@Marvin How does prefect distribute flows when running more than 1 instance of a deployment using

.serve()

?

When using

.serve()

in Prefect 3.x to run more than one instance of a deployment, the process is designed to handle multiple flows within the same Python environment. The

serve()

function is a long-running process that must be actively running to execute remotely triggered or scheduled runs. Here are some key points: 1. Multiple Flows: You can serve multiple flows using the same process. The flows are registered with the API once

serve()

is called, and they can be scheduled and executed independently of each other. 2. Environment: All deployments served must share a Python environment. 3. Scheduling and Execution: Deployments can be scheduled and executed independently. The Prefect UI and API allow you to manage these deployments, including triggering new runs, pausing, and canceling runs. 4. Work Pools: For more advanced configurations, you can use work pools to dynamically provision infrastructure and manage execution environments on a per-flow run basis. For more details, you can explore the Prefect documentation on running flows in local processes and the deployment overview.

@Marvin Can I run 2 instances of a deployment with

.serve()

?

Running multiple instances of a deployment using

.serve()

in Prefect 3.x isn't directly supported in the same way as traditional scaling methods. The

.serve()

method is intended for serving multiple flows within a single process and environment, rather than distributing across multiple instances. For scaling and running multiple instances of a deployment, you might want to consider using Prefect's work pools or other orchestration methods that allow for dynamic provisioning of infrastructure. This approach provides more control over scaling and execution environments. If you're looking to achieve high availability or load balancing, exploring Prefect's work pool configurations or using external orchestration tools might be more suitable.

@Marvin If I set a concurrency limit on a deployment using

.serve()

and it has reached its limit, what happens to the flows that have been triggered that haven't been submitted?

When a concurrency limit is set on a deployment using

.serve()

and it reaches its limit, any additional flow runs that are triggered but haven't been submitted will be queued. These queued runs will wait until a concurrency slot becomes available. Prefect manages this queuing automatically, ensuring that new runs are submitted as soon as the concurrency limit allows. For more details on configuring concurrency limits, you can refer to the Prefect documentation on task run limits.