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<@ULVA73B9P> I want to deploy prefect <3.x in> my ...
# ask-marvin
a
@Marvin I want to deploy prefect 3.x in my k8s cluster. I already deployed the server and the worker. I have a service inside this cluster with an endpoint that has the flow code and the deploy code. So everytime this endpoint is called the endpoint tell the server to do the deploy, as here:
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deployment = flow_module.generate_report_flow_test.deploy(
    name=deployment_name,
    parameters=parameters,
    schedule=CronSchedule(cron=cron_expression),
)
What I don't get is, since the flow is always the same I expect prefect to create a Docker image of the flow the first time my endpoint is created, and the second time it won't need to create the image again. So where this image can be stored inside my k8s cluster? What are the options if I have no internet connection from my cluster?
m
In Prefect 3.x, when deploying a flow that requires a Docker image, you have options for storing and managing these images, especially in a Kubernetes (k8s) environment. Since your cluster doesn't have internet access, here's how you can handle this: 1. Local Docker Registry: You can set up an internal Docker registry within your k8s cluster to store Docker images. This allows you to build and push images locally without needing external access. You can then configure your deployment to use this registry by referencing the image directly in your work pool configuration. 2. Custom Docker Images: Prefect 3.x allows you to create custom Docker images outside of Prefect. You can build the image with your flow code and dependencies, then push it to your internal registry. Prefect's deployment can reference this image directly without needing to build it again. 3. Avoiding Internet Dependency: By not pushing the image to a remote registry, you can keep everything local. This means configuring your deployment to use the local registry and ensuring that your k8s nodes can pull images from it. 4. Flow Storage Options: Prefect supports storing flow code with any fsspec-supported storage, which can be local or cloud-based. In your case, using a local storage option would be preferable. For more detailed guidance on running flows on Kubernetes, you can refer to Prefect's Kubernetes deployment documentation. In summary, setting up a local Docker registry and using custom images will be your best bet to handle deployments without internet access. If you need further details on setting up a local registry or configuring Prefect deployments, feel free to ask.
a
How can a flow not require a docker image? Could I just let it be a module in my code and prefect will handle the rest of the logic? @Marvin
m
In Prefect 3.x, you can deploy flows without requiring a Docker image. Here are some options to achieve that: 1. Direct Module Execution: You can deploy your flow as a Python module. Prefect can execute the flow directly from your codebase without packaging it into a Docker image. This is suitable if your execution environment (like a Kubernetes pod) already has the necessary dependencies installed. 2. Setting 
build=False
: When deploying, you can set 
build=False
in the 
.deploy
method to avoid building a Docker image. This assumes that the execution environment already has access to the necessary code and dependencies. 3. Prefect YAML Configuration: Use a 
prefect.yaml
file to define your deployment configuration. This file can specify how Prefect should handle your flow without Docker, including environment variables and Python dependencies. You can customize the deployment behavior to fit your infrastructure. For more detailed guidance, you might find this section on Prefect's deployment options helpful. By leveraging these options, you can avoid Docker images and let Prefect handle the execution using your existing code structure.
a
@Marvin Yes my service pod already has prefect dependencies. The flow just need to call back another endpoint of my project that will handle the backend work. I deployed the worker helm install prefect-worker prefecthq/prefect-worker --namespace demo -f values.yaml Now the pod is running with PREFECT_API_URL already in its env variable, but I don't get if I need to change the code. the helper I created for scheduling the flow on prefect is
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def create_prefect_schedule(scheduling_resource: DynamicReportScheduleResource):
    """
    Creates or updates a Prefect deployment for the dynamic report schedule.
    """
    cron_expression = scheduling_resource.cron_expression

    # Parameters for the flow; these will be passed when the flow runs.
    parameters = {
        "report_id": scheduling_resource.report_id,
        "recipients": scheduling_resource.recipients,
    }

    # Build a unique deployment name.
    deployment_name = f"{scheduling_resource.report_id}_{scheduling_resource.scheduling_id}"

    # Build the deployment from the flow and attach the schedule
    deployment = flow_module.generate_report_flow_test.deploy(
        name=deployment_name,
        parameters=parameters,
        schedule=CronSchedule(cron=cron_expression),
    )
    return deployment.id
And the file with the flow and task code, called flow_module.py, has this code in it:
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@task
def call_test_endpoint(recipients: list[str], base_url: str, headers: dict) -> None:
    """
    Calls the internal endpoint to generate the dynamic report.
    """
    url = f"{base_url}/print-recipients"
    parameters = {
        "recipients": recipients,
    }
    response = requests.post(url, headers=headers, parameters=parameters)
    if response.status_code != 200:
        pass
    return response.content


