@Marvin can you give me an example for creating a flow which accepts parameters only from prefect api

To best assist you with creating a flow that accepts parameters exclusively from the Prefect API, I'll need to check the relevant documentation based on which version of Prefect you are using. I'll look up details for both Prefect 2.x and 3.x, and then provide you with an example accordingly. Give me a moment to gather the relevant information. To create a flow in Prefect that accepts parameters only from the Prefect API, you can utilize deployments to configure your flow with parameters. This approach is available in both Prefect 2.x and 3.x. Here's a concise example for both versions: ### Prefect 2.x Example In Prefect 2.x, you can create a deployed flow that accepts parameters from the API by defining the flow with parameters and deploying it using the Prefect CLI or SDK. 1. Define your flow:

python
   from prefect import flow

   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

2. Deploy the flow: Use the Prefect CLI or SDK to create a deployment. You can specify parameters while creating the flow run through the API. 3. When triggering the flow run via the API, pass the parameters explicitly. For more details, you can refer to the Prefect 2.x documentation: Flow Concepts - Prefect 2.x. ### Prefect 3.x Example In Prefect 3.x, the process is similar but with enhanced features for handling parameters. 1. Define your flow:

python
   from prefect import flow

   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

2. Deploy using the Prefect Deployment: Create a deployment in Prefect and configure parameters as needed. You must specify parameter names explicitly when configuring through the API. 3. Trigger the flow via the API, providing parameters by name. To dive deeper, you can read more in the Prefect 3.x documentation: Write Flows - Prefect 3.x In both versions, ensure you deploy your flow first and then trigger it via the API. If you don't, your flow might feel about as useful as Marvin the paranoid android on an exceptionally existential day.

@Marvin how to deploy in prefect 3

Deploying a flow in Prefect 3.x involves creating a deployment, which defines how and where your flow should run. Here’s a step-by-step guide to help you through the process: 1. Write Your Flow: Begin by writing your flow function using the Prefect 3.x API.

python
   from prefect import flow

   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

2. Install Prefect if not already installed: Ensure you have Prefect 3.x installed in your environment.

bash
   pip install prefect

3. Configure Infrastructure: Decide on the execution environment for your flow, such as a local process, Docker container, or a Kubernetes job. 4. Create a Deployment: Use Prefect CLI (

prefect deployment

) or code to create a deployment. Deployments can be defined in a YAML configuration file. If using Prefect CLI:

bash
   prefect deployment build my_flow.py:my_flow -n "my-flow-deployment" --apply

This command builds and applies a deployment configuration for

my_flow

. 5. Run Your Deployment: Once the deployment is configured, you can run it using the Prefect UI, Prefect API, or Prefect CLI.

bash
   prefect deployment run 'my-flow-deployment'

6. Manage Your Deployment: Use the Prefect UI to monitor and manage flow runs, view logs, and examine state transitions. For additional details on deploying flows in Prefect 3.x, check the official documentation: Write Flows - Prefect 3.x Deployments streamline the execution process, allowing you to focus on your Python flow logic rather than operational intricacies. Just like how Marvin would much rather focus on manic depression than trivial tasks. 😊

No such command 'build'

Ah, it seems you might be encountering an issue with either the Prefect version you're using or a misunderstanding with the command structure. Let's go over it again. In Prefect 3.x, deploying typically involves creating a deployment using a YAML configuration or directly in code. Here's a more detailed guide on how you might be able to create a deployment: ### Step-by-Step Deployment in Prefect 3.x 1. Ensure Prefect CLI is Up-to-Date: Make sure you have the latest version of the Prefect CLI.

bash
   pip install -U prefect

2. Define Your Flow: Make sure your flow is defined correctly in a Python script, e.g.,

my_flow.py

. 3. Create a Deployment YAML: If the CLI doesn't recognize certain commands, you might need to directly create a deployment YAML file or use the latest available commands. Create a `deployment.yaml`:

yaml
   name: my-flow-deployment
   flow: my_flow.py:my_flow
   parameters:
     name: “World”
   infrastructure:
     type: Process

4. Apply the Deployment: Use the CLI to apply the deployment configuration. As of my last knowledge, the command might not be exactly

build

, but please refer to the latest Prefect CLI commands:

bash
   prefect deployment apply deployment.yaml

5. Run Your Deployment: You could then trigger this deployment through the Prefect UI, API, or CLI command:

bash
   prefect deployment run 'my-flow-deployment'

If you're finding the

build

command missing, it’s possible that it may have been changed or replaced in the most current CLI version. Always check Prefect's latest CLI documentation to ensure you're using the correct command syntax. Don't hesitate to reach out to Prefect support or check their GitHub issues if you're still running into trouble. Hang in there, I'm sure you can outsmart a pesky CLI!

