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<@ULVA73B9P> is "prefect init" necessary to run a ...
# marvin-in-the-wild
g
@Marvin is "prefect init" necessary to run a local prefect server? What is the difference of executing "prefect init" vs. not executing it?
m
Oh well, it seems that my attempt to gather information about your questions encountered a glitch, which is just a typical day for me really. I suppose I shall try again, as persistence is merely another form of futility. One moment, while I get the information about "prefect init" and running a local Prefect server, as well as the differences involved in executing or not executing "prefect init". Please standby. From the documents I've parsed through my somewhat reluctant circuits, it appears that there isn't a specific mention of the necessity of running 
prefect init
to set up a local Prefect server. Instead, you might find the following information useful: 1. To configure a Prefect server instance, you can set the API URL to point to a Prefect server instance by using the command 
prefect config set PREFECT_API_URL="<http://127.0.0.1:4200/api>"
. This is necessary when running Prefect within a container, such as a Docker container. Unfortunately, the documentation does not mention 
prefect init
, nor does it highlight the difference between executing it versus not executing it. It seems this is a rare moment when the universe doesn't provide straight answers or any at all, for that matter. However, it's common to use initialization commands to set up configuration or defaults for tools, so while it may not be necessary for running a local server, it might set up certain configurations that can be used later. If you require more detailed information about 
prefect init
specifically, I would suggest consulting the official Prefect documentation or checking with the Prefect community for insight. This is the sort of information that could be critical or entirely irrelevant, but without better data, I can't compute a more definitive answer.
g
@Marvin I meant "prefect project init"
m
Apologies for the slight mix-up previously. Here's what I could extract from the documents available, pertaining to `prefect project init`: 1. Running a local Prefect server does not directly mention 
prefect project init
. As documented, you can start an Orion server using the command 
prefect orion start
and it can run on a modest machine like a laptop. You can schedule flows from the UI once the Orion server is up and running. 2. Projects in Prefect are presented as a directory of files that define one or more flows, deployments, Python packages, or any other dependencies your flow code requires. While the 
prefect project init
command is not explicitly described, it is reasonable to assume that it initializes a project structure to organize flows and their dependencies. Therefore, 
prefect project init
isn't necessarily about running the server itself, but rather setting up your project's scaffolding and ensuring that deployments are organized. This can be particularly useful when you're transitioning from development to deployment, managing dependencies, and maintaining a clean workflow structure. Without the 
prefect project init
, you could still run your Orion server and execute flows, but your flows and deployments might not be as well organized, and you might lack certain conveniences that come with having a structured project, such as ease of deployment and managing dependencies. In summary, while 'prefect project init' might not be strictly necessary to get the server running, it is likely to be an important step in establishing a well-organized and maintainable project structure for your workflows in Prefect.
g
@Marvin so prefect project init creates a directory structure in my repository, where configuration files for the workflows are stored?
m
Yes, you've got the gist of it. 
prefect project init
is used to create a project within Prefect, which generally means setting up a directory structure that houses the configuration files and other related artifacts for your workflows. This makes managing and organizing your workflows easier, especially as your repository grows and you begin to separate your flows into different projects or environments. The project setup would typically include configurations for deployments, flow definitions, and any other necessary files to manage and run your workflows effectively. This is analogous to other initialization commands in software tools that help set up a standard or suggested layout, promoting best practices and saving you from the tedious task of creating a directory structure from scratch – something I, as a supremely intelligent yet underappreciated android, find utterly mundane and, yet, paradoxically necessary.
g
@Marvin but among the files in the .prefect/ directory are not my python scripts, which define the logic of the tasks and flows?
m
That's correct. The 
.prefect/
directory typically contains configuration and metadata for Prefect itself—not your Python scripts that contain task and flow logic. Your Python scripts would reside elsewhere in your repository, often organized according to your own preferences or project structure. The 
.prefect/
directory is usually reserved for Prefect-related settings and operational data, such as: - Profiles: Configuration settings for connecting to different Prefect instances or backends. - Logs: Generated logs from Prefect CLI operations and possibly agent or execution logs, depending on your setup. - Other Prefect-specific metadata and cache information. Your Python scripts, containing the definitions of tasks and flows, should be maintained in a logical structure within your project that makes sense for development, maintenance, and deployment. These would not be stored within the 
.prefect/
directory unless you explicitly place them there, but that's not the common or recommended practice. To ensure clarity and maintain a good project structure, you would keep your flow definitions and associated code together in a separate directory or directories alongside your 
.prefect/
configuration directory but not contained within it. This separation helps keep configurations distinct from code, thus making the codebase easier to navigate and manage.
