@Marvin prefect 2 what is the role of the worker? i'm using a k8s worker. is it just to spin up k8s jobs or is there more to it?

In Prefect 2.x, a Kubernetes worker's primary role is to execute flow runs as Kubernetes jobs. Essentially, it creates Kubernetes jobs to run the tasks you define in your flow. However, there are additional responsibilities and configurations you might consider: 1. Job Management: Kubernetes workers manage the lifecycle of these Kubernetes jobs, ensuring they are properly started and monitored until completion. 2. Namespace Configuration: There is the potential to configure the namespace where Kubernetes jobs are executed. By default, a Kubernetes worker may use the

default

namespace unless specified otherwise. This can cause issues if the worker does not have permissions in that namespace, necessitating proper Role/RoleBinding configurations. 3. Security: Workers can be configured to store your Prefect Cloud API key securely within a Kubernetes secret for accessing and reporting to Prefect Cloud. 4. Custom Job Manifests: You can customize the Kubernetes job manifests to tailor the execution environment according to your needs. For more detailed information on configuring and using Kubernetes workers in Prefect 2.x, you can check Prefect's Kubernetes Worker documentation. If you need further assistance or run into specific issues, feel free to ask!

@Marvin what could cause these workers to run high memory? like 2Gi

High memory usage by Kubernetes workers in Prefect 2.x can stem from several factors: 1. Resource Allocation: If the Kubernetes jobs spawned by the worker require substantial resources, this could lead to high memory usage. Prefect allows customization of memory allocation for Kubernetes jobs, which needs to be managed judiciously. 2. Memory Leaks: There have been reports of potential memory leaks, particularly when using certain executors like the DaskExecutor. Memory usage might keep increasing over time rather than stabilizing once jobs are completed, suggesting a memory leak. 3. High Task Parallelism: Running multiple concurrent tasks or flows in parallel can also cause increased memory usage. Each task or flow might require its own memory, adding up as parallelism increases. 4. Executor Configuration: Certain configurations, like those involving multi-threading or process management (such as Dask), if not optimized, can contribute to unnecessary memory overhead. 5. Monitoring and Logs: Comprehensive logging and monitoring within the worker or job can also significantly increase memory usage. If you are experiencing sustained high memory usage, it is advisable to look into optimizing the number of concurrent jobs, inspecting logs and configurations for potential memory leaks, and possibly limiting or reallocating resources appropriately within your Kubernetes cluster. For more in-depth details about this issue, you can check related discussions and reported issues on GitHub here and here.