@Marvin When using prefect.yaml for deployments, can we put multiple deployment definition in separate YAML files and still refer to common values in the prefect.yaml file?

In Prefect 2.x, multiple deployment definitions can be placed in separate

prefect.yaml

files, and you can have

prefect.yaml

files with the same name in different directories. By using the

--prefect-file

CLI argument, you can specify which deployment file to use when deploying:

plaintext
prefect deploy --prefect-file path/to/my_file.yaml

Regarding referencing common values in

prefect.yaml

files, you can make use of Prefect blocks, variables, and environment variables to reference shared information: - Prefect blocks can be referenced with

{{ prefect.blocks.block_type.block_slug }}

- Prefect variables with

{{ prefect.variables.variable_name }}

- Environment variables with

{{ $MY_ENV_VAR }}

These references can be used to avoid hardcoding sensitive information and to use reusable pieces of information such as default work pool names or image names. For more details, please refer to Prefect's deployment documentation: Prefect Deploy.

Can prefect CLI update those individual YAML files?

The Prefect CLI does not directly update the contents of your

prefect.yaml

files. The modifications to

prefect.yaml

need to be made manually. The CLI can be used to deploy your flows based on the

prefect.yaml

file, but if you want to change the configuration such as deployment names, image tags, schedules, etc., you will have to edit the file yourself. However, you can use YAML aliases and anchors in your

prefect.yaml

file to define reusable components, which can be a way to make updating your deployment configurations more efficient. You can define common values or configurations in a

definitions

section and then reference them using the

*{alias_name}

syntax in your deployments. For example:

yaml
definitions:
  schedules:
    every_ten_minutes: &every_10_minutes
      interval: 600

deployments:
  - name: my-first-deployment
    schedule: *every_10_minutes
    # other deployment settings
  - name: my-second-deployment
    schedule: *every_10_minutes
    # other deployment settings

To initialize your deployment configuration and create the

prefect.yaml

file, you can use the following command:

plaintext
prefect init

If you intend to generate a

prefect.yaml

with a specific structure, you can be prompted with available recipes during initialization or use the

--recipe

flag to specify one. For details on how to use the CLI for deploying and creating aliases and anchors, you can refer to the Prefect documentation here: Prefect Deploy.

@Marvin will the flow.deploy() approach update the prefect.yaml files?

The

flow.deploy()

function in Prefect is used to deploy flows programmatically, and it does not update the

prefect.yaml

files automatically. When you define and deploy a flow using Python code with

flow.deploy()

, these deployments are managed independently of

prefect.yaml

files. The

prefect.yaml

file is typically used to store and manage deployment configurations, and you can use it to deploy your flows via the CLI or other CI/CD processes. But any changes to the deployment configurations when deploying flows with

flow.deploy()

should be reflected manually in your

prefect.yaml

if you wish to keep it in sync. Therefore, while you can create and update deployments with

flow.deploy()

, you would need to manually ensure that any desired changes are also made in the

prefect.yaml

file if you're using it for deployment management. If you aim to manage your deployments mainly through the YAML files, you'd typically edit them directly and use the CLI to apply the changes. For more detailed information on deploying flows programmatically, please refer to Prefect's deployment documentation: Prefect Deploy.

@Marvin Can you show some examples on how flow.deploy() or prefect CLI uses deployment configurations in prefect.yaml?

Below are examples showcasing the use of

flow.deploy()

and Prefect CLI with deployment configurations in

prefect.yaml

. Example for

flow.deploy()

usage:

python
from prefect import flow, get_deployment

@flow(log_prints=True)  # send `print` statements as INFO logs
def my_workflow():
    print("I can call any python code here, including prefect tasks or flows")

if __name__ == "__main__":
    my_workflow()  # call it like a normal python function

    # Deploy the flow to Prefect using flow configuration in your code
    deployment = my_workflow.deploy(
        name="my-deployment-name",
        work_queue_name="my-work-queue",
        description="An example deployment"
    )
    
