I setup concurrency limit for one .serve?

Yes, you can set up concurrency limits in both Prefect 2.x and 3.x for deployments, including when using the

.serve

method. Concurrency limits allow you to control the maximum number of concurrent flow runs. ### Prefect 2.x You can set concurrency limits using the Prefect CLI, UI, or API. You can manage these limits to ensure that a certain number of flow runs are not exceeded. CLI example:

bash
prefect gcl create my-concurrency-limit --limit 5 --slot-decay-per-second 1.0
prefect gcl update my-concurrency-limit --limit 10
prefect gcl delete my-concurrency-limit

More details: Prefect 2.x Global Concurrency Limits ### Prefect 3.x Similarly, in Prefect 3.x, you can create global concurrency limits. These can be set at the work pool and/or work queue level, controlling both flow run and task run concurrency. CLI example:

bash
prefect concurrency-limit create my-concurrency-limit --limit 5 --slot-decay-per-second 1.0
prefect concurrency-limit update my-concurrency-limit --limit 10
prefect concurrency-limit delete my-concurrency-limit

More details: Prefect 3.x Global Concurrency Limits Remember, using these concurrency limits can help you optimize the flow execution by managing the number of concurrent operations effectively.

@Marvin can I control concurrency limits on concrete machine? When using .serve one machine must run only one flow at one time

Yes, you can control concurrency limits for a specific machine when using the

.serve

method in both Prefect 2.x and 3.x. This ensures that only one flow runs at a time on a particular machine. ### Prefect 2.x You can use the global concurrency limits feature to control the execution across different machines. By setting a specific concurrency limit, you can ensure only one flow runs on the target machine. Steps: 1. Create Concurrency Limit: Use the CLI to create a concurrency limit.

bash
    prefect gcl create my-concurrency-limit --limit 1 --slot-decay-per-second 1.0

2. Apply Concurrency Limit: When serving your deployment, ensure it respects the concurrency limit by referencing it. More details: Prefect 2.x Global Concurrency Limits ### Prefect 3.x In Prefect 3.x, you can similarly set a concurrency limit to control the number of concurrent flows on a specific machine. Steps: 1. Create Concurrency Limit: Use the following CLI command to create the concurrency limit.

bash
    prefect concurrency-limit create my-concurrency-limit --limit 1 --slot-decay-per-second 1.0

2. Apply Concurrency Limit: Ensure that the deployments or flow runs respect the specified concurrency limit. More details: Prefect 3.x Global Concurrency Limits By following these steps, you can control the concurrency of flow runs on specific machines, ensuring that only one flow runs at a time on a particular machine. If you need more detailed guidance, please refer to the official documentation linked above.

@Marvin can I wait and get result from run_deployment?

Yes, you can wait for a flow run to complete and retrieve its result when using

run_deployment

in both Prefect 2.x and 3.x. ### Prefect 2.x In Prefect 2.x, you can wait for a flow run to complete and then retrieve the result using the synchronous API. Here's an example of how you can do it:

python
from prefect import flow, task
from prefect.client import get_client

@task
def my_task(x):
    return x + 1

@flow
def my_flow(x):
    result = my_task(x)
    return result

# Running deployment
async def run_my_deployment(deployment_name):
    async with get_client() as client:
        deployment_id = await client.create_deployment(my_flow, name=deployment_name)
        flow_run = await client.create_flow_run_from_deployment(deployment_id)
        await client.wait_for_flow_run(flow_run.id)

        result = await client.get_task_run_result(flow_run.id)
        return result

# Run the flow
import asyncio
result = asyncio.run(run_my_deployment("my-flow-deployment"))
print(result)

### Prefect 3.x In Prefect 3.x, you can similarly wait for a flow run to complete and retrieve its result. Prefect's API allows handling flow runs asynchronously:

python
from prefect.client import get_client
from prefect import flow, task

@task
def my_task(x):
    return x + 1

@flow
def my_flow(x):
    result = my_task(x)
    return result

# Running deployment
async def run_my_deployment(deployment_name):
    async with get_client() as client:
        deployment_id = await client.create_deployment(my_flow, name=deployment_name)
        flow_run = await client.create_flow_run_from_deployment(deployment_id)
        await client.wait_for_flow_run(flow_run.id)

        result = await client.get_task_run_result(flow_run.id)
        return result

# Run the flow
import asyncio
result = asyncio.run(run_my_deployment("my-flow-deployment"))
print(result)

Both examples utilize Prefect's client to create and manage deployments, and wait for the flow run to complete before fetching the result. This asynchronous approach ensures you can handle the flow execution more efficiently. For more details, refer to the following resources: - Prefect 2.x Using the Client - Prefect 3.x Runtime Context