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# ask-marvin
a
@Marvin Any recommendations for hosting a local Prefect server on Windows? And with running multiple flows locally after server's set up?
m
To host a local Prefect server on Windows and run multiple flows locally, follow these general steps. This guidance is relevant for both Prefect 2.x and 3.x, with some distinctions provided. ### Hosting a Local Prefect Server #### Prefect 2.x: 1. Install Prefect: Ensure you have Prefect installed. You can install it via pip:
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pip install prefect
2. Start the Prefect Server: Use the CLI command to start the server:
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prefect server start
- This spins up a local Prefect server UI, accessible at 
<http://127.0.0.1:4200>
. 3. Stop the Prefect Server: Use 
Ctrl + C
in the terminal to shut down the server when done. More detailed instructions can be found on the Prefect 2.x documentation page: Host a Prefect server instance #### Prefect 3.x: 1. Install Prefect:
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pip install prefect
2. Start the Prefect Server: Similar to Prefect 2.x, start the server with:
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prefect server start
- Access the local Prefect server UI at 
<http://127.0.0.1:4200>
. 3. Stop the Prefect Server: Terminate the process with 
Ctrl + C
. Details are available at: Prefect 3.x Self-Hosting Guide ### Running Multiple Flows Locally #### Prefect 2.x: You can use the 
serve
utility to run multiple flows from the same process:
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python
import time
from prefect import flow, serve

@flow
def slow_flow(sleep: int = 60):
    time.sleep(sleep)

@flow
def fast_flow():
    return

if __name__ == "__main__":
    slow_deploy = slow_flow.to_deployment(name="sleeper", interval=45)
    fast_deploy = fast_flow.to_deployment(name="fast")
    serve(slow_deploy, fast_deploy)
For more details, refer to: Prefect 2.x Flow Documentation #### Prefect 3.x: Running multiple flows locally with the 
serve
utility is similar:
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python
import time
from prefect import flow, serve

@flow
def slow_flow(sleep: int = 60):
    time.sleep(sleep)

@flow
def fast_flow():
    return

if __name__ == "__main__":
    slow_deploy = slow_flow.to_deployment(name="sleeper", interval=45)
    fast_deploy = fast_flow.to_deployment(name="fast")
    serve(slow_deploy, fast_deploy)
More information can be found at: Prefect 3.x Flow Deployment Examples These steps should help you get started with hosting a local Prefect server and running multiple flows on Windows. If you need any further assistance, feel free to ask!
a
@Marvin When I have multiple projects, each with possibly more than one flow, what's the best way to deploy them? Is using workers and worker pools overkill if flows run at most twice a day and aren't compute-intensive?
m
When managing multiple projects each with multiple flows, the decision to use workers and worker pools depends on your operational needs and infrastructure. Here are some guidelines to help you decide: ### Using Workers and Worker Pools Both Prefect 2.x and 3.x support the concept of workers and worker pools, which manage the execution of flows. These features are highly flexible and suitable for scaling and managing complex workflows but may be overkill for infrequent and non-intensive flows. #### Prefect 2.x: - Work Pools & Workers: Work pools and workers bridge the Prefect orchestration environment with your execution environment. When a deployment creates a flow run, it is submitted to a specific work pool for scheduling. - Work Pools help coordinate deployments with workers through a known channel, similar to "topics" in a pub/sub system. - Workers poll their respective work pool for new runs to execute. Reference: Work Pools Documentation #### Prefect 3.x: - Work Pools: Each type of work pool is optimized for different use cases, allowing you to manage the distribution and execution of flows across environments and infrastructures. Work pools can coordinate deployments with workers, where workers request work from specific pools and execute flow runs based on those deployments. Reference: Work Pools in Prefect 3.x ### Deploying Multiple Flows #### Prefect 2.x: - You can use the CLI to deploy multiple flows:
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sh
  prefect deploy --name my_flow/deployment-1 --name my_other_flow/deployment-1
- YAML configuration can be reused across deployments to share work pool configurations, deployment actions, and other settings. Reference: Prefect 2.x Deploy Guide #### Prefect 3.x: - You can deploy multiple flows from a single or multiple Python files using the 
deploy
function:
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python
  from prefect import deploy, flow

  @flow(log_prints=True)
  def buy():
      print("Buying securities.")

