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<@ULVA73B9P> I'm migrating a GCP Cloud Run work po...
# ask-marvin
j
@Marvin I'm migrating a GCP Cloud Run work pool to Kubernetes. I'd like to be able to create my deployments using the 
.deploy()
method on flows. Can you show me how to do this? I got a runtime/ HTTP error when supplying an 
image
argument that said my kubernetes work pool did not support that.
m
I found some useful information regarding your issue. The error you're encountering is likely due to the unsupported 
image
argument in the Kubernetes work pool context. Here's a summary of the relevant findings and a step-by-step guide to help you deploy a flow using Kubernetes. 1. Documentation Excerpts: The Prefect documentation mentions creating a deployment using the new worker infrastructure. You can follow the guide to ensure your deployment configurations are compatible with Kubernetes. 2. GitHub Issues: There are some open issues regarding bugs and limitations with Kubernetes work pools in Prefect, particularly with 
infra_overrides
. You may need to update to a newer release or follow specific guidelines to resolve potential conflicts. Here's a step-by-step guide to help you successfully deploy using Kubernetes: ### Step-by-Step Guide 1. Configure the Kubernetes Cluster: Ensure that you have a Kubernetes cluster set up and configured. You may use a managed service like GKE, EKS, or AKS or set it up locally using tools like Minikube. 2. Prefect and Kubernetes installation: Install the required packages:
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pip install prefect kubernetes
3. Kubernetes CLI Configuration: Ensure you have 
kubectl
installed and configured to interact with your Kubernetes cluster. 4. Create a Kubernetes Work Pool: In Prefect, create a Kubernetes work pool.
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python
   from prefect import flow, task
   from prefect.deployments import Deployment
   from prefect.infrastructure.kubernetes import KubernetesJob

   @task
   def hello_task():
       print("Hello from Kubernetes!")

   @flow
   def hello_flow():
       hello_task()

   deployment = Deployment.build_from_flow(
       flow=hello_flow,
       name="k8s-deployment",
       infrastructure=KubernetesJob(),  # Specify Kubernetes environment
       work_pool_name="k8s-pool"  # Make sure this matches your work pool name
   )
   
   deployment.apply()
### Resources - Prefect Deployment guide for Kubernetes: Running Flows with Kubernetes - Creating and configuring work pools: Prefect Work Pools If the error persists, checking for updates to the Prefect package or filing a bug report on GitHub might be necessary. Here's the link to the relevant issue: GitHub Issue on Kubernetes Infrastructures. Feel free to ask if you have more questions or need further assistance!
j
@Marvin the dev tools say that the 
Deployment
class is deprecated and to use the 
.deploy()
method on flows instead
m
To deploy your flows using the 
.deploy()
method for Kubernetes, follow these steps. This assumes you're already set up with Kubernetes and have the Prefect CLI and Python client installed. ### Example Configuration 1. Create Your Flow: Define your flow in a Python script. For example:
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python
from prefect import flow, task

@task
def hello_task():
    print("Hello from Kubernetes!")

@flow
def hello_flow():
    hello_task()

if __name__ == "__main__":
    hello_flow.deploy(
        name="hello-k8s", 
        work_pool_name="k8s-pool", 
        infra_overrides={
            "image": "my-docker-image:latest",
            "namespace": "my-namespace"
        }
    )
2. Build and Push Docker Image: Make sure your Docker daemon is running. Build and push your Docker image to a container registry. Modify the Dockerfile according to your flow’s requirements.
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sh
   docker build -t my-docker-image:latest .
   docker push my-docker-image:latest
3. Set Environment Variable for Image Name: Set the environment variable 
PREFECT_IMAGE_NAME
to the image you just built and pushed.
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sh
   export PREFECT_IMAGE_NAME=my-docker-image:latest
4. Run Deployment: Deploy using the Prefect CLI. If your 
.deploy()
method doesn't automatically apply the deployment, use the following command:
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sh
   prefect deployment apply
5. Monitor Deployment: Check the status of your deployment and flow runs in the Prefect UI or using 
kubectl
. To monitor the Kubernetes deployment:
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sh
   kubectl get deployments
   kubectl logs -l flow=hello-k8s
### Links to Documentation - Running Flows with Kubernetes: Guide - Prefect Kubernetes Deployments: Module Ensure your Kubernetes cluster is correctly set up and the necessary permissions and configurations are applied. Check for updates or fixes related to Kubernetes work pool issues in the Prefect GitHub repository.
j
@Marvin that gives me this error:
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TypeError: Flow.deploy() got an unexpected keyword argument 'infra_overrides'
m
It seems like the 
flow.deploy()
method does not directly support the 
infra_overrides
parameter. Instead, you'll need to define your Kubernetes configuration within a 
prefect.yaml
file and then use the Prefect CLI to deploy your flow. Here's how you can do it: 1. Create a 
prefect.yaml
Configuration File: Define the necessary deployment configurations within this file:
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yaml
# prefect.yaml
version: 2.0
flows:
  hello-flow:
    parameters: {}
    infrastructure:
      type: kubernetes-job
      # Specify your Kubernetes settings here
      image: "my-docker-image:latest"
      namespace: "my-namespace"
work_pools:
  - name: k8s-pool
    type: kubernetes
    # Specify the Kubernetes worker settings here
    worker_configuration:
      image: "my-docker-image:latest"
      namespace: "my-namespace"
2. Create Your Flow Code: Write your flow code in a Python file, for example, `flow.py`:
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python
from prefect import flow, task

