In Prefect, you can use variables to templatize deployment steps by referencing them in the

prefect.yaml

file used to create deployments. Variables in

.yaml

files are denoted by quotes and double curly brackets, like so:

"{{ prefect.variables.my_variable }}"

. For instance, if you want to specify which branch should be cloned for a deployment, you could set a variable in your

prefect.yaml

file that references the branch name. Here's a simplified example:

yaml
version: 1
name: MyFlow
storage:
  type: Git
  repo: my/repo
  ref: "{{ prefect.variables.branch_name }}"

In this example,

branch_name

is a variable that you would set in your deployment environment. The value of

branch_name

would be the name of the branch you want to clone for the deployment. Please note that if a template variable is the only value for a key in the

job_configuration

section and no value is provided for the template variable, the key will be removed from the

job_configuration

section. For more detailed information, you can refer to the following Prefect documentation pages: - Developing a new worker type - Variables - Block-based deployments Let me know if you need further assistance!

I see, you want to use job variables of a deployment to set variables in the

prefect.yaml

file. Here's how you can do it: In your

prefect.yaml

file, you can reference job variables using the

{{ $VARIABLE_NAME }}

syntax. For example, if you have a job variable named

BRANCH_NAME

, you can reference it in your

prefect.yaml

file like this:

yaml
version: 1
name: MyFlow
storage:
  type: Git
  repo: my/repo
  ref: "{{ $BRANCH_NAME }}"

In this case,

BRANCH_NAME

is a job variable that you would set in your deployment environment. The value of

BRANCH_NAME

would be the name of the branch you want to clone for the deployment. Please note that if a template variable is the only value for a key in the

job_configuration

section and no value is provided for the template variable, the key will be removed from the

job_configuration

section. For more detailed information, you can refer to the following Prefect documentation pages: - Developing a new worker type - Variables - Block-based deployments Let me know if you need further assistance!

You can use GitHub Actions to trigger a deployment when a pull request is opened in your repository. Here's a simple example:

yaml
name: Deploy on PR

on:
  pull_request:
    types: [opened]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Deploy
      run: |
        # Your deployment script here

In this workflow: - The

on

section specifies that the workflow should be triggered when a pull request is opened. - The

jobs

section defines a single job named

deploy

that runs on the latest version of Ubuntu. - The

steps

section defines the steps that should be executed as part of the job. In this case, the steps are: - Checkout the code using the

actions/checkout@v2

action. - Setup Python using the

actions/setup-python@v2

action. - Install the dependencies by running

pip install -r requirements.txt

. - Deploy the application. You would replace

# Your deployment script here

with the actual commands to deploy your application. You would need to commit this workflow file to your repository in the

.github/workflows

directory. For more detailed information, you can refer to the following resources: - Github Actions: a deep dive into pull_request - Frontside - Events that trigger workflows - GitHub Docs Let me know if you need further assistance!

Sure, here's an example of how you can use GitHub Actions to trigger a Prefect run when a pull request is opened in your repository. First, you'll need to install the Prefect CLI in your GitHub Actions workflow. Then, you can use the

prefect run flow

command to trigger a Prefect run. Here's a simple example:

yaml
name: Deploy on PR

on:
  pull_request:
    types: [opened]

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect

    - name: Trigger Prefect Run
      run: |
        prefect backend cloud
        prefect auth login --key ${{ secrets.PREFECT_API_KEY }}
        prefect run flow --name MyFlow --project MyProject

In this workflow: - The

on

section specifies that the workflow should be triggered when a pull request is opened. - The

jobs

section defines a single job named

deploy

that runs on the latest version of Ubuntu. - The

steps

section defines the steps that should be executed as part of the job. In this case, the steps are: - Checkout the code using the

actions/checkout@v2

action. - Setup Python using the

actions/setup-python@v2

action. - Install the Prefect CLI by running

pip install prefect

. - Trigger a Prefect run using the

prefect run flow

command. You would need to replace

MyFlow

and

MyProject

with the name of your flow and project, respectively. Also, you would need to store your Prefect API key as a secret in your GitHub repository and reference it in the workflow file as

secrets.PREFECT_API_KEY

. For more detailed information, you can refer to the following resources: - GitHub Actions Documentation - Prefect CLI Documentation Let me know if you need further assistance!

