Soham
05/17/2024, 8:00 PMMarvin
05/17/2024, 8:01 PM@dask.delayed
with @task
when using Prefect, especially within a flow that leverages the DaskTaskRunner
. Below are some examples and discussions I found relevant to your question:
1. In a documented example, tasks are defined using @task
and make use of Dask dataframe operations within a Prefect flow that uses the DaskTaskRunner
. This suggests that Dask and Prefect can be integrated smoothly.
python
import dask.distributed
from prefect import flow, task
from prefect_dask import DaskTaskRunner, get_dask_client
# ... (task definitions here)
@flow(task_runner=DaskTaskRunner(address=client.scheduler.address))
def dask_pipeline():
# ... (flow code here)
pass
Source: prefect-dask documentation
2. An issue on GitHub discusses recursively calling a function with dask.delayed
in a Prefect flow and trying to run each task in its own Dask worker pod for parallelization. The user employs Prefect's @task
and .submit
methods along with Dask.
Source: GitHub Issue
3. Another GitHub Issue indicates that functions must be wrapped with @task
for them to be submitted in parallel to Dask when using DaskTaskRunner
.
Source: GitHub Issue
4. There is also a mention of image processing pipelines failing due to memory issues when running tasks with DaskExecutor
. This highlights the need to manage resources efficiently when using Dask and Prefect together.
Source: GitHub Issue
In summary, it is possible to use @dask.delayed
with @task
in a Prefect flow, particularly when using the DaskTaskRunner
. However, you should take care to manage resources and understand the orchestration behavior to ensure your tasks are executed as intended.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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