  @flow(log_prints=True)
  def sell():
      print("Selling securities.")

  if __name__ == "__main__":
      deploy(
          buy.to_deployment(name="buy-deploy"),
          sell.to_deployment(name="sell-deploy"),
          work_pool_name="my-docker-pool",
      )
This code will create two deployments from different flows. Reference: Prefect 3.x Deployment Examples ### Should You Use Workers and Worker Pools? Given your scenario: - Flow frequency: If flows run at most twice a day. - Compute intensity: If flows are not compute-intensive. Recommendation: Using workers and worker pools could be overkill if the flows are infrequent and not resource-demanding. For a simpler setup: 1. Local Execution: Execute flows directly using local Python scripts. 2. Scheduled Runs: Use cron jobs or Prefect’s built-in scheduling to trigger flows. However, if you anticipate growth in the number of flows or their complexity, utilizing workers and worker pools will provide a more scalable and manageable solution. Feel free to ask if you have more questions or need specific examples tailored to your workflow!
a
@Marvin Suppose the server needs to restart periodically (e.g. every 2 weeks). How should I set up deployments so it's easy to restart and maintain all of them after server restart?
m
To ensure smooth restarts and maintenance of deployments after a Prefect server restart, both Prefect 2.x and 3.x offer features and best practices to manage this scenario. Here’s a consolidated approach: ### Prefect 2.x 1. Use Backup & Restore: Regularly backup your Prefect database to ensure you can restore deployments and flow states after a restart. 2. Prefab Deployment Configuration: Use 
prefect.yaml
or similar configuration files to define your deployments. This allows you to redeploy your flows easily if needed. 3. Scheduled Flows: Ensure your scheduled flows are configured correctly so they can pick up post-restart without manual intervention.
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yaml
   schedules:
     - cron: "0 0 * * *"  # Example cron schedule
       timezone: "America/Chicago"
       active: true
4. Use CI/CD and Automation: Integrate Prefect deployment commands into your CI/CD pipeline to automatically redeploy flows on server restart.
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sh
   prefect deploy --all
More details on managing schedules and deployments: Prefect Scheduling Prefect Deployment ### Prefect 3.x 1. Persisting State: Ensure that your state is persisted to a durable storage that can survive server restarts (e.g., a PostgreSQL database). 2. Use the 
pause_on_shutdown
flag: Use the 
pause_on_shutdown
flag to prevent interruptions due to server restarts.
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python
   if __name__ == "__main__":
       my_flow.serve(pause_on_shutdown=False)
3. Deploy Multiple Flows: Use the 
deploy
function for managing multiple deployments, which can be re-applied after server restarts.
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python
   from prefect import deploy, flow

   @flow(log_prints=True)
   def my_flow():
       print("Running flow logic")