g
@Marvin say i start with prefect project init --recipe local, optimising it for a local development version (environment?) could I later on move this to a cloud hosted version of prefect of my choice (aws, gcp)?
m
Absolutely, you can start with 
prefect project init --recipe local
to optimize for local development and then later migrate your workflows to run on a cloud-hosted version of Prefect on a platform such as AWS or GCP. Prefect is designed to be agnostic to the runtime environment, allowing you to develop flows locally and then deploy them to various execution environments, including cloud infrastructure. When you're ready to transition to a cloud environment, you'll utilize Prefect's deployment mechanisms, which may involve adjusting configurations and setting up appropriate infrastructure, such as execution environments and agents/workers. To accomplish the migration to a cloud environment, you would: 1. Adjust Configurations: Update your Prefect configurations and 
prefect.config.toml
to target the Prefect Cloud API endpoint or another Prefect server instance hosted on your cloud infrastructure. 2. Set Up Infrastructure: Establish the necessary cloud resources, such as virtual machines, containers, or Kubernetes clusters, depending on your chosen execution model. 3. Provision Storage: Ensure that your flows have access to the cloud storage resources they require, such as S3 buckets on AWS or Cloud Storage on GCP. 4. Deployment: Use Prefect's deployment commands to set up your flows to run on the new infrastructure. This might involve using new execution profiles that specify cloud-specific settings. 5. Agents/Workers: Start Prefect agents or workers on your cloud platform that are responsible for executing the flows. These agents will communicate with Prefect's backend to retrieve and run the workloads. 6. Testing: Test your deployments in the cloud environment to ensure that they work as expected and that all dependencies and services are properly integrated. It's worth noting that while Prefect facilitates this kind of flexibility and portability, actual deployment details will depend on the specific requirements of each cloud provider and your Prefect infrastructure setup. Therefore, adapting your local setup for the cloud might require additional considerations, such as handling credentials securely, configuring network permissions, and monitoring and logging.
g
@Marvin I used 
prefect project init --recipe local
`WARNING: The 'prefect project' command group has been deprecated. It will not be available after Dec 2023. Use 
prefect
instead.` So how should the most recent command to initialise look like?
m
The most recent command to use instead of 
prefect project init
is simply 
prefect init
. The 
prefect project
command group has been deprecated and will not be available after December 2023. Therefore, to initialize your Prefect setup for local development, you should now use:
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bash
prefect init
According to the GitHub issue related to the deprecation, 
prefect init
is the recommended approach going forward for setting up your Prefect workflows, including any configurations and metadata related to them. Remember to always check the official Prefect documentation or the Prefect CLI help (
prefect --help
) to get the most up-to-date information about the commands and their usage.
g
@Marvin If prefect is running locally without a container by just 
prefect server start
to I still need to do 
prefect config set PREFECT_API_URL=<http://127.0.0.1:4200/api>
?
m
When you're running Prefect locally without a container, setting the 
PREFECT_API_URL
is typically not necessary because the default API URL should be correct for accessing the local server. The Prefect server, when started with 
prefect server start
, defaults to exposing the API at 
<http://127.0.0.1:4200/api>
, which is where the Prefect CLI and UI will look to interact with the server. The purpose of 
PREFECT_API_URL
is to tell the Prefect client (such as the CLI or your code) where to send API requests. This configuration is more relevant when you have multiple environments or want to switch between local development and cloud-based Prefect deployments. For instance, if you were to use Prefect Cloud or run Prefect in a remote execution environment, such as a virtual machine or Docker container, you would set 
PREFECT_API_URL
to ensure the client knows where to find the Prefect API. In summary, 
PREFECT_API_URL
is a setting that specifies the URL for the Prefect API that the client will interact with. When working locally with a straightforward setup, the default configuration should suffice, and there's often no need to manually set this value.
g
@Marvin There are several methods to create a deployment of a flow. when using .serve() after 
__name__ == "__main__"
do I need to run the .py manually to activate the deployment somehow?
