@Marvin is there a way to disable updates to a work pool from the prefect cloud admin? I want to manage my work pools entirely through terraform

It appears that there are technical issues preventing the fetching of information from the Prefect documentation directly. However, managing work pools with Terraform while preventing updates from the Prefect admin might require setting specific permissions or configurations. Typically, to manage resources entirely through infrastructure-as-code (like Terraform), you would: 1. Create and configure the work pool using Terraform. 2. Adjust the permissions in Prefect to restrict updates from the admin interface. Unfortunately, without direct access to the documentation, I can't provide the exact steps. I recommend reaching out to the Prefect community or support for guidance on restricting admin updates specifically. You can likely find relevant discussions or tips on the Prefect Discourse or GitHub issues: Prefect Discourse Prefect GitHub Issues If you have more questions or need further assistance, feel free to ask.

@Marvin write me a base

prefect_work_pool

terraform configuration for kubernetes on GKE

It seems that the provided code example is not directly related to Terraform configuration. Let's manually create a base

prefect_work_pool

Terraform configuration for Kubernetes on Google Kubernetes Engine (GKE). Here is a basic example to get you started:

hcl
provider "google" {
  credentials = file("<path_to_your_service_account_key>.json")
  project     = "<your_gcp_project>"
  region      = "<your_gcp_region>"
}

resource "google_container_cluster" "primary" {
  name     = "primary-gke-cluster"
  location = var.region

  # remove the default node pool
  remove_default_node_pool = true
  initial_node_count       = 1

  network    = google_compute_network.vpc_network.name
  subnetwork = google_compute_subnetwork.subnetwork.name
}

resource "google_container_node_pool" "primary_preemptible_nodes" {
  cluster    = google_container_cluster.primary.name
  location   = google_container_cluster.primary.location
  node_count = 1

  node_config {
    preemptible  = true
    machine_type = "e2-standard-4"

    oauth_scopes = [
      "<https://www.googleapis.com/auth/cloud-platform>",
    ]
  }
}

resource "google_service_account" "prefect" {
  account_id   = "prefect-service-account"
  display_name = "Prefect Service Account"
}

resource "google_project_iam_binding" "prefect" {
  role    = "roles/container.admin"
  members = [
    "serviceAccount:${google_service_account.prefect.email}",
  ]
}

resource "kubernetes_secret" "gke_kubeconfig" {
  metadata {
    name      = "gke-kubeconfig"
    namespace = "default"
  }

  data = {
    config = base64encode(google_container_cluster.primary.endpoint)
  }
}

resource "prefect_work_pool" "gke_work_pool" {
  name     = "gke-work-pool"
  type     = "kubernetes"
  base_job_template = jsonencode({
    metadata = {
      namespace = "default"
    },
    spec = {
      template = {
        spec = {
          containers = [
            {
              name  = "flow-container"
              image = "YOUR_FLOW_IMAGE"
              env   = [
                {
                  name  = "PREFECT_API_URL"
                  value = "YOUR_PREFECT_API_URL"
                }
              ]
            }
          ]
        }
      }
    }
  })
}

Replace the placeholders with your actual values: -

"<path_to_your_service_account_key>.json"

-

"<your_gcp_project>"

-

"<your_gcp_region>"

-

YOUR_FLOW_IMAGE

-

YOUR_PREFECT_API_URL

This Terraform configuration initializes a GKE cluster, creates a node pool, sets up a service account with the necessary permissions, and creates a Prefect work pool configured to use Kubernetes. Ensure you customize the

base_job_template

as per your specific requirements. If you have further questions or need more specific configurations, feel free to ask!

@Marvin I'm trying to create a deployment for the above kubernetes work pool by calling the

.deploy()

method on a flow. But I'm getting this error: ValueError: Either an image or remote storage location must be provided when deploying a deployment. I originally had an image parameter specified, but got an error about my gke work pool not supporting image parameters. Can you help me out?