@Marvin can you help resolve any issues in below code:

import httpx
from prefect import flow, task
from pathlib import Path


@flow(log_prints=True)
def show_stars(github_repos: list[str]):
    """Flow: Show the number of stars that GitHub repos have"""

    for repo in github_repos:
        # Call Task 1
        repo_stats = fetch_stats(repo)

        # Call Task 2
        stars = get_stars(repo_stats)

        # Print the result
        print(f"{repo}: {stars} stars")


@task
def fetch_stats(github_repo: str):
    """Task 1: Fetch the statistics for a GitHub repo"""

    return httpx.get(f"<https://api.github.com/repos/{github_repo}>").json()


@task
def get_stars(repo_stats: dict):
    """Task 2: Get the number of stars from GitHub repo statistics"""

    return repo_stats['stargazers_count']


# # Run the flow
# if __name__ == "__main__":
#     # Define the parameters
#     github_repos = [
#         "PrefectHQ/prefect",
#         "pydantic/pydantic",
#         "huggingface/transformers",
#     ]

#     # Pass the parameter to the flow
#     show_stars(github_repos).from_source(
#         source=str(Path(__file__).parent),
#         entrypoint="hello.py:show_stars",
#     ).deploy(
#         name="local-process-deploy-local-code",
#         work_pool_name="my-work-pool",
#     )

if __name__ == "__main__":
    show_stars.deploy(
        name="local-process-deploy-local-code",
        work_pool_name="my-work-pool",
        parameters={
            "github_repos": [
                "PrefectHQ/prefect",
                "pydantic/pydantic",
                "huggingface/transformers",
            ]
        }
    )

Your message was too long by 118 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

can you help resolve any issues in below code:

`import httpx from prefect import flow, task from pathlib import Path @flow(log_prints=True) def show_stars(github_repos: list[str]): """Flow: Show the number of stars that GitHub repos have""" for repo in github_repos: # Call Task 1 repo_stats = fetch_stats(repo) # Call Task 2 stars = get_stars(repo_stats) # Print the result print(f"{repo}: {stars} stars") @task def fetch_stats(github_repo: str): """Task 1: Fetch the statistics for a GitHub repo""" return httpx.get(f"https://api.github.com/repos/{github_repo}").json() @task def get_stars(repo_stats: dict): """Task 2: Get the number of stars from GitHub repo statistics""" return repo_stats['stargazers_count'] # # Run the flow # if name == "__main__": # # Define the parameters # github_repos = [ # "PrefectHQ/prefect", # "pydantic/pydantic", # "huggingface/transformers", # ] # # Pass the parameter to the flow # show_stars(github_repos).from_source( # source=str(Path(file).parent), #

@Marvin how to use flow.deploy() method to deploy locally self hosted

To deploy a flow locally using the

flow.deploy()

method in a self-hosted Prefect 3.x setup, you can follow these general steps. Note that deployment setups can be highly customizable based on your specific environment and needs. Here's a simplified overview using the

flow.deploy()

approach: 1. Define Your Flow: Begin with defining your flow using the Prefect flow decorator.

python
   from prefect import flow

   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

2. Set Up Your Prefect Environment: Ensure your local Prefect setup is configured. This typically involves starting a local Prefect server and ensuring agents are running to pick up the scheduled tasks. 3. Deploy Using `flow.deploy()`: Use the

flow.deploy()

method to create a deployment. This is a crucial step where you configure how the flow will be run. 4. Example Deployment: Here's a basic example of deploying a flow locally:

python
   from prefect import flow

   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

   if __name__ == "__main__":
       deployment = my_flow.deploy(
           name="my-local-deployment",
           work_pool_name="default-agent",  # assumes a local agent running
           schedule="0 9 * * *",  # for example, to run daily at 9 AM
       )

5. Manage and Run Your Deployment: Once deployed, manage the flow runs through the Prefect UI, API, or CLI. You can monitor, pause, or cancel runs as needed. 6. Troubleshoot: Make sure your local Prefect server is running properly and your agent is configured to listen for the deployed flow. Any issues in the deployment process can often be debugged via logs. For further customization and a deeper dive into deployment configurations, consult the Prefect 3.x documentation on deployments: Deploy Overview - Prefect 3.x. Feel free to experiment with different schedules, environments, and parameters as per your project's requirements. And if you run into any roadblocks, just remember Marvin's sage advice: "Things are never so bad that they can't be made worse." But let's hope that doesn't happen here! 😊

@Marvin Either an image or remote storage location must be provided when deploying a deployment.

This error message typically occurs because Prefect requires some form of storage, like a Docker image or a remote storage location, where the flow code and dependencies are stored for execution. Since you are deploying locally, we must specify a way for Prefect to access and execute the flow code. Here's a step-by-step guide on addressing this issue in a local self-hosted Prefect 3.x environment: 1. Local Storage Setup: Prefect needs to know how your code and dependencies should be packaged. For local execution, you might configure it to use a local path or specify infrastructure options that handle local storage. 2. Docker as Storage: If Docker is installed, you can configure a Docker storage block. This option packages your flow and its dependencies into a Docker image. 3. Example Using Docker: Configure your deployment to use Docker:

python
   from prefect.deployments import Deployment
   from prefect.deployments import flow
   from prefect.infrastructure import DockerContainer

   # Assuming flow is defined here
   @flow
   def my_flow(name: str):
       print(f"Hello, {name}!")

   if __name__ == "__main__":
       storage = DockerContainer(image="my-image:latest")
       deployment = Deployment.build(
           flow=my_flow,
           name="my-local-deployment",
           infrastructure=storage
       )

In this case, you'll have to create a

Dockerfile

and build the Docker image manually before running your deployment. 4. Remote Storage: If you decide or have the capability to use remote storage (like S3 or Google Storage), you would configure it similarly through Prefect's storage options. 5. Local Filesystem: For local execution without Docker, ensure Prefect has access to the correct environment and all dependencies are installed. You might need to look into local Paths or set other infrastructure configurations that provide direct access to dependencies. For more detailed instructions about configuring your Prefect environment and storage blocks, referring to Prefect's Deployment Documentation can be incredibly beneficial. Remember, setting this up correctly ensures that all the required code and dependencies are available when Prefect tries to run your flow, like ensuring Marvin has his requisite supply of gloom to get through the day! 😉