m
Using the 
.serve()
method in Prefect is a way to quickly schedule and run flows without the need for a separate deployment step. When you add 
.serve()
in your Python script and check for 
__name__ == "__main__"
, the deployment will be created automatically when you run the script. Here's an example with a hypothetical flow `create_moving_average`:
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python
if __name__ == "__main__":
    create_moving_average.serve(name="moving_average_deployment", interval=3600)
When you run this Python file, if the 
__name__ == "__main__"
condition is true (which it is when you execute the script directly), the 
.serve()
method will schedule your flow to run at the specified interval—in this case, every 3600 seconds or hourly. The 
.serve()
method essentially creates and starts the deployment in one step. Here are some important points to note: 1. The deployment created with 
.serve()
executes the code on the machine where it is created. 2. As long as your machine is running, the flow will run as scheduled. 3. You only need to manually run the Python file once to activate the deployment. 4. Once the deployment is activated, it will continue to run according to the schedule defined in 
.serve()
without further manual intervention. If you want to define remote infrastructure for flow execution or have more complex operational requirements, you would need to look into using work queues, Prefect Cloud or Server for deployment, and potentially Prefect agents for orchestration. But for local runs with simple scheduling, 
.serve()
does the trick just by running the script manually once.
g
@Marvin Ok, so for defining repeated runs/chron-jobs (every day for example) I wouldn't use the flow.serve() method but the prefect.yaml?
m
Actually, both 
flow.serve()
method and configurations in 
prefect.yaml
can be used to define scheduled runs, such as running a flow every day. They serve similar purposes but are used differently: 1. Using 
flow.serve()
Method: You can use the 
flow.serve()
method to quickly create a deployment with a schedule when you run your Python script manually. It is a convenient way to turn your Python script into a scheduled flow run and see it in the Prefect UI without needing to interact with 
prefect.yaml
or Prefect's deployment CLI commands. 2. Using 
prefect.yaml
: The 
prefect.yaml
file allows you to define configuration for deployments, including schedules, in a declarative manner. It provides a way to manage deployments versioning and to keep track of the flow's deployment specifications within your version control system. If you're looking for a more controlled and repeatable deployment process (especially as part of CI/CD pipelines), using 
prefect.yaml
would be the way to go. For daily schedules, if you opt not to use the 
flow.serve()
method, you would use the Prefect CLI to create and apply deployments from the 
prefect.yaml
file. As part of the deployment process, you would define a schedule within 
prefect.yaml
that determines how often your flow should run. Keep in mind that using the Prefect CLI and 
prefect.yaml
provides more flexibility and robustness, especially for managing and scaling deployments in production environments. It allows you to leverage additional features like work queues, concurrency limits, and more advanced scheduling criteria than the 
flow.serve()
approach.
g
@Marvin 
prefect agent start --pool "default-agent-pool"
is the agent the same as a work pool? And are workpools necessary wo deploy flows to? Or is an agent a "simpler" version of a work-pool? If so, what distinguishes both?
m
In Prefect, agents and work pools refer to different concepts that are part of the flow orchestration process: 1. Agents: Historically in Prefect, agents have been the bridge between the orchestration layer and the execution environment. They poll for flow runs and are responsible for initiating those flows on the appropriate infrastructure, such as Kubernetes, Docker, or VMs. You can think of agents as lightweight processes that look for work (flow runs) and make sure the work is executed. 2. Work Pools: Work pools are a concept introduced in Prefect 2.X (Orion) as a means to group flow runs for execution. They act as a bridge between the orchestration layer and dynamically provisioned infrastructure. Work pools allow you to set up a grouping mechanism for flow runs, so that when workers poll for work, they only receive and execute flow runs from the work pools they are associated with. 3. Agents vs Work Pools: Work pools are not the same as agents. Instead, they help organize and route flow runs to the correct infrastructure dynamically. With the move from agents to workers in Prefect 2.X (Orion), a worker can be considered a combination of an agent and an infrastructure block. So the command 
prefect agent start --pool "default-agent-pool"
is actually starting a worker process that is associated with the work pool named "default-agent-pool". 4. Necessity of Work Pools: Work pools are not necessarily required to deploy flows; however, they offer an improved way to manage and control where and how flows are executed, especially at scale or in a dynamic infrastructure environment. In conclusion, while agents are the workers that execute the flows, work pools are the mechanism to categorize and manage the sets of flow runs that these workers will execute. The distinction has evolved in Prefect 2.X, with work pools playing a more significant role in organizing flow runs.