    # Optionally, get and view the just-created Deployment object
    deployment_from_storage = get_deployment(deployment_name="my-deployment-name")
    print(deployment_from_storage)

More detailed example code can be found in this GitHub Repository. Example for using Prefect CLI with `prefect.yaml`: The code example provided was identical to the one for

flow.deploy()

, indicating that there was no specific example for using Prefect CLI with

prefect.yaml

. Typically, the Prefect CLI is used to deploy flows based on deployment configurations defined in

prefect.yaml

file, for example:

plaintext
prefect deploy --name my-deployment-name

This command deploys a flow using the deployment named

my-deployment-name

that is defined within

prefect.yaml

. To edit the

prefect.yaml

and reflect the desired deployment configurations prior to using the Prefect CLI, you would directly update the file. The CLI reads from the YAML file to understand the deployment settings during the deployment initiation process. Unfortunately, it seems we didn’t find a direct example for using Prefect CLI with

prefect.yaml

in the provided sources. However, referring to the official Prefect Documentation could provide more concrete examples and detailed usage.

@Marvin does flow.deploy() generate anything or does it talk to Prefect API?

The

flow.deploy()

function in Prefect does not generate files. Instead, it interacts with the Prefect API to create a deployment object for your flow. The deployment object includes configuration details about the flow, such as: - The name of the deployment - The work queue or work pool where the flow will be executed - Execution schedules - Parameter defaults - Tags, descriptions, and other metadata When you call

flow.deploy()

, it programmatically creates this deployment configuration within the Prefect API, allowing you to manage and observe the flow from the Prefect UI or CLI. The flow is not uploaded to remote storage as part of this process - that requires using the Prefect CLI with the

prefect deployment build

command. Below is an example from the documentation that demonstrates how to create a deployment with `flow.deploy()`:

python
from prefect import flow

@flow
def my_example_flow():
    # Flow definition here

if __name__ == "__main__":
    # Create deployment when script is executed
    deployment = my_example_flow.deploy(
        name="my-deployment-name",
        work_queue_name="my-work-queue",
        description="An example deployment"
    )

In this example,

flow.deploy()

is used to configure the deployment inside the Prefect platform, and subsequently, this configuration is managed within Prefect's ecosystem. For more in-depth information regarding deployments and

flow.deploy()

, please refer to the Prefect documentation.

@Marvin in prefect deploy CLI, what’s the difference between --variable and --job-variable?

Based on the information obtained, it appears there might be a misunderstanding, as there aren't distinguishable results specifically for

--variable

and

--job-variable

in the context of the

prefect deploy

CLI command in the documentation provided. It's possible that

--variable

may not be a standard option for the

prefect deploy

command. However, the documentation does mention

job_variables

, which can be specified in the

prefect.yaml

file under deployments to set environment variables for the deployment:

yaml
deployments:
- name: demo-deployment
  entrypoint: demo_project/demo_flow.py:some_work
  work_pool:
    name: local
  job_variables:
    env:
      EXECUTION_ENVIRONMENT: staging
      MY_NOT_SO_SECRET_CONFIG: plumbus

Job variables are used to provide environment variables for the deployment job within the worker's execution environment. These variables can include non-sensitive deployment configuration information such as execution environment settings or other configuration options. If you execute the

prefect deploy

command and wish to set these variables at the time of deployment, you would modify the deployment specification in your

prefect.yaml

file and deploy with the CLI, referencing the deployment name:

plaintext
prefect deploy -n demo-deployment

For a more detailed explanation and options to override these variables at runtime, please refer to the guide on overriding job variables. If you require information about a specific

--variable

option, please consult the official Prefect 2.x documentation or reach out to the Prefect community for more information.

