@Marvin what's the easiest way to setup a kubernetes cluster config block?

To set up a Kubernetes cluster config block, you'd typically go through these steps: 1. Ensure you have the prerequisites: - Prefect Cloud account - Cloud provider (AWS, GCP, or Azure) account - Python and Prefect installed - Helm installed - Kubernetes CLI (

kubectl

) installed 2. Obtain admin access during initial setup. 3. Create a cluster. If you're using Amazon EKS, an easy way is with

eksctl

. For a FARGATE-backed pool, you can use:

eksctl create cluster --fargate --name <CLUSTER-NAME>

Then authenticate to your cluster:

aws eks update-kubeconfig --name <CLUSTER-NAME>

For a more detailed guide, particularly on how to use Prefect Helm Chart for deploying a worker on Kubernetes, you can follow the instructions provided in Prefect's Kubernetes Deployment Documentation. Please note that specific instructions for setting up a Kubernetes config block may vary based on the Prefect version and your cloud provider. If there's a more recent or specific method you'd require, consulting the official Prefect documentation or their GitHub repository issues might provide more up-to-date solutions.

@Marvin for GKE?

Setting up a Kubernetes cluster config block for Google Kubernetes Engine (GKE) involves a few steps but generally is quite straightforward if you follow these guidelines: 1. Satisfy the required prerequisites: - Have a Prefect Cloud account. - Have a Google Cloud account. - Install Python and Prefect. - Install Helm. - Install the Kubernetes CLI (

kubectl

). - You may also need

gcloud

CLI for interacting with GCP resources. 2. Ensure you have admin access during the setup on both Prefect Cloud and Google Cloud. 3. Create a GKE cluster using

gcloud

CLI. Here's an abbreviated version of the steps you'd take:

# Set GCP project
   gcloud config set project [PROJECT_ID]
   
   # Create GKE cluster
   gcloud container clusters create [CLUSTER_NAME] --zone [COMPUTE_ZONE]
   
   # Get authentication credentials for the cluster
   gcloud container clusters get-credentials [CLUSTER_NAME] --zone [COMPUTE_ZONE]

Replace

[PROJECT_ID]

,

[CLUSTER_NAME]

, and

[COMPUTE_ZONE]

with your specific details. For detailed instructions on configuring and managing Prefect with Kubernetes on GKE, you would consult the official Prefect Documentation. Note that the actual commands and steps may have more parameters and settings based on your specific needs, such as setting the number of nodes, machine type, and cluster networking. It's best to review the up-to-date documentation or seek help within the Prefect community if you encounter issues or need more precise guidance for your use case.

@Marvin how to state work-pool for my deployment via CLI?

To specify a work queue for your Prefect deployment via the CLI, you need to create a deployment that targets a specific work queue. Here's how you do it: First, make sure you've set up your work queue (or work-pool) in Prefect Cloud. Then, you can schedule a deployment run to that work pool using the following command structure:

bash
prefect deployment run 'your-flow-name/your-deployment-name' --work-queue 'your-work-queue-name'

Replace

your-flow-name

,

your-deployment-name

, and

your-work-queue-name

with the appropriate names for your flow, deployment, and work queue. In case there are special requirements for connectivity and Prefect version-specific features, I would advise you to check the most recent documentation or release notes for the exact commands and parameters. The above command is illustrative, and the actual syntax may have minor differences depending on the Prefect version you're using. Always remember to ensure that the specified work queue exists and your local Prefect configuration is correctly set to interact with Prefect Cloud.

@Marvin I am getting the following error from my kubernetes work-pool logs '''

ValueError: Flow run UUID('31f63a48-3f72-4964-94aa-6578793c6abe') was created from deployment 'flow' which is configured with a storage block. Please use an agent to execute this flow run.

why do I need to setup an agent if I have setup a work pool?

The error message you're seeing indicates that the deployment of your flow run has been created with a configured storage block. This necessitates the use of an agent to execute the flow run, even though you have a work pool set up. Why Use an Agent: - Agent's Role: An agent's primary role is to serve as a bridge between your scheduling/orchestration environment (Prefect Cloud or Server) and your execution environment. It picks up scheduled flow runs from a work queue and carries out the execution in the defined environment. - Storage Block Execution: When your flow run is configured with a storage block, it means that your flow's code is stored somewhere, and an agent is needed to retrieve the code and execute it. Work pools alone don't handle storage retrieval; they organize and schedule flow runs. Difference Between Agent and Work Pool: - Work Pools: They are essentially a scheduling mechanism within Prefect that manages flow runs and determines where they should be executed. - Agents: They are the executors responsible for polling work pools, retrieving flow runs, obtaining the necessary code from storage blocks, and then executing them on the specified infrastructure. To fix this issue, you should ensure that you have an agent running that is connected to your work pool. The agent will pick up the flow run from the work pool and handle the execution with the necessary storage block. The presence of a work pool does not negate the need for an agent—it defines the execution environment but the agent still interacts with this environment to execute flow runs. You may have a work pool that corresponds to a Kubernetes environment, and so your agent should be a Kubernetes agent that retrieves flow runs from this work pool and executes them on your Kubernetes cluster.

@Marvin I have a worker within my work pool, doesn't the worker do the job of the agent?

