You are correct that in Prefect, parallelization is not a default behavior and needs to be explicitly defined. Here are the key points about parallelizing tasks in Prefect: 1. Dynamic Flows: Since flows are dynamic, there isn't a predefined DAG, so tasks won't automatically parallelize. 2. Task Mapping: Using

task.map

is a common way to parallelize tasks explicitly. This allows you to run the same task across multiple inputs in parallel. 3. Task Runners: Additionally, using specific task runners like

ConcurrentTaskRunner

,

DaskTaskRunner

, or

RayTaskRunner

can help in achieving parallel execution of tasks. Here's an example using `task.map`:

@flow
def my_flow():
    task_a.map([1, 2, 3])

For more information, you can refer to the Prefect Task Runners documentation and the Mapping over Iterables section.

It seems you're right in your understanding. Parallelism is not automatic due to the dynamic nature of flows in Prefect. Here's a concise summary: 1. Explicit Parallelization: You need to explicitly define parallelization in your flow, for example, using

task.map

. 2. Example of

task.map

:

@flow
   def my_flow():
       task_a.map([1, 2, 3])

3. Concurrency Limits: Concurrency can be managed using concurrency context managers if needed. 4. Documentation on Mapping: More details can be found here. 5. Concurrent Tasks Example: More advanced examples involving concurrency can be found here. For a detailed code example related to mapping tasks, see this code example.