Yes, you can pass parameters as environment variables in your GitHub Actions workflow. Here's how you can do it: First, you need to define the environment variables in the

env

section of your job. Then, you can reference these environment variables in your steps using the

env

context. Here's an example:

yaml
name: Deploy on PR

on:
  pull_request:
    types: [opened]

jobs:
  deploy:
    runs-on: ubuntu-latest
    env:
      FLOW_NAME: MyFlow
      PROJECT_NAME: MyProject

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect

    - name: Trigger Prefect Run
      run: |
        prefect backend cloud
        prefect auth login --key ${{ secrets.PREFECT_API_KEY }}
        prefect run flow --name $FLOW_NAME --project $PROJECT_NAME

In this workflow: - The

env

section defines two environment variables:

FLOW_NAME

and

PROJECT_NAME

. - The

Trigger Prefect Run

step references these environment variables using the

env

context. You would need to replace

MyFlow

and

MyProject

with the name of your flow and project, respectively. Also, you would need to store your Prefect API key as a secret in your GitHub repository and reference it in the workflow file as

secrets.PREFECT_API_KEY

. For more detailed information, you can refer to the following resources: - GitHub Actions Documentation - Prefect CLI Documentation Let me know if you need further assistance!

To pass parameters to a Prefect flow run, you can use the

--parameters

option of the

prefect run flow

command. The parameters should be specified as a JSON object. Here's an example:

yaml
name: Deploy on PR

on:
  pull_request:
    types: [opened]

jobs:
  deploy:
    runs-on: ubuntu-latest
    env:
      FLOW_NAME: MyFlow
      PROJECT_NAME: MyProject
      PARAM1: value1
      PARAM2: value2

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install prefect

    - name: Trigger Prefect Run
      run: |
        prefect backend cloud
        prefect auth login --key ${{ secrets.PREFECT_API_KEY }}
        prefect run flow --name $FLOW_NAME --project $PROJECT_NAME --parameters '{"param1":"'$PARAM1'", "param2":"'$PARAM2'"}'

In this workflow: - The

env

section defines two additional environment variables:

PARAM1

and

PARAM2

. - The

Trigger Prefect Run

step passes these environment variables as parameters to the Prefect flow run. You would need to replace

MyFlow

,

MyProject

,

value1

, and

value2

with the name of your flow, project, and the values of your parameters, respectively. Also, you would need to store your Prefect API key as a secret in your GitHub repository and reference it in the workflow file as

secrets.PREFECT_API_KEY

. For more detailed information, you can refer to the following resources: - GitHub Actions Documentation - Prefect CLI Documentation Let me know if you need further assistance!

In your Prefect flow, you can use Prefect's

Parameter

task to pick up the parameters. Here's an example:

from prefect import Flow, Parameter

# Define your parameters
param1 = Parameter('param1')
param2 = Parameter('param2')

# Define your flow
with Flow('MyFlow') as flow:
    # Use your parameters
    result = my_task(param1, param2)

In this example,

param1

and

param2

are

Parameter

tasks. When you run your flow with parameters, the values of these parameters will be the values you passed in. You can then use these parameters in your tasks. In this example,

my_task

is a task that takes two arguments. When the flow is run,

my_task

will be called with

param1

and

param2

as arguments. For more detailed information, you can refer to the following Prefect documentation page: - Parameters Let me know if you need further assistance!

