@Marvin What requirements do I need for a deployment that will run a flow on a Kubernetes cluster?

To deploy a flow on a Kubernetes cluster using Prefect 3.x, you'll need to consider the following requirements: 1. Docker Image: Specify the Docker container image for your jobs. If not set, the latest Prefect 3 image is used by default. You can override this per deployment through job variables. 2. Kubernetes Namespace: Define the Kubernetes namespace for creating jobs. By default, this is set to

default

, but you might want to set it to

prefect

or another namespace you prefer. 3. Image Pull Policy: Choose an image pull policy. If using

IfNotPresent

, ensure unique image tags to avoid cached old images. 4. Finished Job TTL: Set the time-to-live (TTL) for finished jobs. For example, setting it to 60 seconds will clean up completed flow runs after a minute. 5. Pod Watch Timeout: Configure the pod creation timeout. A recommended setting is 300 seconds, especially if using serverless node pools with longer startup times. 6. Kubernetes Cluster Configuration: Typically, leave the cluster configuration blank if the worker has appropriate access and permissions. However, you can specify a

KubernetesClusterConfig

block if needed. You can find more detailed information and examples in the Prefect Kubernetes deployment documentation. For more general deployment information, you can refer to the Prefect deployment overview.

@Marvin When I deploy I am met with the following error: how do I resolve it? pydantic.errors.PydanticUserError: The

__modify_schema__

method is not supported in Pydantic v2. Use

__get_pydantic_json_schema__

instead in class

SecretStr

. (

The error you're encountering is due to the usage of

pydantic.v1.SecretStr

which relies on the deprecated

__modify_schema__

method in Pydantic v2. This issue is recognized in the Prefect community, particularly with the

prefect_aws.credentials

module. To resolve this, you have a couple of options: 1. Downgrade Pydantic: Temporarily downgrade to Pydantic v1 if your project allows it and you need an immediate workaround. 2. Update Prefect: Keep an eye on updates from Prefect. The issue is being tracked, and a fix may be implemented in future Prefect releases. You can follow the GitHub issue for updates. 3. Modify Code: If possible, avoid using

SecretStr

from

pydantic.v1

and instead adapt your code to work with Pydantic v2's approach, using

__get_pydantic_json_schema__

. For more details, you can check the Prefect GitHub issue related to this error.

@Marvin How do I create a deployment that run a python script that is stored in Azure blob storage?

To create a deployment that runs a Python script stored in Azure Blob Storage using Prefect 3.x, you can follow these steps: 1. Install Prefect Azure Integration: - Ensure you have the

prefect-azure

package installed. This package provides capabilities to interact with Azure services.

bash
   pip install prefect-azure

2. Register Azure Block Types: - After installation, register the block types to make them available for use.

python
   from prefect_azure import register_blocks
   register_blocks()

3. Accessing Blob Storage: - Use the Azure Blob Storage block to download your script. You can define a task to retrieve the script from Azure Blob Storage.

python
   from prefect import task, flow
   from prefect_azure.blob_storage import BlobStorage

   @task
   def download_script(blob_name: str, container_name: str):
       blob_storage = BlobStorage.load("your-blob-storage-block-name")
       script_content = blob_storage.read(blob_name=blob_name, container_name=container_name)
       return script_content

4. Create and Deploy Your Flow: - Define your flow to include the task that downloads and executes the script.

python
   @flow
   def run_script_from_blob():
       script_content = download_script(blob_name="your_script.py", container_name="your-container")
       exec(script_content)

   run_script_from_blob()

5. Deploy on Azure Container Instances (Optional): - If you want to run your flow on Azure Container Instances (ACI), Prefect provides an ACI worker that can be used to dynamically scale your infrastructure. You can refer to the Azure Container Instances Worker Guide for more details. 6. Resources: - For more information, refer to the Prefect Azure documentation. This setup allows you to dynamically retrieve and execute a Python script stored in Azure Blob Storage using Prefect's orchestration capabilities.

marvin made up that last part https://docs.prefect.io/v3/deploy/infrastructure-concepts/store-flow-code#cloud-provider-storage