Hey @Marvin I have a set of workflows that have configuration parameters that need to be initiated upon first usage. My preference would be that users modify the variables through the UI dashboard, but there will be multiple users running this on their local PCs and the first time they launch it they won't know the variable names or default values to use. Should I have a load script that sets up the variables and flows and deployments, but only if it is the very first run? What is the best way to detect this, just a

.init

file touch for which I could check existence in the

main.py

or a

bash

startup script?

To manage configuration parameters for your Prefect workflows and allow users to modify them through the UI dashboard, you can use Prefect Variables. Variables enable you to store and reuse non-sensitive data, such as configuration information. They can be created and modified via the Prefect UI, API, and CLI. For the detection of the first run, you might indeed consider a

.init

file or using a more robust method like checking if the required variables are set in Prefect's backend. However, there doesn't seem to be a built-in way in Prefect to detect a first run directly. Here is a suggestion for setting up your workflows: 1. Define default values for your variables within your

main.py

script. 2. Upon starting the script, check if these variables are already set in Prefect's backend. 3. If not, initialize them with the default values. Optionally, create an

.init

file to mark that initialization has been done for future checks. 4. Encourage users to modify these variables through the UI dashboard for further customization. Remember, Prefect Variables are mutable and intended for infrequent writes and frequent reads. For sensitive information, use Secret blocks instead. For more details, refer to Prefect documents on Variables and Profiles & Configuration. For a Prefect code example related to setting default parameters, you can reference this basic flow example from GitHub:

python
from prefect import flow

@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

    # Or serve it as a long-lived process
    # my_workflow.serve("my-deployment-name")

Link to GitHub Code Example The example above demonstrates a basic flow structure, but you will need to extend it to include functionality for setting up and checking configuration parameters as per your requirements.

@Marvin thanks for the suggestions. In addition to initialization, I will need to manage changes to deployments, variables, flow runs, etc. from different users and propagate that information to various deployments. Is there a preferred way to snapshot this data and propagate to another deployment across the network? I think the variables, current deployments and flows, etc. are all stored in a local sqlite DB stored on disk (not sure file paths, but would be good to know for linux, windows and mac) but perhaps that is what I should periodically capture and propagate?

Indeed, Prefect uses a local SQLite database by default, which is stored at

~/.prefect/prefect.db

. To manage changes across deployments and propagate them to various users, capturing the state of the database and data isn't the most efficient or recommended approach, especially since manipulating the database directly may lead to inconsistencies or synchronization issues. Instead, the preferred way to manage changes to deployments, variables, flow runs, etc., is through the use of Prefect's functionalities like CLI, API, or UI. Build a CI/CD pipeline that incorporates these changes and propagates them across your network by updating the relevant deployments, variables, and flows via automation tools. For synchronization across different deployments and machines, consider the following high-level approach: 1. Use a version control system (like Git) to maintain your flow definitions and configurations. 2. Set up a CI/CD pipeline that triggers on changes (using tools like Jenkins, GitLab CI/CD, GitHub Actions, etc.). 3. Automatically update deployments using Prefect commands or API calls within your CI/CD pipeline. When it comes to multi-environment propagation, such as staging or production environments, follow best practices for: - Making code available in different environments through development stages. - Handling independent deployment of distinct groupings of work (like in a monorepo). - Efficiently using build time to avoid repeated work. For more advanced scenarios where you need efficient data propagation strategies, consider setting up an automated workflow that uses Prefect’s API or CLI to fetch and apply changes. You can learn more about the CI/CD integration and accessing the local Prefect databases. I would advise against directly copying the SQLite database file as a means to synchronize state across deployments due to the potential for data conflicts and the lack of control over concurrent access to the database. Instead, use Prefect's intended tools and processes to ensure a more robust deployment and synchronization mechanism.

Okay @Marvin this is helpful. However, how should I handle the following use case: An engineer with domain expertise in a particular domain uses an existing running prefect, but because of their unique knowledge of a given domain, they are able to apply important variables such as mathematical constants for fluid flow. These are important and should be version controlled for the next time a deployment happens.