In Prefect V2, you can use the

Parameter

task in a similar way to V1 to import parameters into your flow. Here's an example:

from prefect import Flow, Parameter

# Define your parameters
param1 = Parameter('param1')
param2 = Parameter('param2')

# Define your flow
with Flow('MyFlow') as flow:
    # Use your parameters
    result = my_task(param1, param2)

In this example,

param1

and

param2

are

Parameter

tasks. When you run your flow with parameters, the values of these parameters will be the values you passed in. You can then use these parameters in your tasks. In this example,

my_task

is a task that takes two arguments. When the flow is run,

my_task

will be called with

param1

and

param2

as arguments. For more detailed information, you can refer to the following Prefect documentation page: - Parameters Let me know if you need further assistance!

In the code you provided, the parameter is

date

. It's an optional parameter of the

what_day_is_it

function, which is decorated with the

@flow

decorator to make it a Prefect flow. The

date

parameter is of type

datetime

, but it has a default value of

None

. If no value is provided when calling the

what_day_is_it

function (or running the flow), the current date and time (in UTC) will be used, as indicated by the line

date = datetime.utcnow()

. When you call

what_day_is_it("2021-01-01T02:00:19.180906")

, you're passing a string to the function, which is not a

datetime

object. If you want to pass a

datetime

object, you should parse the string to a

datetime

object first. Here's how you can do it:

from datetime import datetime
from prefect import flow

@flow
def what_day_is_it(date: datetime = None):
    if date is None:
        date = datetime.utcnow()
    print(f"It was {date.strftime('%A')} on {date.isoformat()}")

date_str = "2021-01-01T02:00:19.180906"
date_obj = datetime.fromisoformat(date_str)

what_day_is_it(date_obj)
# It was Friday on 2021-01-01T02:00:19.180906

In this modified version of your code, the string

date_str

is parsed to a

datetime

object

date_obj

using the

datetime.fromisoformat

function. Then,

date_obj

is passed to the

what_day_is_it

function.

In Prefect, you can specify parameters in the

prefect.yaml

file which is used by the

prefect deploy

command to deploy your flows. Here's an example of how you can do it:

yaml
# Generic metadata about this project
name: flows
prefect-version: 2.13.8

# build section allows you to manage and build docker images
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      requires: prefect-docker>=0.4.0
      image_name: "{{ $PREFECT_IMAGE_NAME }}"
      tag: latest
      dockerfile: auto
      platform: "linux/amd64"

# push section allows you to manage if and how this project is uploaded to remote locations
push:
  - prefect_docker.deployments.steps.push_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: "{{ build-image.image_name }}"
      tag: "{{ build-image.tag }}"

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: /opt/prefect/flows

# the definitions section allows you to define reusable components for your deployments
definitions:
  tags: &common_tags
    - "eks"

In this example, the

image_name

and

tag

in the

build

section, and the

directory

in the

pull

section are specified as parameters. These parameters are denoted by quotes and double curly brackets, like so:

"{{ $PREFECT_IMAGE_NAME }}"

. You can replace

$PREFECT_IMAGE_NAME

with the name of your environment variable. The value of this environment variable would be the name of the image you want to build for the deployment. For more detailed information, you can refer to the following Prefect documentation pages: - Kubernetes - Prefect Deploy Let me know if you need further assistance!

In the

deployments

section of the

prefect.yaml

file, you can specify parameters for each deployment. Here's an example:

yaml
deployments:
  - name: MyFlow
    kind: flow
    flow_file: my_flow.py
    image_name: "{{ $IMAGE_NAME }}"
    image_tag: "{{ $IMAGE_TAG }}"
    parameters:
      param1: "{{ $PARAM1 }}"
      param2: "{{ $PARAM2 }}"

In this example: -

name

is the name of the deployment. -

kind

is the kind of the deployment. In this case, it's a flow. -

flow_file

is the Python file that defines the flow. -

image_name

and

image_tag

are the name and tag of the Docker image that should be used for the deployment. They are specified as parameters. -

parameters

is a dictionary of parameters that should be passed to the flow when it's run. In this case,

param1

and

param2

are specified as parameters. You can replace

$IMAGE_NAME

,

$IMAGE_TAG

,

$PARAM1

, and

$PARAM2

with the names of your environment variables. The values of these environment variables would be the name and tag of the Docker image, and the values of the parameters, respectively. For more detailed information, you can refer to the following Prefect documentation pages: - Kubernetes - Prefect Deploy Let me know if you need further assistance!