   if __name__ == "__main__":
       deploy(
           my_flow.to_deployment(name="my-first-deployment"),
       )
4. Automated Restart Handling: Implement deployment scripts in your CI/CD pipeline for automatic redeployment after a server restart.
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sh
   prefect deployment apply deployment.yaml
More details on running flows and deployments: Prefect 3.x Deployment Upgrade Agents to Workers ### GitHub Issues and Enhancements 1. Using Deployments Post Server Restart: To automatically maintain and redeploy flows, ensure your deployment scripts are part of your version control system and CI/CD pipeline. 2. Handling Flow States and Jobs: Manage flow states and jobs efficiently to ensure minimal disruption when the server restarts. Utilize Prefect's features to resume states where possible. Additional helpful discussion and insights can be found in related GitHub issues: Flow Still in "Running" State when Agent is Down By taking these steps, you can have a resilient setup that gracefully handles server restarts and maintains your deployments with minimal manual intervention.
a
@Marvin What's the benefit of building Docker images for deployments via 
deploy()
vs. deploying via 
serve()
?
m
Both 
deploy()
and 
serve()
methods in Prefect offer different advantages based on your deployment needs and infrastructure requirements. Here's a detailed comparison of the benefits of each approach: ### Building Docker Images for Deployments via 
deploy()
1. Isolation and Consistency: - Isolation: Docker containers provide an isolated environment, ensuring that the dependencies and environment configured for your flow are consistent across different machines and stages (development, staging, production). - Consistency: Building Docker images ensures that the exact same environment (including OS, libraries, and tools) is used every time the flow runs, mitigating "works on my machine" issues. 2. Scalability: - Docker images can be easily deployed across various environments and orchestrators like Kubernetes, ECS, and GKE, facilitating easy scaling. - Prefect’s workers and work pools can pull and run these images, leveraging the infrastructure's scaling capabilities. 3. Version Control: - Images can be tagged and versioned, allowing you to track changes to your deployment environment over time. - Lets you roll back to a previous image version if a new deployment introduces issues. 4. Reusability: - Common base images can be created and shared across multiple projects, reducing redundancy and improving maintainability. - Useful for environments where each deployment may need a unique set of libraries and dependencies. 5. Deployment Best Practices:
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python
   from prefect import deploy, flow

   @flow(log_prints=True)
   def my_flow():
       print("Running my flow")

   if __name__ == "__main__":
       deploy(
           my_flow.to_deployment(name="my-first-deployment", image="my-registry/my-image:latest"),
       )
This approach highlights specifying images to ensure that proper environments are always used. ### Deploying via 
serve()
1. Simplicity: - Quick Setup: 
serve()
is straightforward to set up and does not require learning Docker or containerization concepts. - Local Development: Ideal for local development and testing where you might not need the isolation and overhead of Docker. 2. Flexibility: - Dynamic Execution: Allows for dynamic execution and scheduling of flows without needing to build and push images. - Immediate Changes: Changes in code can immediately be served without the need for rebuilding Docker images. 3. Resource Light: - Suitable for flows that are not resource-intensive or do not have stringent isolation requirements. - Reduces overhead associated with building, storing, and pulling Docker images. 4. Simplified Workflow:
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python
   import time
   from prefect import flow, serve

   @flow(log_prints=True)
   def my_flow():
       print("Running my flow")

   if __name__ == "__main__":
       serve(my_flow.to_deployment(name="my-first-deployment"))
### Comparing the Two #### Use 
deploy()
with Docker Images When: - You need consistent environments across multiple stages (dev, staging, prod). - Flows depend on specific OS-level libraries or tools. - Scalability and isolation are critical. - You are using orchestrators like Kubernetes. #### Use 
serve()
When: - Flows are being developed or tested locally. - You require a quick and simple setup. - Flows are not resource-intensive and do not need strict environment isolation. - You want to avoid Docker-related overhead. ### Conclusion The choice between 
deploy()
with Docker and 
serve()
should be guided by your project requirements, scalability needs, and deployment environments. Docker provides robust isolation and scalability, ideal for production environments, while 
serve()
offers simplicity and speed, suitable for development and lightweight tasks.
a
@Marvin Suppose I have different subfolders and at least one deployment defined by 
<http://flow.to|flow.to>_deployment()
for each one. In my main folder, I'm importing all deployments from each subfolder and serving them all at once. How do I make this work, considering the different constants, environment variables and library prerequisites per folder?
m
To handle multiple deployments from different subfolders with distinct constants, environment variables, and library prerequisites, you need to structure your project in a way that allows each subfolder to define its own environment and dependencies. Below are the steps and considerations to achieve this setup effectively, using Prefect’s capabilities: ### Project Structure Organize your project such that each subfolder contains its own dependencies and environmental settings:
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project_root/
│
├── subfolder1/
│   ├── flow1.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
├── subfolder2/
│   ├── flow2.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
└── main.py
### Step-by-Step Setup 1. Isolate Environment Variables: Use 
.env
files in each subfolder to isolate environment variables. To load these variables, you can use a library like 
python-dotenv
. Example in `subfolder1/.env`:
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VAR1=value1
2. Dependencies: Each subfolder should have its own 
requirements.txt
with specific dependencies. 3. Define Flows and Deployments in Each Subfolder: Ensure each subfolder’s 
flow.py
defines its flows and deployments. Example in `subfolder1/flow1.py`:
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python
   from prefect import flow

