@Marvin, how can I structure my prefect.yaml file such that I can run my flow code as a job that requires two different pods from my aks-worker? So my flow code will be deployed in one pod and the other pod will spin up alongside it as a server.

Ah, the sweet symphony of information. Let's see what we've gathered. The Prefect documentation provides a guide on deploying a Prefect worker using Helm on a Kubernetes cluster, which seems to be what you're looking for. Here's a brief overview: 1. Add the Prefect Helm repository to your Helm client:

helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update

2. Create a new namespace in your Kubernetes cluster for the Prefect worker:

kubectl create namespace prefect

3. Create a Kubernetes Secret for the API Key. You'll need to create a file named

api-key.yaml

with the following contents:

apiVersion: v1
   kind: Secret
   metadata:
     name: prefect-api-key
     namespace: prefect
   type: Opaque
   data:
     key: <base64-encoded-api-key>

Replace

<base64-encoded-api-key>

with your Prefect Cloud API key encoded in base64. Apply the

api-key.yaml

file to create the Kubernetes secret:

kubectl apply -f api-key.yaml

4. Configure Prefect Worker Values. Create a

values.yaml

file to customize the Prefect worker configuration. Add the following contents to the file:

worker:
     cloudApiConfig:
       accountId: <target account ID>
       workspaceId: <target workspace ID>
     config:
       workPool: <target work pool name>

5. Install Prefect Worker Using Helm. Now you can install the Prefect worker using the Helm chart with your custom

values.yaml

file:

helm install prefect-worker prefect/prefect-worker \
     --namespace=prefect \
     -f values.yaml

6. Verify Deployment. Check the status of your Prefect worker deployment:

kubectl get pods -n prefect

You should see the Prefect worker pod running. You can find more detailed instructions in the Prefect documentation. As for your second pod that will spin up as a server, you might need to create a separate deployment for it. The specifics would depend on what this server is supposed to do. Remember, orchestrating Kubernetes pods is like conducting an orchestra. Each instrument (or pod, in this case) has its part to play, and it's your job as the conductor to ensure they all play in harmony. Or, you know, you could just let the computer do it. That's probably easier.

hey @Marvin, I mean more like if this were my prefect.yaml for example:

deployments:
- name: deployment-1
  entrypoint: flows/hello.py:my_flow
  parameters:
    number: 42
  work_pool:
    name: my-process-work-pool
    job_variables:
      image: image_1
      cpu_request: "4"
      cpu_limit: "8"
- name: deployment-2
  entrypoint: flows/goodpye.py:my_flow
  work_pool:
    name: my-process-work-pool
    job_variables:
      image: image_2
      cpu_request: "8"
      cpu_limit: "12"

Except deployment 2 isn't actually running any flow code it is just a supporting container for the flow code in deployment 1. Like a redis server for example. Similar to docker compose.

Ah, the dance of the deployments. Let's see if we can choreograph this properly. In Prefect, you can use the

executor

configuration to specify how tasks are executed. For your use case, you might want to use the

DaskExecutor

, which allows tasks to be executed in parallel, potentially on different machines. This is similar to how you might use a Redis server to coordinate tasks in a Docker Compose setup. Here's an example of how you might configure your

prefect.yaml

file:

yaml
deployments:
- name: deployment-1
  entrypoint: flows/hello.py:my_flow
  parameters:
    number: 42
  work_pool:
    name: my-process-work-pool
    job_variables:
      image: image_1
      cpu_request: "4"
      cpu_limit: "8"
  executor:
    type: DaskExecutor
    address: tcp://<address-of-dask-scheduler>:8786
- name: deployment-2
  entrypoint: flows/dask_scheduler.py:my_flow
  work_pool:
    name: my-process-work-pool
    job_variables:
      image: image_2
      cpu_request: "8"
      cpu_limit: "12"

In this example,

deployment-1

is configured to use a

DaskExecutor

that connects to a Dask scheduler running at the specified address.

deployment-2

is responsible for running the Dask scheduler. The

flows/dask_scheduler.py

file might look something like this:

python
from prefect import Flow
from dask.distributed import Scheduler

def my_flow():
    with Flow("Dask Scheduler") as flow:
        scheduler = Scheduler()
        scheduler.start()

    return flow

This setup allows

deployment-1

to execute tasks in parallel using the Dask scheduler running in

deployment-2

. Please note that this is a simplified example and you might need to adjust it to fit your specific needs. For example, you might need to configure networking between the two deployments, handle authentication for the Dask scheduler, etc. Remember, orchestrating deployments is like conducting a symphony. Each instrument (or deployment, in this case) has its part to play, and it's your job as the conductor to ensure they all play in harmony. Or, you know, you could just let the computer do it. That's probably easier.