@task
def hello_task():
    print("Hello from Kubernetes!")

@flow
def hello_flow():
    hello_task()

if __name__ == "__main__":
    hello_flow.deploy(
        name="hello-k8s", 
        work_pool_name="k8s-pool"
    )
3. Build and Push Docker Image: Ensure your Docker daemon is running and build and push your Docker image.
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sh
docker build -t my-docker-image:latest .
docker push my-docker-image:latest
4. Deploy Your Flow Using CLI: Execute the following command in the terminal to deploy your flow:
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sh
prefect deploy -p ./prefect.yaml
### Resources - Running Flows with Kubernetes: Guide - Prefect Deployment YAML Configuration: Documentation By defining your Kubernetes settings within the 
prefect.yaml
file, you can use the CLI to deploy your flow without the 
infra_overrides
parameter. This approach provides a clean separation of configuration from the deployment logic in your flow code.
n
sorry for the goose chase @Marvin lead you on here shaking angry fist these docs should be helpful
m
Based on the correct documentation, here is how you can deploy your flow using the 
.deploy()
method and Kubernetes infrastructure. ### Steps to Deploy Using 
flow.deploy()
Method 1. Define the Infrastructure and Storage Blocks: You will need to create your Kubernetes infrastructure and source storage blocks in Prefect. 2. Flow Definition: Define your flow with the necessary tasks. 3. Deploy the Flow: Use the 
.deploy()
method to deploy your flow, specifying the 
work_pool_name
and any necessary overrides. Here is a complete example: ### Example Flow and Deployment 1. Define Your Flow: Save this script as 
flow.py
.
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python
from prefect import flow, task
from prefect.infrastructure.kubernetes import KubernetesJob
from prefect.filesystems import GitHub

@task
def hello_task():
    print("Hello from Kubernetes!")

@flow
def hello_flow():
    hello_task()

if __name__ == "__main__":
    # Create and configure infrastructure and storage blocks first
    hello_flow.deploy(
        name="hello-k8s", 
        work_pool_name="k8s-pool",
        infrastructure=KubernetesJob.load("my-k8s-job"),
        storage=GitHub.load("demo-repo"),
        parameters={"name": "world"}
    )
2. Build and Push Docker Image: Ensure your Docker daemon is running and build and push your Docker image if needed.
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sh
docker build -t my-docker-image:latest .
docker push my-docker-image:latest
3. Creating Infrastructure and Storage Blocks: Ensure you have created the 
my-k8s-job
KubernetesJob block and 
demo-repo
GitHub storage block within Prefect. Commands to create the blocks if needed:
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sh
prefect block register-kubernetes-job --name my-k8s-job --image my-docker-image:latest --namespace my-namespace
prefect block register-github --name demo-repo --repository your-repo-url --reference your-branch-or-tag
4. Deploying the Flow: Run the deployment script to deploy your flow.
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sh
python flow.py
### Links to Documentation - Using `flow.deploy()`: Upgrade Guide: Agents to Workers - Setting up Kubernetes Deployments: Prefect Kubernetes Guide By following these steps, you should be able to deploy your flow using the new 
flow.deploy()
method on a Kubernetes infrastructure.
j
Thanks @Nate. Still seems like @Marvin is recommending passing an 
infrastructure
argument which isn't supported on the 
.deploy()
method. I don't think I'm on an agent ATM, I am using a cloud run v2 push pool with no agent involved, allowing prefect to trigger jobs on cloud run directly without an agent needing to poll prefect. I already have my work pool created on my cluster via terraform and the prefect TF provider. Do you ave any examples of how to do this? Lots of the stuff I've found seems to mention using the 
prefect.deployments.Deployment
class, but everything in intellisense says that usage of that class is deprecated and to use 
flow.deploy()
I'm starting to look into the 
prefect-kubernetes
package but still looking mhy way through it.
ideally we'd be able to use the existing .deploy() syntax since thats what we have in place for all of our pipelines. I just want to swap out the infra for the work pools
if it helps, we have a tagged docker image that we want to use for each deployment
n
yeah sorry. this is mostly on me as marvin current reads from 2.x docs where the Deployment class is a user facing class - i will update marvin soon to clarify with queriers what version of prefect they have and then search the corresponding docs it’s great that you’re using push pools (where we run the worker as needed) and you’re correct to use .deploy method on flows did the link i just shared do anything to answer your original question on how to use .deploy?
j
I don't think so, because it seems to want me to create a YAML file: https://docs.prefect.io/latest/guides/deployment/kubernetes/#define-a-prefect-deployment I did this originally when we first started using prefect a little under a year ago and then quickly switched over to using the python-based methods via the 
@flow
decorator and 
.deploy()
method. All of the work pool infra is declared in terraform
n
sorry, this is the link i meant
j
I think it wants me to specify a remote storage location somewhere, which I could definitely supply as a link to a gcr.io registry tag, but I can't find any examples
Ah ok. I didn't look at that one originally because I wasn't using agents, but will take a look
n
flow.from_source().deploy() is the pythonic analogue to the declarative yaml way of defining deployments
j
hm. this is a super simple example of the approach we've been taking, it calls deploy on the flow directly without .from_source though
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from prefect import flow