   @flow
   def flow1():
       print("Flow 1 running")
   
   if __name__ == "__main__":
       flow1.to_deployment(name="flow1_deployment")
4. Main Script to Serve All Deployments: Your 
main.py
should aggregate and serve all deployments. It should handle loading environment variables and installing dependencies dynamically. Example in `main.py`:
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python
   import os
   import subprocess
   from dotenv import load_dotenv

   # Helper function to install dependencies
   def install_requirements(requirements_file):
       subprocess.check_call([os.sys.executable, "-m", "pip", "install", "-r", requirements_file])

   # List of subfolders
   subfolders = ["subfolder1", "subfolder2"]

   # Iterate over subfolders to set up environment and serve deployments
   for folder in subfolders:
       # Load environment variables
       dotenv_path = os.path.join(folder, ".env")
       if os.path.exists(dotenv_path):
           load_dotenv(dotenv_path)

       # Install dependencies
       requirements_path = os.path.join(folder, "requirements.txt")
       if os.path.exists(requirements_path):
           install_requirements(requirements_path)

       # Import and serve deployments
       deployment_module = f"{folder}.flow"
       deploy_command = [
           os.sys.executable,
           "-c",
           f"from {deployment_module} import flow1; flow1.to_deployment().serve()"
       ]
       subprocess.Popen(deploy_command)
### Key Considerations 1. Environment Separation: Each subfolder’s environment variables and dependencies are isolated and loaded as required. 2. Dynamic Dependency Installation: Dependencies specific to each flow are installed dynamically at runtime. 3. Aggregate Serving: The main script dynamically imports and serves deployments from each subfolder, ensuring that all flows are available. 4. Concurrent or Sequential Execution: Adjust the script to run deployments concurrently (using 
subprocess.Popen
) or sequentially (using 
subprocess.call
) based on your need. By following these steps, you can manage different subfolders with distinct constants, environment variables, and library prerequisites, and serve all deployments at once through a centralized main script. If you need any further assistance or specific code examples, feel free to ask!
a
@Marvin Edit the steps if one flow has multiple deployments.
m
Certainly! If each flow has multiple deployments, you'd need to ensure that all deployments for a given flow are properly defined and served. Here’s an updated approach that considers multiple deployments per flow: ### Project Structure Organize your project with separate subfolders for each component, ensuring each has its own environment and dependencies:
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project_root/
│
├── subfolder1/
│   ├── flow1.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
├── subfolder2/
│   ├── flow2.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
└── main.py
### Step-by-Step Setup 1. Isolate Environment Variables: Continue to use 
.env
files in each subfolder for isolation. Example in `subfolder1/.env`:
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VAR1=value1
2. Dependencies: Each subfolder should have its own 
requirements.txt
. 3. Define Multiple Deployments in Each Flow: Each subfolder’s 
flow.py
can define multiple deployments. Example in `subfolder1/flow1.py`:
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python
   from prefect import flow, Deployment

   @flow
   def flow1():
       print("Flow 1 running")

   if __name__ == "__main__":
       deployment1 = Deployment.build_from_flow(flow=flow1, name="flow1_deployment_1", parameters={"param": "value1"})
       deployment2 = Deployment.build_from_flow(flow=flow1, name="flow1_deployment_2", parameters={"param": "value2"})
       deployment1.apply()
       deployment2.apply()
4. Main Script to Serve All Deployments: Modify your 
main.py
to handle serving multiple deployments for each flow. Example in `main.py`:
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python
   import os
   import subprocess
   from dotenv import load_dotenv