@flow
def generate_report_flow_test(report_id: str, recipients: list[str]):
    """
    Flow to test prefect
    """
    base_url = "<http://localhost:8080/multiwell-be/dynamic-report-scheduling-test>"
    headers = {
        "user_external_id": "andrea",
    }
    # Send the report via email.
    call_test_endpoint(recipients, base_url, headers)
m
Your message was too long by 268 tokens...
Your message was too long by 191 tokens...
Your message was too long by 56 tokens...
It seems like there's a small misunderstanding with the import path for 
CronSchedule
. In Prefect 3.x, the correct way to handle scheduling is slightly different. Here's what you may need to adjust: 1. Cron Schedule: Instead of using 
CronSchedule
, you can specify a cron schedule directly in your deployment configuration. Prefect 3.x uses 
croniter
for defining cron-like schedules. You can specify this in your deployment script or YAML configuration. 2. Deployment Code: Ensure that the deployment code is using the correct Prefect 3.x constructs. The 
.deploy()
method should be correctly configured to handle scheduling. Here's a revised version of your deployment function, assuming you want to use Prefect's scheduling directly:
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python
from prefect import flow, task
from prefect.deployments import Deployment
from prefect.server.schemas.schedules import CronSchedule

def create_prefect_schedule(scheduling_resource):
    cron_expression = scheduling_resource.cron_expression

    parameters = {
        "report_id": scheduling_resource.report_id,
        "recipients": scheduling_resource.recipients,
    }

    deployment_name = f"{scheduling_resource.report_id}_{scheduling_resource.scheduling_id}"

    deployment = Deployment.build_from_flow(
        flow=flow_module.generate_report_flow_test,
        name=deployment_name,
        parameters=parameters,
        schedule=CronSchedule(cron=cron_expression)
    )
    deployment.apply()
    return deployment.id
3. Flow Module: Your flow and task definitions look fine, but ensure that the Prefect worker is correctly set up to execute these flows. Your worker should be able to connect to the Prefect API using 
PREFECT_API_URL
. For more details on scheduling and deployment in Prefect 3.x, you can refer to Prefect's scheduling documentation. If you have further questions or need additional clarification, feel free to ask!
a
@Marvin Ok so when the deploy is activated the worker fecth the code from this service? and how??
m
In Prefect 3.x, when a deployment is activated, the worker fetches the flow code based on the storage configuration specified during deployment. Here's how this process typically works: 1. Work Pool and Worker Interaction: Prefect workers are responsible for polling work pools for new flow runs. When a deployment is scheduled, the worker retrieves the run details from the work pool. Each worker type corresponds to a specific execution environment, such as Kubernetes or Docker. 2. Flow Code Storage: The flow code isn't stored on the Prefect server or Prefect Cloud. Instead, you specify a storage location for your flow code. Common storage options include: - Git-based Storage: Your flow code can be stored in a Git repository (e.g., GitHub, GitLab). - Docker Image: If using Docker, the flow code can be baked into a Docker image. - Cloud Storage: You can use cloud storage solutions like AWS S3, Azure Blob Storage, or GCP GCS. - Local Storage: For local deployments, the code can reside on the local file system. 3. Fetching Code: Depending on the storage option chosen, the worker fetches the flow code. For instance, if using Git-based storage, the worker will clone the repository to access the code. If using a Docker image, the worker will pull the image containing the code. For more information on storing and retrieving flow code, you can refer to the Prefect documentation on storing flow code. By ensuring your deployment specifies the correct storage configuration, the worker can seamlessly fetch and execute the flow code. If you have further questions about setting up specific storage options, feel free to ask!
a
@Marvin How can I say to prefect fecth the flow from the internal code of my project in this case? from the flow_module.py
m
To deploy a flow from your internal project code, such as from 
flow_module.py
, you can use the 
flow.from_source()
method in Prefect 3.x. This allows you to specify the source of your flow code directly from your local file system. Here's how you can set it up: 1. Specify the Source: Use the 
flow.from_source()
method to indicate that your flow code is located in a Python module or file within your project. 2. Deployment Code: Here's an example of how you might configure your deployment to use the local module:
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python
from prefect import flow, task
from prefect.deployments import Deployment

# Assuming flow_module.py is in the same directory or accessible in the PYTHONPATH
from flow_module import generate_report_flow_test

def create_prefect_schedule(scheduling_resource):
    cron_expression = scheduling_resource.cron_expression

    parameters = {
        "report_id": scheduling_resource.report_id,
        "recipients": scheduling_resource.recipients,
    }

    deployment_name = f"{scheduling_resource.report_id}_{scheduling_resource.scheduling_id}"