For the use case of an engineer with domain expertise applying important variables such as mathematical constants for fluid flow, version controlling these variables is critical. Prefect Variables allow you to store and reuse non-sensitive bits of data, including configuration information like mathematical constants. Although variables can be changed via the Prefect UI, API, and CLI, they are not inherently version-controlled. To version control domain-specific constants, you should: 1. Store these variables in a version-controlled repository (such as Git) inside configuration files or scripts that define these constants. 2. Apply changes to these variables by creating pull requests or commits so that there is an audit trail of changes, which ensures accountability and trackability. 3. Use automated CI/CD processes that trigger when changes are made. This can integrate with Prefect's tools to update flows or variables in Prefect with new values from the repository. 4. Optionally, use Prefect's parameterization capabilities within flows, so constants can be passed as parameters and are defined when the flow is run or deployed. This approach allows for: - Auditing: You have a record of changes and can trace them back to individual commits or pull requests. - Review: Changes can be reviewed by peers before being applied, ensuring quality control. - Automation: CI/CD can automatically propagate these constants across different deployments and environments when changes are merged. For more information about managing variables, refer to the Prefect Variables documentation. Remember that while Prefect can store variables, for the ability to version control and review changes by multiple domain experts, integrating Prefect with external version control systems and using CI/CD pipelines for automation is the best practice.

@Marvin Can you describe what you mean by "Prefect's parameterization capabilities" ? Shoot me a link to the documentation

Prefect's parameterization capabilities refer to the ability to pass arguments to flows which can be used to customize the flow's behavior on each run. Parameters in Prefect are similar to function arguments in regular Python code: they allow you to define certain variables at runtime. When you create flow runs, either through the Prefect UI, API, or CLI, you can pass in different parameter values. This enables a single flow to handle multiple use cases or scenarios, each perhaps requiring different configurations or input data. Here is an example of using parameters in Prefect from the documentation:

python
from prefect import flow
from pydantic import BaseModel

class Model(BaseModel):
    a: int
    b: float
    c: str

@flow
def model_validator(model: Model):
    print(model)

# Flow can be called with different 'Model' parameter values

In this example, the

model_validator

flow takes a single parameter

model

of type

Model

defined by a Pydantic model. When the flow is executed, parameter values are coerced to their appropriate Python representation based on type hints. For more detailed information and guidance on how to use parameters in your flows, you can visit the following links to the Prefect documentation: - Flows - Parameters documentation within the Flows section In the context of your use case, you can define constants as parameters with defaults and allow the domain expert engineer to override these parameter values when executing the flow. This way, the constants that are crucial for computations can be both version-controlled and parameterized for flexibility.

@Marvin Can you briefly explain Pydantic?

Certainly, Pydantic is a Python library that is used for data validation and settings management using Python type annotations. The library allows developers to define data models with types, and automatically handles data conversion, validation, and error reporting when data is parsed. Here's a brief rundown on Pydantic's core features: 1. Data validation: Pydantic ensures that data conforms to defined types and constraints, and will raise informative errors if invalid data is provided. 2. Type annotations: Your data models use standard Python type annotations to define the shape and type of data. 3. Settings management: You can use Pydantic models to manage environment variables and complex settings, benefiting from the same validation and error handling as regular data modeling. 4. Data conversion: Pydantic attempts to coerce incoming data to the defined types, making it easy to work with various data formats. 5. JSON support: Pydantic models can be easily exported to JSON, and JSON or other data formats can be parsed into Pydantic models. An example Pydantic model might look like this:

python
from pydantic import BaseModel

class UserModel(BaseModel):
    id: int
    name: str
    signup_ts: datetime = None
    friends: List[int] = []

# Data can then be parsed into the model, automatically validating and converting it:
user_data = {
    "id": "123",  # Automatically converted to an integer
    "name": "John Doe",
    "signup_ts": "2021-01-01 12:22",  # Automatically parsed to a datetime object
    "friends": [1, 2, "3"]  # List of integers, "3" is converted
}

user = UserModel(**user_data)

In this code,

user_data

dictionary keys and values are validated and coerced to the types specified in the

UserModel

. Pydantic will raise errors if any of the types or constraints are not met. For more detailed information, have a look at the official Pydantic documentation. It provides a comprehensive guide to all the features and usage patterns of the library.

Ahhh, I see, thanks