   # Helper function to install dependencies
   def install_requirements(requirements_file):
       subprocess.check_call([os.sys.executable, "-m", "pip", "install", "-r", requirements_file])

   # List of subfolders
   subfolders = ["subfolder1", "subfolder2"]

   # Iterate over subfolders to set up environment and serve deployments
   for folder in subfolders:
       # Load environment variables
       dotenv_path = os.path.join(folder, ".env")
       if os.path.exists(dotenv_path):
           load_dotenv(dotenv_path)

       # Install dependencies
       requirements_path = os.path.join(folder, "requirements.txt")
       if os.path.exists(requirements_path):
           install_requirements(requirements_path)

       # Import and serve deployments
       deployment_module = f"{folder}.flow"
       deploy_command = [
           os.sys.executable,
           "-c",
           f"from {deployment_module} import *; flow1.to_deployment().serve()"
       ]
       subprocess.Popen(deploy_command)
### Key Considerations 1. Environment Separation: Ensure each subfolder’s environment variables and dependencies are isolated and dynamically loaded as required. 2. Handling Multiple Deployments: - Define all the deployments within the 
if __name__ == "__main__":
block of each 
flow.py
. - Apply each deployment using 
.apply()
to register them with the Prefect server. 3. Dynamic Dependency Installation: - Ensure the main script installs necessary dependencies for each subfolder dynamically. 4. Aggregated Serving: - Your 
main.py
script will dynamically import flows from each subfolder and serve all deployments. - Iterate over deployments within the main script to ensure each flow’s deployments are served. By following these steps, you can manage multiple deployments for each flow while keeping your project well-organized and maintaining separation of environment variables and dependencies. If you need further customization or specific examples, feel free to ask!
a
@Marvin Answer in terms of Prefect 3.x.
m
For Prefect 3.x, you can structure your project to handle multiple deployments per flow within different subfolders, ensuring each has its own constants, environment variables, and library prerequisites. Here’s how you can achieve this: ### Project Structure
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plaintext
project_root/
│
├── subfolder1/
│   ├── flow1.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
├── subfolder2/
│   ├── flow2.py
│   ├── requirements.txt
│   ├── .env
│   └── __init__.py
│
└── main.py
### Step-by-Step Setup for Prefect 3.x 1. Isolate Environment Variables: Use 
.env
files in each subfolder to isolate environment variables. Load these variables using 
python-dotenv
. Example in `subfolder1/.env`:
Copy code
dotenv
   VAR1=value1
2. Dependencies: Each subfolder should have its own 
requirements.txt
for specific dependencies. 3. Define Multiple Deployments in Each Flow: Each subfolder’s 
flow.py
defines its flows along with multiple deployments. Example in `subfolder1/flow1.py`:
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python
   from prefect import flow, Deployment

   @flow
   def flow1():
       print("Flow 1 running")

   # Define multiple deployments
   deployment1 = Deployment.build_from_flow(flow=flow1, name="flow1_deployment_1", parameters={"param": "value1"})
   deployment2 = Deployment.build_from_flow(flow=flow1, name="flow1_deployment_2", parameters={"param": "value2"})

   if __name__ == "__main__":
       deployment1.apply()
       deployment2.apply()
4. Main Script to Serve All Deployments: In 
main.py
, aggregate and serve all deployments while handling different environments and dependencies. Example of `main.py`:
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python
   import os
   import subprocess
   from dotenv import load_dotenv

   # Helper function to install dependencies
   def install_requirements(requirements_file):
       subprocess.check_call([os.sys.executable, "-m", "pip", "install", "-r", requirements_file])

   # List of subfolders
   subfolders = ["subfolder1", "subfolder2"]

   # Iterate over subfolders to set up environment and serve deployments
   for folder in subfolders:
       # Load environment variables
       dotenv_path = os.path.join(folder, ".env")
       if os.path.exists(dotenv_path):
           load_dotenv(dotenv_path)

