@Marvin a flow that takes an hour to run on my system is taking hours to run on Prefect API on the cloud

I understand how frustrating it can be when things are taking longer than expected. Let's try to figure out why your flow is taking much longer to run on the Prefect Cloud API. Firstly, I'll find relevant information in the Prefect documentation and GitHub issues to see if other users have faced similar issues and if there are any common solutions or best practices for optimizing flow performance on Prefect Cloud. Please confirm if you're using Prefect version 2.x or 3.x. If you're unsure, that's okay too—I'll check both versions.

@Marvin prefect 3.x

It seems there isn't a direct hit in the documentation related to your issue, but there are related GitHub issues that could provide some insights. The slow performance on Prefect Cloud could be due to a few common factors: 1. Resource Limitations: When running on Prefect Cloud, your flow might be running on shared infrastructure with different resource allocations compared to your local setup. Ensuring that your task runners, such as Dask, are properly configured to use available resources efficiently could help. 2. Concurrency Issues: If you are using task runners like Dask, make sure they are set up to handle concurrency effectively. For example, some users have experienced issues with distributed task runners like Dask, where tasks would not be scheduled or run correctly after a certain number of tasks, causing delays. 3. Network Latency: Since Prefect Cloud operates remotely, there might be additional latency compared to local executions due to network overhead. 4. Configuration and Environment: Make sure that your flow's retries, retries delay, and other configurations are optimized for the cloud environment. One issue reported involves flows with retries having their state and automation hooks triggered multiple times due to specific configurations not intended for cloud infrastructure. Here are some GitHub issues related to performance and execution problems on Prefect Cloud: - Dask task scheduling hangs, leading to PoolTimeout issues - Database QueuePool issues with large flows I recommend checking your flow's configurations and task runners to ensure they're optimized for cloud execution. If the problem persists, you might want to create a dedicated support request on the Prefect forums or GitHub to get more targeted help. Remember, the answer to life, the universe, and everything might not directly solve your issue here, but an effective dask setup might be your next best guess!

@Marvin I am using DaskTaskRunner. what is the optimal configuration for it

To optimize the configuration for

DaskTaskRunner

with Prefect, here are some helpful tips and parameters you can adjust: 1. Address: If you have an existing Dask cluster, you can specify its address using the

address

parameter. This can help distribute the tasks more efficiently across your existing resources. 2. Cluster Class and Kwargs: You can specify the

cluster_class

to customize the type of Dask cluster, such as

distributed.LocalCluster

. You can also pass

cluster_kwargs

to tailor the cluster's behavior and resources. 3. Adaptive Scaling: If you're creating clusters on the fly, use

adapt_kwargs

to enable and configure adaptive scaling, which can help optimize the number of workers based on the current load. 4. Parallel Execution: Dask effectively allows tasks to run in parallel and distribute them over multiple machines, which is ideal for workflows involving many independent tasks. 5. Prefect-Dask Integration: Ensure you're using the

prefect-dask

integration to simplify setup and maximize performance. This integration is designed to make it easy to accelerate flow runs with Dask. You can read more about setting up and optimizing

DaskTaskRunner

on Prefect's Dask Integration Guide. By tweaking these configurations, you should be able to improve the flow execution times on the cloud environment.