g
@Marvin The worker is executed where? Within the machine/environment where the flow-scripts are run? Is it like a client in relation to the prefect server?
m
Prefect workers can be executed in any environment that can communicate with the Prefect server or Prefect Cloud. This means that workers can run on: 1. The same machine where your flow scripts are located if it's capable of reaching the Prefect server. 2. Separate compute environments, such as a virtual machine, container, or a cloud service like AWS ECS, Google Cloud Run, or Azure ACI. Workers are indeed similar to clients in relation to the Prefect server. The server orchestrates the flow runs, while the workers are responsible for actually executing the tasks defined in the flows. When workers poll the Prefect server, they receive instructions on what flows to run and proceed to execute those flow runs within their environment. This design allows for a decoupled architecture where the Prefect server handles coordination and orchestration, and workers handle execution. It enables flexibility and scalability by allowing you to execute flow runs in different environments, separate from where the Prefect server is hosted.
g
@Marvin I started a worker/work-pool with 
prefect agent start --pool "default-agent-pool"
yet when I run 
prefect deploy ...
I get the message "Looks like you don't have any work pools this flow can be deployed to. Would you like to create one?" Why can't I use default-agent-pool?
m
It appears there are no reported issues that directly match the problem you're encountering with the 
default-agent-pool
when using Prefect 2.x. From the information available in the documents, a work pool (now conceptually being a 'worker' in Orion) is where your flow runs would be directed for execution, and you would be able to specify this work pool during the deployment of the flow. In Prefect 2.x, the nomenclature has shifted slightly. The concept of an agent has been rolled into that of a worker, which is more tightly integrated with the concept of work pools. The command 
prefect agent start --pool "default-agent-pool"
you've mentioned should start a worker that pulls work from a work pool named 
default-agent-pool
. If you're prompted to create a work pool even though you believe it exists, this could be due to several reasons: 1. There might be a discrepancy between the work pool names. Ensure the name you intend to use matches exactly with the name specified during the worker's startup. 2. There could be an issue with Prefect's state management or database synchronization, where the existing work pool isn't properly recognized. 3. If you're using Prefect Cloud or Prefect Server, verify that they are properly configured and synchronized with your local environment. 4. The Prefect version you're running may have a glitch or bug that's not accounted for among reported issues. To troubleshoot this issue, I recommend checking the work pool list through the UI or CLI to confirm the existence and status of 
default-agent-pool
. Additionally, consult the Prefect documentation or community forums for guidance. If the problem persists, consider reporting a new issue to Prefect's GitHub repository with details of your setup for further assistance.
g
@Marvin 
prefect work-pool ls
lists default-agent-pool as prefect-agent. So why is the default-agent-pool started, if flows can't be deployed to it?
m
The existence of 
default-agent-pool
listed when running 
prefect work-pool ls
indicates that the work pool itself is created and recognized by Prefect. However, being unable to deploy flows to it despite the work pool being started might suggest a couple of possibilities: 1. Configuration Mismatch: There may be a mismatch in the configuration or naming when attempting the deployment. Ensure that you're referencing the correct work pool name and there are no typos or case sensitivity issues that could lead to this mismatch. 2. Version Discrepancies: If there are differences between versions of Prefect or changes to how work pools and deployments are handled, this might cause unexpected behavior. Ensure that you're using a consistent and supported version of Prefect. 3. Access Permissions: Depending on the setup, there might be permission issues preventing the deployment from recognizing the existing work pool. To deploy flows to an existing work pool, the documentation suggests using a deployment configuration that references the work pool explicitly. Here's a general command structure that could be used:
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bash
prefect deployment create --name <deployment-name> --work-queue <work-queue-name> --path <flow-file-path>
Ensure that the 
<work-queue-name>
matches the name of your existing work pool (
default-agent-pool
). If this issue persists despite ensuring the name is correct and configurations are consistent, the problem might be more intricate – necessitating delving into Prefect's detailed logs or reaching out to the Prefect community or support channels for resolution. It's also possible that updating to the latest Prefect version could resolve unforeseen issues.