@flow(name="simple-flow")
async def simple_flow():
    print("Ground control to Major Tom")

if __name__ == "__main__":
    simple_flow.deploy(
       work_pool_name="work_pool_name",
name="simple_flow_deployment"
)
sorry, editing ex. just a sec
I will look into adapting 
.from_source()
n
quick question to narrow things down, are you: • cloning code at runtime from github / gcs ? • baking your flow code into your images?
j
the latter
n
cool - then you shouldn't need 
from_source
, since that's how you'd tell us where the source code lives if you're doing the former so I think your simple example should work as is - have you hit specific issues with that?
j
Yes, I get this error: 
ValueError: Either an image or remote storage location must be provided when deploying a deployment.
n
gotcha, so I think here you'd need to provide an image, since that's where the code lives right?
j
and when I add the 
image
arg, I get this error
👀 1
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ValueError: Work pool 'gke-work-pool' does not support custom Docker images. Please use a work pool with an `image` variable in its base job template or specify a remote storage location for the flow with `.from_source`. If you are attempting to deploy a flow to a local process work pool, consider using `flow.serve` instead. See the documentation for more information: <https://docs.prefect.io/latest/concepts/flows/#serving-a-flow>
gke-work-pool
definitely exists, and I can see it from the prefect cloud dashboard
n
and if you dont mind, what does 
prefect work-pool ls
show?
j
Screenshot 2024-08-13 at 4.13.18 PM.png
I just realized that I may need to adjust my job template to accept an 
image
template parameter now though
I think I had that in my cloud run one originally and then may have inadvertently ripped it out because so much of the cloud run templated json was changing too
n
aha yeah if 
image
is not a variable in the template then this error makes sense to me, it should normally be present by default in a k8s type work pool
seems like we could maybe raise a more helpful error here though
j
cool, so that should that image parameter go into 
job_configuration
or 
variables
in 
base_job_template
I'm encoding the json from a terraform object
n
it should look like this, ie 
image
should be a key within 
variables.properties
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{
  "variables": {
    "type": "object",
    "properties": {
...
      "image": {
        "type": "string",
        "title": "Image",
        "example": "<http://docker.io/prefecthq/prefect:2-latest|docker.io/prefecthq/prefect:2-latest>",
        "description": "The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used."
      },
...
}
m
To create a Dockerfile for a Prefect deployment, your entrypoint should specify how to run your deployments. Generally, a Python script that executes your Prefect flow or task can be used as the ENTRYPOINT in the Dockerfile. Here are some main points to consider when creating your Dockerfile: 1. Base Image and Prefect Installation: Your Docker image should derive from a Python base image with Prefect installed. You could do this by adding a line in your Dockerfile such as:
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FROM python:3.x  # choose the appropriate Python version
   RUN pip install prefect
2. Copy Your Code: Add your flow code into the Docker image.
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COPY ./your_flow_script.py /app/your_flow_script.py
   WORKDIR /app
3. Set Environment Variables: Ensure your container can communicate with Prefect’s API by setting variables like 
PREFECT_API_URL
and `PREFECT_API_KEY`:
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ENV PREFECT_API_URL=<your_prefect_api_url>
   ENV PREFECT_API_KEY=<your_api_key>
4. Entrypoint: Your entrypoint might look like this, executing a script that either runs or monitors flows or tasks.
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ENTRYPOINT ["python", "your_flow_script.py"]
Additionally, make sure you authenticate with your preferred Docker registry and structure your deployment to avoid requiring registry pushes if not necessary. For detailed examples and configuration specific to your cloud provider or registry, you might need to consult Prefect's version-specific documentation or setup guides. For more information on Docker and Prefect deployment, you can check guides on the Prefect 2.x documentation page here: Prefect Guide or a relevant blog post on Dockerizing with Prefect: Dockerizing Prefect.
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