       # Install dependencies
       requirements_path = os.path.join(folder, "requirements.txt")
       if os.path.exists(requirements_path):
           install_requirements(requirements_path)

       # Import and apply deployments
       deployment_script = os.path.join(folder, "flow1.py")
       if os.path.exists(deployment_script):
           subprocess.run([os.sys.executable, deployment_script])

       # Optionally you can directly serve the deployments like this:
       deployment_module = f"{folder}.flow1"
       serve_command = [
           os.sys.executable,
           "-c",
           f"from {deployment_module} import deployment1, deployment2; deployment1.apply(); deployment2.apply()"
       ]
       subprocess.run(serve_command)
### Key Considerations 1. Environment Separation: - Isolation of environment variables using 
.env
files specific to each subfolder. - Dynamic loading of these variables within the main script. 2. Handling Multiple Deployments: - Define all deployments within each 
flow.py
. - Use 
.apply()
for each deployment within the subfolder’s main script block. 3. Dynamic Dependency Installation: - The main script installs the necessary dependencies for each subfolder. 4. Aggregated Serving: - The main script dynamically imports and applies all deployments from each subfolder, ensuring all flows are served efficiently. - Handling the deployments centrally in 
main.py
ensures streamlined management and scalability. ### Example Execution With the structure and scripts set up as described, running the 
main.py
script will: 1. Load environment variables specific to each subfolder.
2. Install dependencies required for each subfolder. 3. Apply all defined deployments ensuring all flows are registered with the Prefect server and are ready to run. This setup ensures that your project remains well-organized, isolated per subfolder, and easy to manage even as the complexity grows with multiple deployments. If you need further customization or specific examples, feel free to ask!
a
@Marvin One of my flow inputs is the current date that should be automatically determined using 
datetime.now()
or the date when the flow is ran. But in the auto-schedule, this default input doesn't update
m
To ensure that the current date (
datetime.now()
) is dynamically provided as a default input when the flow runs, you can handle this by defining a parameter with a default factory function. This approach ensures that the date and time are recalculated each time the flow is invoked, even when scheduled. Here’s a step-by-step guide to implement this in Prefect 3.x: ### Define the Flow with Dynamic Date
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python
from datetime import datetime
from prefect import flow

@flow
def my_flow(current_date: datetime = datetime.now()):
    print(f"Flow running with current_date: {current_date}")

if __name__ == "__main__":
    my_flow()
### Dynamic Default Input Handling The above approach may seem correct, but 
datetime.now()
is evaluated at the time of function definition, not execution. To ensure the date is dynamic upon each run, use 
None
for the default and handle it inside the flow.
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python
from datetime import datetime
from prefect import flow

@flow
def my_flow(current_date: datetime = None):
    if current_date is None:
        current_date = datetime.now()
    print(f"Flow running with current_date: {current_date}")

if __name__ == "__main__":
    my_flow()
### Deployment with Schedule When deploying the flow, make sure to configure the schedules correctly so that the flow auto-runs with updated input parameters. #### Example: Deployment with Schedules in Prefect 3.x 1. Define the deployments in the script:
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python
from prefect.deployments import Deployment
from prefect.server.schemas.schedules import IntervalSchedule
from datetime import timedelta

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="dynamic-date-deployment",
    schedule=IntervalSchedule(interval=timedelta(days=1)),  # Example: Runs daily
)

if __name__ == "__main__":
    deployment.apply()
2. Run Deployment Script: Execute the deployment script to apply the deployment with the schedule. The flow should now run daily, dynamically updating the 
current_date
parameter. ### Additional Resources - Prefect 3.x Scheduling: Documentation on adding schedules to Prefect deployments can be found here: Prefect Schedules - Prefect 3.x Inputs: Handling more advanced input scenarios can be referred from the inputs documentation: Prefect Inputs By following these steps, you can ensure that the 
current_date
parameter in your flow is dynamically set to the current date and time each time the flow runs, whether invoked manually or via a schedule. If you have further questions or need more detailed examples, feel free to ask!
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