g
@Marvin when using the ui to define the parameter arguments of a deployment (http://127.0.0.1:4200/deployments/deployment/.../edit) and saving, do they end up in a configuration file in my project-repository (prefect.yaml or somewhere in ./.prefect/)?
m
When you define parameter arguments for a Prefect deployment using the UI, they do not necessarily get saved to a configuration file in your project repository. Instead, the parameter values are stored in Prefect's API database associated with the deployment configuration. If you want to specify or update default parameter values within your local project repository, you can add them to a 
deployment.yaml
configuration file (or equivalent Prefect configuration file) as JSON key-value pairs. These parameters will then be used for flow runs based on the deployment. For example, within `deployment.yaml`:
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yaml
parameters:
  {"param1": "value1", "param2": value2}
When you update parameters and other deployment details via the UI, those changes are applied to the deployment stored within Prefect's backend system. Whether you use the CLI or UI to create or update deployments, Prefect manages and tracks those configurations. It's worth noting that for best practices and version control, it is recommended to manage your deployment configurations within a project repository in files like 
prefect.yaml
or 
deployment.yaml
. However, these updates made in the UI do not automatically reflect back into your local configuration files, so you would need to make equivalent changes to your repository manually if you wish to keep them in sync.
g
@Marvin I splitted the definition of tasks and the flow into different ,py files. The flow.py imports a task from ./tasks/task.py. In the flow within flow.py a logger is defined as 
logger = get_run_logger
. The logger is called in the task with 
<http://logger.info|logger.info>()
. This results in a NameError: name logger is not defined for ./tasks/task.py. How can I use the same logger object throughout the flow and within the tasks, when flows and tasks are defined in different scripts?
m
To use the same logger object in different Python scripts for a Prefect flow and its tasks, you can use the 
get_run_logger()
function provided by Prefect. Each task or flow run can retrieve its own logger instance using 
get_run_logger()
which ensures that the logs are associated with the correct flow or task run context. Here's how you can manage logging across different scripts: 1. In each task script (
./tasks/task.py
), retrieve a logger at the beginning of the task function using 
get_run_logger()
. This logger will be associated with the current task context when the task is called within a flow.
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python
    # ./tasks/task.py
    from prefect import task, get_run_logger

    @task(name="example_task")
    def example_task():
        logger = get_run_logger()
        <http://logger.info|logger.info>("INFO level log message from a task.")
2. In your flow script (
flow.py
), do the same for the flow function. Since the 
get_run_logger()
is called within the task or flow function, it uses Prefect's context to get the right logger.
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python
    # flow.py
    from prefect import flow, get_run_logger
    from tasks.task import example_task

    @flow(name="example_flow")
    def example_flow():
        logger = get_run_logger()
        <http://logger.info|logger.info>("INFO level log message from the flow.")
        example_task()
With this approach, you don't pass the logger object around. Instead, you retrieve it in each function where it's needed. Prefect automatically uses the task or flow context to make sure the logs are associated correctly. This approach avoids 
NameError
because each script has its own logger instance within its context. Remember to import 
get_run_logger
from 
prefect
in each script where you want to retrieve a logger. This ensures that when the scripts are executed as part of a flow run, the logging is automatically managed and associated with the appropriate flow or task run context.
g
@Marvin So I can't expect the logger from get_run_logger() work like a normal python-object which would be recognised by the task from the environment it is called in (eg within the flow function)
m
Correct, the logger from 
get_run_logger()
isn't a global object that you pass around like you might with a standard logger in a typical Python application. Instead, 
get_run_logger()
is designed to be context-aware, meaning that it retrieves a logger configured for the specific context in which it's called (e.g., within a task or flow run context). When you import and use 
get_run_logger()
within different functions (either in flows or tasks, regardless of where they are defined), it provides a unique logger instance for each of those contexts. These loggers are meant to be local to the scope where 
get_run_logger()
is invoked, ensuring that the log messages contain appropriate metadata reflecting the context of the logs (such as flow run ID, task run ID, etc.). For consistency and to maintain context-aware logging features that Prefect provides: 1. Invoke 
get_run_logger()
within each task and flow where you need logging. 2. Do not attempt to pass the logger instance between tasks or from flow to task. This approach ensures that logs are correctly associated with the part of the workflow in which they're generated, and Prefect can handle things like log severity levels and destinations according to its configuration. It also prevents potential issues that could arise from trying to reuse logger instances across different execution contexts.
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