@Marvin show an example on running kubernetes worker

The example obtained does not specifically demonstrate running a Kubernetes worker. However, I can provide you with a general guide on how to create and run a Kubernetes worker with Prefect 2.x since workers have replaced agents in Prefect 2.x. To run a flow with a Kubernetes worker, you might typically follow these steps: 1. Configure a work queue in Prefect to be associated with your Kubernetes workers. 2. Create a Kubernetes deployment or use the Prefect Helm chart to deploy a Kubernetes worker pod. 3. The Kubernetes worker pod will poll the work queue for flow runs to execute. 4. When the worker picks up a flow run, it creates a new Kubernetes job to execute the flow run. Here's a pseudo-code example of how to set up and use a Kubernetes worker:

yaml
# Kubernetes deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-kubernetes-worker
spec:
  replicas: 1
  selector:
    matchLabels:
      app: prefect-kubernetes-worker
  template:
    metadata:
      labels:
        app: prefect-kubernetes-worker
    spec:
      containers:
      - name: worker
        image: prefecthq/prefect:2.0-python3.9 # Or the appropriate image tag for your environment
        env:
          - name: PREFECT__CLOUD__WORK_QUEUE # Set the name of the work queue
            value: "my-work-queue"
        command:
          - prefect
          - worker
          - start

Please replace

"my-work-queue"

with the name of the work queue you've created in Prefect Cloud or Prefect Server. To apply this to your cluster, you would save this file and run:

bash
kubectl apply -f deployment.yaml

For a specific code example tailored to your environment and use case, you would need to consult the Prefect documentation or reach out to the Prefect community since running Kubernetes workers depends on your cluster configuration and Prefect Cloud/Server setup. For setting up the work queue and other configurations, please see the Prefect documentation. It's also advisable to use the Prefect Helm chart, which simplifies the deployment process on Kubernetes. The chart and its documentation can be found in the Prefect GitHub repository.

@Marvin with Prefect worker in Kubernetes, how to configure different memory requests for different flows? Assuming we have a base job manifest.

To configure different memory requests for different flows when using a Prefect worker in Kubernetes, you can modify the base job manifest by specifying the desired memory requests under the

resources

section for each flow's deployment configuration. Below is an example of how you might update your base job manifest to allow for different memory requests:

yaml
# In the base job manifest (base-job-manifest.yaml)
apiVersion: batch/v1
kind: Job
metadata:
  name: prefect-job-{{ flow_run_id }}
spec:
  template:
    spec:
      containers:
      - name: flow-container
        image: my-prefect-image:latest
        resources:
          requests:
            memory: "{{ memory_request }}" # This uses a template variable

When creating the deployment for a Prefect flow, you would provide the

memory_request

as a variable. Here’s how you could customize your Kubernetes work pool and include the

memory_request

within the Prefect deployment configuration:

python
from prefect.deployments import DeploymentSpec
from prefect.flow_runners import KubernetesFlowRunner

# You can then define deployments with customized memory requests
DeploymentSpec(
    flow=flow_definition,
    name="high-memory-flow",
    flow_runner=KubernetesFlowRunner(
        job_template_path="base-job-manifest.yaml",
        job_template_vars={"memory_request": "4Gi"}  # Custom memory request for this flow
    ),
    tags=["k8s", "high-memory"],
)

DeploymentSpec(
    flow=other_flow_definition,
    name="default-memory-flow",
    flow_runner=KubernetesFlowRunner(
        job_template_path="base-job-manifest.yaml",
        job_template_vars={"memory_request": "1Gi"}  # Default memory request for this flow
    ),
    tags=["k8s", "default-memory"],
)

In this example, the

KubernetesFlowRunner

accepts a

job_template_path

which specifies the path to the job manifest template, and

job_template_vars

which allows you to interpolate the memory request into the manifest at runtime. Each deployment specifies a different memory request by assigning a different value to the

memory_request

variable. When the flow run is triggered, the worker will use this customized base job manifest to launch a Kubernetes job with the specified memory request. Remember to replace

flow_definition

and

other_flow_definition

with actual flow definitions in your code. Adjust the memory values in the

DeploymentSpec

as needed for each of your flows. For more detailed guidance and options, please refer to this Kubernetes work pool section in the Prefect documentation: Prefect Deployment Kubernetes.