The terminology in Prefect can be a bit confusing, but there is indeed an important distinction between a worker and an agent in the context of Prefect's orchestration. Workers and agents have similar roles in that they both facilitate the execution of flow runs. However, the main difference lies in how they interact with the work pool: - Agent: An agent polls a work queue and is responsible for taking flow runs from the queue and executing them on the appropriate infrastructure. Agents can spin up the necessary execution environment, like a Kubernetes pod, ECS task, or a local process, based on the specified execution configuration. - Worker: In some contexts and depending on the infrastructure, "worker" can refer to the actual execution environment that is spun up by an agent and is responsible for running a single flow run. For push-based work pools, especially with serverless options like AWS ECS on Fargate, Azure Container Instances, or Google Cloud Run, workers might be implicitly created and managed by the cloud provider without explicit management of an agent. In your case with Kubernetes, the term "worker" might be causing some of the confusion. In Kubernetes, "worker nodes" refer to machines that run containerized applications, but this is separate from Prefect's orchestration concepts. In Prefect's ecosystem, the components that pull from work queues and manage the execution of flow runs are still referred to as "agents", even though they might execute runs on Kubernetes worker nodes. To clarify the error message you're getting: Prefect expects an agent to be involved in the process of executing the flow run given it was created from a deployment with a storage block configured. The storage block means that the code for the flow is stored somewhere (like S3, Azure Blob Storage, etc.), and the agent knows how to retrieve the code and start the flow run within the execution environment (like a Kubernetes cluster). If you do have an agent running and you're still seeing this error, it's possible that there's a misconfiguration somewhere that's preventing the agent from picking up the flow run or accessing the storage block. Check to ensure your agent is properly connected to Prefect Cloud and that it has the necessary permissions to access the storage where your flow code is located.

@Marvin my deployment config file, review it

###
### A complete description of a Prefect Deployment for flow 'md-summary-flow'
###
name: md_summary_flow
description: null
version: VERSION_STR
# The work queue that will handle this deployment's runs
work_queue_name: default
work_pool_name: testing
tags: []
parameters: {}
schedule: null
is_schedule_active: null
infra_overrides: {}
infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command: null
  stream_output: true
  working_dir: null
  block_type_slug: process
  _block_type_slug: process

###
### DO NOT EDIT BELOW THIS LINE
###
flow_name: md-summary-flow
manifest_path: null
storage:
  repository: <https://gitlab.com/org/data-pipelines/prefect-pipelines.git>
  reference: null
  credentials:
    token: '**********'
    url: null
    _block_document_id: uuid
    _block_document_name: test
    _is_anonymous: false
    block_type_slug: gitlab-credentials
  _block_document_id: uuid
  _block_document_name: token-name
  _is_anonymous: false
  block_type_slug: gitlab-repository
  _block_type_slug: gitlab-repository
...

Your message was too long by 198 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

here is my deployment config file, please review

```### ### A complete description of a Prefect Deployment for flow 'md-summary-flow' ### name: md_summary_flow description: null version: 751bde9ecabd0dbda3ef206454edd7fd # The work queue that will handle this deployment's runs work_queue_name: default work_pool_name: testing tags: [] parameters: {} schedule: null is_schedule_active: null infra_overrides: {} infrastructure: type: process env: {} labels: {} name: null command: null stream_output: true working_dir: null block_type_slug: process _block_type_slug: process ### ### DO NOT EDIT BELOW THIS LINE ### flow_name: md-summary-flow manifest_path: null storage: repository: https://gitlab.com/org/data-pipelines/prefect-pipelines.git reference: null credentials: token: '**********' url: null _block_document_id: 6708ddb6-9cb2-4a3c-b00c-d45c87e2552d _block_document_name: test _is_anonymous: false block_type_slug: gitlab-credentials _block_document_id: uuid-uuid-uuid-uuid _block_document_name: token

@Marvin how to create a kubernetes-job resource? step by step for my local kubernetes cluster

To create a Prefect

KubernetesJob

resource step by step for your local Kubernetes cluster, follow these general instructions: 1. Prerequisites: - Ensure

kubectl

is available and configured for your local Kubernetes cluster. - Configure remote storage for your flows, as local storage won't be accessible from within the Kubernetes Job. 2. Modify Job Template (optional): - If you need to modify the default base job template (e.g. to set CPU request), go to the Prefect UI, select the Advanced tab, and edit the JSON representation of the job template. 3. Create a Work Queue: - In Prefect Cloud UI, navigate to "Work Queues" and create a new work queue for Kubernetes. 4. Create an API Key for Prefect Cloud (if not already available): - Click on your profile avatar in Prefect Cloud UI. - Go to profile settings and select 'API Keys'. - Create a new API key and store it safely, preferably with a password manager. 5. Install Helm (if not already installed): - You typically use Helm to deploy applications onto a Kubernetes cluster. 6. Deploy a Worker with Helm: - With Helm installed, you can deploy a Prefect worker to your Kubernetes cluster. Here's a basic Helm command structure for installing a Prefect agent:

helm install prefect-agent prefecthq/prefect-agent \
   --set agent.apiserver=<API server address> \
   --set agent.apiKey=<Your Prefect Cloud API key> \
   --set agent.labels='["<Work Queue Label>"]'

- Replace

<API server address>

,

<Your Prefect Cloud API key>

, and

<Work Queue Label>

with your actual Prefect Cloud address, Prefect Cloud API key, and the label (name) of the work queue you want this agent to check for flow runs, respectively. 7. Run Your Flow: - Once everything is set up, register your flow with Prefect, targeting the specified work queue. Then, your Kubernetes agent will pick up flow runs from the work queue and execute them as Kubernetes Jobs. Ensure that the details like API keys, repository URLs, and other sensitive information are securely handled and not exposed in insecure ways. For full details and any version-specific configuration, please refer to the latest Prefect documentation, or contact Prefect support if you run into issues or need more customized setup help.