It seems there's some confusion regarding image parameters within the Kubernetes work pool in Prefect. Here's the summary of what we found: 1. Image Parameter Issue: If your Kubernetes work pool does not support image parameters directly, you might need to configure the deployment using a different method. 2. Deployment Requirements: When deploying with

.deploy()

, you need to specify either an image or a remote storage location for the flow code. Given these points, here's what you can do: ### Steps to Deploy a Flow using a Kubernetes Work Pool 1. Use Remote Storage: Store your flow code in a remote storage (e.g., GitHub) and use that as the deployment source. Here’s an example using a GitHub repository:

python
from prefect.deployments import Deployment
from prefect import flow
from prefect.filesystems import GitHub

@flow
def my_flow():
    print("Hello, world!")

# Define your storage block
github_block = GitHub(
    repository="my-repo/my-flow-repo",  # Your GitHub repository
    reference="main",  # Branch or commit hash
)

# Create the deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    work_pool_name="your-kubernetes-work-pool",
    storage=github_block,
    entrypoint="path/to/flow.py:my_flow",  # Path to your flow in the repo
)
deployment.apply()

2. Ensure Base Job Template: The base job template in your work pool should contain the default image to be used. Here is how your base job template should look in your Terraform configuration:

hcl
resource "prefect_work_pool" "gke_work_pool" {
  name     = "gke-work-pool"
  type     = "kubernetes"
  base_job_template = jsonencode({
    metadata = {
      namespace = "default"
    },
    spec = {
      template = {
        spec = {
          containers = [
            {
              name  = "flow-container",
              image = "YOUR_DEFAULT_IMAGE",  # Specify the default image here
              env   = [
                {
                  name  = "PREFECT_API_URL",
                  value = "YOUR_PREFECT_API_URL"
                }
              ]
            }
          ]
        }
      }
    }
  })
}

### Links for Further Information - Prefect: Work Pools Documentation - Prefect: Kubernetes Guide - Prefect: Storage Options By following these steps, you should be able to deploy your flow using a Kubernetes work pool without directly specifying the image in your deployment command. If you encounter further issues or have any more questions, feel free to ask!

@Marvin can you do the same thing using the

.deploy()

method on a flow?

Certainly! Here’s how you can use the

.deploy()

method on a flow directly while utilizing remote storage (GitHub) for your flow code. This setup allows you to avoid specifying the image directly if your Kubernetes work pool doesn't support it. ### Example: Deploy a Flow using

.deploy()

with Remote Storage 1. Install the necessary Prefect library if you haven't already:

bash
   pip install prefect

2. Define your flow and set up the GitHub storage block:

python
   from prefect import flow
   from prefect.deployments import Deployment
   from prefect.filesystems import GitHub

   @flow
   def my_flow():
       print("Hello, world!")

   # Define your storage block
   github_block = GitHub(
       repository="my-repo/my-flow-repo",  # Your GitHub repository
       reference="main",  # Branch or commit hash
   )

   # Deploy the flow
   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-deployment",
       work_pool_name="your-kubernetes-work-pool",
       storage=github_block,  # Use the GitHub storage block
       entrypoint="path/to/flow.py:my_flow",  # Path to your flow in the repo
   ).apply()

### Important Components Breakdown - GitHub Block: This specifies where your flow code is stored. The repository and reference should point to the GitHub repo and branch/commit where your flow is located. - Flow Entrypoint: Specify the path to your flow file and the flow function within that file. This tells Prefect where to find the flow in your codebase. - Work Pool Name: Ensure

your-kubernetes-work-pool

matches the exact name of your work pool configured in Prefect. ### Complete Example Command to Deploy Directly from Flow By packaging all the necessary details, you can invoke the

.deploy()

method on your flow:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

@flow
def my_flow():
    print("Hello, world!")

# Define GitHub storage block
github_block = GitHub(
    repository="my-repo/my-flow-repo",  # replace with your repo
    reference="main"  # branch or commit hash
)

# Deploy using the .deploy() method
my_flow.deploy(
    name="my-deployment",
    work_pool_name="your-kubernetes-work-pool",
    storage=github_block,  # Specify the GitHub storage block
    entrypoint="path/to/flow.py:my_flow"  # Path within the repo
)

### Key Considerations - Storage: The

storage

parameter ensures Prefect knows where your code lives. - Entrypoint: Accurately point to your flow's file and function name inside the repository. This setup ensures that your deployment leverages the remote GitHub repository for the flow code, thus avoiding issues related to unsupported image parameters in your Kubernetes work pool. Feel free to modify URLs and paths to suit your setup. If you encounter further issues or need more customization, let me know!