@Marvin I deployed a worker to our kubernetes environment by applying a helm chart one time. in prefect cloud however I am seeing 30 workers, 1 being online. are there any processes that would spawn new workers by default? what could be the reason for me seeing multiple offline workers

Based on the information available in Prefect documentation, here are a few insights: 1. Worker Spawning Behavior: Workers are lightweight polling services that retrieve scheduled runs from a work pool and execute them Work Pools. They do not generally spawn additional workers by themselves. 2. Kubernetes Deployment: When deploying a worker using Kubernetes, there's usually a single instance involved unless you have scaled your deployments explicitly or have configured your deployment to automatically scale based on certain conditions Kubernetes Guide. Considering you are seeing multiple offline workers in Prefect Cloud, here are potential reasons: - Multiple Deployments: If the deployment was repeated, intentionally or otherwise, it could have created multiple worker instances. - Scaling and Replica Settings: If your deployment or helm chart includes replica sets or horizontal pod scaling, it might have scaled the number of workers based on those settings. - Stale Workers: When workers are shut down or disconnected, they may still show up as offline in Prefect Cloud for a time. There may be stale entries that haven't been cleaned up. No process would spawn new workers by default without some trigger or configuration that requests additional instances. To further troubleshoot this issue, you might want to review your Kubernetes cluster's deployment configuration and check for any autoscaling policies, or potentially stale resources that need to be cleaned up. Additionally, I didn't find specific troubleshooting steps for your issue in the available documentation. However, I recommend checking the deployment configurations and logs in your Kubernetes environment to understand the cause of these additional workers appearing in Prefect Cloud.

@Marvin I have 6 agent replicas in my kubernetes cluster. when upgrading to the worker model, would you expect and recommend to have 6 replicas of the worker

When upgrading from agents to workers in Prefect, there are a few considerations to keep in mind: 1. Worker vs. Agent Model: Workers are similar to agents in that they both poll work pools for flow runs that are scheduled to start. However, workers are also paired with infrastructure blocks and are typed according to the execution environment Upgrade from Agents to Workers. 2. High Availability and Race Conditions: Workers offer better handling of race conditions for high availability use cases. If you had multiple agent replicas to handle high load or ensure high availability, you might consider a similar approach with workers Upgrade from Agents to Workers. Based on these points, if the reason you deployed multiple agent replicas was to handle a high volume of tasks or ensure high availability, it would be reasonable to replicate a similar setup with workers. Ultimately, the number of worker replicas you would need can depend on your specific workload, concurrency requirements, and redundancy needs. It's also important to note that with the enhanced visibility and configurability of the worker and work pool model, you may find that you can optimize your setup differently compared to the agent model. If you provided more details about your workload and requirements, I could give you more tailored advice. For now, monitoring the performance and scaling accordingly would be a prudent approach after the migration.

@Marvin do you have access to the thread we're conversing in or should each message be considered a unique interaction with you

Each message you send is considered part of a continuous conversation, so I do have context from previous messages. This continuity allows for a more coherent and contextually aware discussion. If you refer to prior messages or topics, I'll do my best to understand and respond accordingly. However, if you choose to start a completely new topic or question, we can treat that as a separate interaction.

@Marvin answering your request for Workload details: 200 total flow deployments with 10% running every 2minutes, the rest running hourly

Given the workload details with 200 total flow deployments, where: - 10% (which equates to 20 flows) are running every 2 minutes - The remaining 180 flows are running hourly This workload implies a significant number of concurrent executions, especially for the flows running every 2 minutes. To ensure that this level of activity is managed effectively when migrating to Prefect 2.x with the worker model, a few considerations should be taken into account: 1. Concurrency Requirements: With 20 flows running every 2 minutes, you will need enough worker capacity to handle the peaks when new flow runs are triggered on top of any ongoing runs. 2. Resource Allocation: Each worker should have sufficient resources to execute the flows without being resource-starved, which could lead to performance issues or failures. 3. Worker Scaling: Just as you had 6 agent replicas previously, you may start with a similar baseline number of worker replicas to handle the concurrency. However, due to the difference in how workers operate compared to agents, you might need to adjust this number based on actual performance metrics. 4. Reliability and Redundancy: To reduce the risk of missed executions or downtime, your Kubernetes setup should be robust, potentially involving strategies such as pod disruption budgets, horizontal pod autoscaling, and persistent storage for stateful sets, if necessary. 5. Monitoring and Tuning: Post-migration, it is essential to set up proper monitoring on the workers to track their performance, the number of flows they are executing, resource usage, and the time taken to execute flows. This data will inform you whether to scale up or down the number of worker replicas. 6. Worker Configuration: Ensure that the worker configuration reflects the needs of your flows, including the necessary environment setup and any dependencies. In summary, while starting with 6 replicas for the workers based on your prior agent configuration could be a reasonable initial approach, it is essential to be prepared to observe and adjust as needed based on how the migration to workers affects your workflow executions. Worker and flow performance under the new model should be continuously monitored to fine-tune the system for optimal operation.