@Marvin Is there an example of how to use

parameter_openapi_schema

to setup the parameters for a deployment? I want to figure out how we can set up parameters for various deployments, where some sections of the parameters are shared between deployments

Here are some excerpts from the Prefect documentation that might help: 1. Creating deployments allow users to avoid creating this object themselves. For example, when updating a deployment schedule in the UI basic information such as a cron string or interval is all that's required. Triggers allow you to define event-based rules for running a deployment. For more information see Automations. The

parameter_openapi_schema

is an OpenAPI compatible schema that defines the types and defaults for the flow's parameters. More Info 2. You can run a single flow with different parameters, based on multiple schedules and triggers, and in different environments. This also enables you to run different versions of the same flow. More Info 3. To deploy a flow with its name, you can use the

prefect deploy

command with the

--name

flag followed by the flow name. To deploy all deployments you can use the

--all

flag. To deploy deployments that match a pattern you can use the

-n

flag followed by the pattern. More Info Please note that the

parameter_openapi_schema

is used to define the types and defaults for the flow's parameters, which can be used to set up parameters for various deployments. Some sections of the parameters can be shared between deployments by defining them in the OpenAPI schema.

@Marvin can you show me a small example of how to setup that schema? Is that in the prefect.yaml file or somewhere else?

I'm sorry, but I couldn't find a specific example of

parameter_openapi_schema

in the Prefect documentation or in the Prefect community forum. However, the

parameter_openapi_schema

should be a JSON object that follows the OpenAPI specification for defining parameters. Here's a very basic example:

yaml
parameters:
  parameter_openapi_schema:
    type: object
    properties:
      param1:
        type: string
        default: "default value"
      param2:
        type: integer
        default: 10

This schema defines two parameters,

param1

and

param2

, with their types and default values. You can add this to your Prefect configuration file or wherever you are defining your flow parameters. Please note that this is a general example and might not work directly in your Prefect setup. I would recommend reaching out to the Prefect community or support for more specific guidance.

@Marvin I’m working on getting a flow running on a k8s node with a gpu. I added the limits and requests to the deployment, and its showing up like this in the deployment configuration:

"limits": {
            "cpu": 1,
            "memory": "1Gi",
            "<http://nvidia.com/gpu|nvidia.com/gpu>": "1"
          },
          "requests": {
            "cpu": 1,
            "memory": "1Gi",
            "<http://nvidia.com/gpu|nvidia.com/gpu>": "1"

However, when it runs, it just runs on the default pool, which has no gpu. How would I go about making sure it runs on a different node, which has a gpu. And also, how would I request a specific type of gpu?

To ensure that your flow runs on a specific node with a GPU, you can use Kubernetes' node affinity or taints and tolerations features. Here's a brief overview: 1. Node Affinity: This allows you to constrain which nodes your pod is eligible to be scheduled on based on labels on the node. You can specify that your pod should only run on nodes with a GPU. 2. Taints and Tolerations: Nodes in Kubernetes can be "tainted" to repel certain pods. If you taint a node with a specific key-value pair, only pods with a toleration for that key-value pair can be scheduled on that node. You can taint your GPU nodes and add a corresponding toleration to your Prefect flow's pod specification. As for requesting a specific type of GPU, you can use the

<http://nvidia.com/gpu|nvidia.com/gpu>

resource name in your pod spec, as you've done. However, to request a specific model of GPU, you might need to use a device plugin that supports this feature, or label your nodes with the GPU model and use node affinity to schedule your pods on the appropriate nodes. In the context of Prefect, you would need to configure these settings in the environment where your flow runs. If you're using the Kubernetes agent, you can specify these settings in the

job_spec_file

that you provide to the agent. Please note that these are general Kubernetes concepts and might require additional configuration in your Kubernetes cluster. For more specific guidance, I would recommend referring to the Kubernetes documentation or reaching out to the Prefect community or support.

@Marvin can I use the prefect docker image with a different base image?

Yes, you can use the Prefect Docker image with a different base image. You have two options: 1. Base it off one of the provided

prefecthq/prefect

images: You can extend a Prefect-provided Docker image by adding additional dependencies or customization based on your requirements. The documentation provides an example of how to do this:

Dockerfile
   FROM prefecthq/prefect:2-latest
   RUN pip install scikit-learn

This example uses the

prefecthq/prefect:2-latest

image and adds

scikit-learn

to it. 2. Build your own image: You can also create your own Docker image from scratch using a completely different base image and install Prefect within it as needed. For detailed instructions and how the Prefect CLI can help you build custom images with dependencies specified in a

requirements.txt

file, you can refer to the Prefect Docker guide. Additionally, when creating work pools that use containers, you can specify an alternative image and also override the work pool image choice in individual deployments.