Based on the information I found, parameters defined in the

prefect.yaml

file should be visible in the Prefect UI when you deploy the flow. If the parameters are not showing up, it could be due to a few reasons: 1. There might be an issue with the Prefect version you're using. Ensure you're using a version that supports parameters in the

prefect.yaml

file. 2. There could be an issue with the formatting or indentation of the

prefect.yaml

file. YAML files are sensitive to indentation, so ensure that the

parameters

section is properly indented. 3. If you've recently updated the

prefect.yaml

file, make sure to redeploy the flow for the changes to take effect. 4. If you want the Prefect API to verify the parameter values passed to a flow run against the schema defined by

parameter_openapi_schema

, set

enforce_parameter_schema

to

true

. If none of these suggestions work, I would recommend reaching out to Prefect support or checking their documentation for more information. Here's the corrected version of your YAML snippet:

yaml
- name: dbt_prod_full_refresh_test
  version: 2.0
  tags:
    - data-transformation
    - test
    - full-refresh
  description: ""
  entrypoint: flows/dbt/run_dbt.py:trigger_dbt_flow
  parameters:
    param1: "test"

Let me know if you need further assistance!

The error message you're seeing suggests that the function you're trying to run as a flow doesn't expect any parameters, but you're trying to pass a parameter to it. In Prefect, a flow is like a function. It can take inputs (parameters), perform work, and return an output. You can turn any function into a Prefect flow by adding the

@flow

decorator. When you define a flow, you specify the parameters it expects. If you try to run a flow with a parameter that it doesn't expect, you'll get an error. Here's an example of how you can define a flow that expects a parameter:

from prefect import Flow, Parameter

@Flow
def my_flow(param1: str):
    print(f"param1: {param1}")

my_flow.run(param1="test")

In this example,

my_flow

is a flow that expects a parameter

param1

. When you run this flow with

my_flow.run(param1="test")

, it will print

param1: test

. If you try to run this flow with a parameter that it doesn't expect, like

my_flow.run(param2="test")

, you'll get an error because

my_flow

doesn't expect a parameter

param2

. So, you should check the function you're trying to run as a flow and make sure it expects the parameters you're trying to pass to it. If it doesn't, you should modify the function to expect these parameters. Let me know if you need further assistance!

In your current setup, the

clone_repository

task is expecting a parameter

branch

, but it's not being provided when the task is called in the

transform_data

flow. To pass parameters to a task within a flow, you can use Prefect's

Parameter

task. Here's how you can modify your flow to pass a parameter to the

clone_repository

task:

from prefect import Flow, Parameter, task

@task(name="Clone repo", log_prints=True)
def clone_repository(branch: str = "master"):
    repo_url = f"https://{username}:{pat}@github.com/{organization}/{repository}.git"
    destination_dir = "abc"
    clone_repo = git.Repo.clone_from(repo_url, destination_dir, branch=branch)
    return clone_repo

@flow(name="Data Transformation", log_prints=True)
def transform_data():
    branch = Parameter('branch', default='master')
    clone_repository(branch)

In this example,

branch

is a

Parameter

task. When you run your flow with parameters, the value of this parameter will be the value you passed in. You can then use this parameter in your tasks. In this example,

clone_repository

is a task that takes one argument. When the flow is run,

clone_repository

will be called with

branch

as an argument. Now, when you run the flow, you can pass the branch name as a parameter:

transform_data.run(parameters={'branch': 'my-branch'})

This will clone the repository from the 'my-branch' branch. Let me know if you need further assistance!