But in complex software development projects, like building a Python library, an API, or software development kit, often you will be working with multiple files, multiple packages, and dependencies. This works fine for simple Python scripting projects. This is a common approach for a lot of beginners and many people transitioning from working with Python for data analytics. py file or notebook, and run your Python program in the terminal. make it easier to manage dependencies for users who are e.g.When developing software with Python, a basic approach is to install Python on your machine, install all your required libraries via the terminal, write all your code in a single. for Conda to start a flow run in a specific conda virtual environment allow an optional command (or a list of commands) to the `Process` block that can be executed before an agent starts the flow run in order to e.g. We currently recommend adjusting the command in the YAML file but some users who are beginners still find it difficult to set up: We see more and more users asking for similar functionality: I searched the Prefect documentation for this feature.ĭuring Orion beta, we had a subprocess flow runner that allowed activating a virtual environment before starting a flow run. I used t … he GitHub search to find a similar request and didn't find it. I added a descriptive title to this issue. Process_block.save("default", overwrite=True) Here is how you can create a Process infra block that will run the flow in a conda environment named dataplatform: from prefect.infrastructure import Process Note that this deployment replaces the default command (shown below) with a command that runs the same in a conda environment: command:Īdjust the infrastructure Block from Code or from the UI From code Path: /Users/anna/repos/prefect-deployment-patterns/_create_deploymentĮntrypoint: flows/parametrized.py:parametrized # The work queue that will handle this deployment's runs # A complete description of a Prefect Deployment for flow 'parametrized' Here, we assume a virtual environment is named dataplatform - adjust it to the name of your Conda environment: # User: str = "Marvin", question: str = "Ultimate", answer: Any = 42 ("The answer to the %s question is %s! □", question, parametrized( ("Hello from Prefect, %s! □", user_name) You can modify the command that is used for the flow run entrypoint to use Python in the virtual environment as follows: Example flow from prefect import task, flowįrom typing import say_hi(user_name: str, question: str, answer: Any) -> None: Adjust the YAML fileĪnother option is to adjust the YAML deployment file before applying the deployment. In that case, you don’t need to do anything else on your infrastructure block or deployment - the default Process block will already work out of the box. Run the agent directly in the virtual environment from which you want to execute the flow runsĪssuming that your agent is already running in the virtual environment that has all required dependencies. There are several ways how you can accomplish that.
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