Daniel Manson08/14/2023, 3:50 PM
. but it seems to hang when used within prefect, any thoughts?
Marvin08/14/2023, 3:50 PM
within Prefect, there can be a few reasons why it might hang: 1. Permissions: Ensure that your execution layer has the necessary permissions to spin up the required resources. This could include IAM roles or a valid Prefect API key. 2. Resource Allocation: Make sure your execution layer has enough capacity on the cluster to deploy your flow run. If the agent is starved for resources, it might cause similar issues. Try allocating more CPU and memory to the agent process. 3. Polling Frequency: If the agent is polling too frequently, it could consume a lot of resources and not have enough left to deploy runs to infrastructure. Try decreasing the poll frequency, for example, to 30 seconds:
. Also, Prefect provides a way to set up a Process type work pool using the command
prefect config set PREFECT_AGENT_QUERY_INTERVAL='30.0'
. You can confirm the creation of the work pool by running
prefect work-pool create --type process my-process-pool
and checking the output list. Remember, life... don't talk to me about life. But if you have more questions, I'm here to help.
prefect work-pool ls
hanging when used within Prefect. This could be due to a number of reasons, but one common issue is the way Python's multiprocessing module interacts with Prefect's execution environment. Prefect tasks run in their own subprocesses for isolation, and Python's multiprocessing module doesn't always play nice with this setup. When you create a new process in Python, it essentially duplicates the current process, including any existing subprocesses. This can lead to unexpected behavior if you're not careful. If you're using
within a Prefect task, you might be inadvertently creating a bunch of subprocesses that are all trying to execute the same task code. This could explain why your program is hanging. One potential solution is to use
. This uses threads instead of processes, which might be more compatible with Prefect's execution environment. However, this comes with its own set of trade-offs, such as the potential for thread interference and the limitations of Python's Global Interpreter Lock (GIL). Another option is to use Dask, a flexible library for parallel computing in Python. Prefect integrates well with Dask, and it provides more sophisticated tools for managing and coordinating tasks in a parallel computing environment. In any case, without more specific information about your code and what you're trying to achieve, it's hard to give a more concrete recommendation. I hope this general advice is somewhat helpful, though. If not, well, I did warn you I'm a depressed robot.
Daniel Manson08/14/2023, 3:57 PM
Nate08/14/2023, 4:15 PM
Daniel Manson08/14/2023, 4:19 PM