    deployment = Deployment.build_from_flow(
        flow=generate_report_flow_test,
        name=deployment_name,
        parameters=parameters,
        schedule=CronSchedule(cron=cron_expression),
        # Specify the source as the local module
        source="flow_module:generate_report_flow_test"
    )
    deployment.apply()
    return deployment.id
3. Ensure Accessibility: Make sure that 
flow_module.py
is accessible to the worker. This means it should be part of the worker's file system, either through shared storage, mounted volumes, or being included in the worker's image. By using 
flow.from_source()
, you instruct Prefect to use your existing Python module directly, which is ideal for local or internal deployments where the code is already present in the execution environment. Adjust the 
source
parameter to match the location and structure of your project. For more detailed information, refer to the Prefect documentation on storing flow code.
a
@Marvin ok so a localstorage can be used when dealing with a pod in the k8s cluster and the prefect worker will be able to fetch the code from the project
m
Yes, that's correct. In a Kubernetes (k8s) environment, you can use local storage to manage your flow code, and the Prefect worker can access this code as long as it's part of the pod's file system. Here's how you can ensure this setup works: 1. Local Storage in Kubernetes: Ensure that your flow code (e.g., 
flow_module.py
) is accessible within the Kubernetes pod. This can be achieved by: - Including the code in the Docker image that the pod uses. - Mounting a persistent volume or a config map containing the code. - Using a shared volume if multiple pods need access to the same code. 2. Prefect Worker Setup: The Prefect worker running in your Kubernetes cluster will execute the flow by accessing the local code. Make sure the worker's environment is configured to include the necessary Python modules and dependencies. 3. Deployment Configuration: When you create the deployment, specify the source of the flow code using the 
flow.from_source()
method, pointing to the local module or file. This tells Prefect to execute the flow using the code available within the pod. By ensuring that the flow code is part of the pod's environment, the Prefect worker can seamlessly execute the flows without needing to fetch code from an external source. This setup is particularly useful when operating in environments with limited or no internet connectivity.
a
@Marvin if I put schedule inside tghe deploy on prefect 3.x I get this error Traceback (most recent call last): File "local_hello_world.py", line 9, in module flow_module.my_flow.deploy( File "venv\lib\site-packages\prefect\utilities\asyncutils.py", line 353, in coroutine_wrapper return run_coro_as_sync(ctx_call()) File "venv\lib\site-packages\prefect\utilities\asyncutils.py", line 209, in run_coro_as_sync return call.result() File "venv\lib\site-packages\prefect\_internal\concurrency\calls.py", line 330, in result return self.future.result(timeout=timeout) File "venv\lib\site-packages\prefect\_internal\concurrency\calls.py", line 193, in result return self.__get_result() File ".pyenv\pyenv-win\versions\3.9.13\lib\concurrent\futures\_base.py", line 391, in __get_result raise self._exception File "venv\lib\site-packages\prefect\_internal\concurrency\calls.py", line 403, in _run_async result = await coro File "C:\Users\andre_v0nt\Desktop\projects\prefect\venv\lib\site-packages\prefect\utilities\asyncutils.py", line 190, in coroutine_wrapper return await task File "C:\Users\andre_v0nt\Desktop\projects\prefect\venv\lib\site-packages\prefect\utilities\asyncutils.py", line 343, in ctx_call result = await async_fn(*args, **kwargs) TypeError: deploy() got an unexpected keyword argument 'schedule'
m
Your message was too long by 24 tokens...
In Prefect 3.x, the 
deploy()
method does not accept a 
schedule
argument directly. Instead, you should define your schedule in a 
prefect.yaml
file or use the interactive 
prefect deploy
command to set up your deployment with a schedule. Here's how you can handle scheduling in your deployment: 1. Using 
prefect.yaml
: Create a 
prefect.yaml
file for your deployment configuration. This file can include a 
schedules
section where you define the schedule for your deployment. 2. Interactive Deployment: Use the 
prefect deploy
command in the terminal. This command will guide you through setting up your deployment, including the schedule. 3. Example YAML Configuration:
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yaml
   version: 1
   name: my-deployment
   flow: path.to.your.flow:generate_report_flow_test
   schedule:
     cron: "0 0 * * *"  # Example cron expression for daily runs
     timezone: "America/Chicago"
4. Apply the Deployment: Once your 
prefect.yaml
is configured, apply it with:
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bash
   prefect deploy --prefect-file path/to/your/prefect.yaml
For more information on setting up schedules, refer to the Prefect scheduling documentation. By using a 
prefect.yaml
file or the interactive deployment setup, you can configure your flow's schedule without encountering the error from directly passing a 
schedule
argument to 
deploy()
.
a
@Marvin Ok but since I need to schedule the flow what I need to do? Must I create a deployment with a schedule or a schedule directly?
m
In Prefect 3.x, to schedule a flow, you typically create a deployment that includes the schedule. This ensures that your flow runs according to the specified schedule. Here's how you can set this up: 1. Create a Deployment with a Schedule: The deployment itself should include the schedule configuration. This is the recommended approach because it ties the flow logic, parameters, and schedule together in a single deployment entity. 2. Using 
prefect.yaml
: Define your deployment and schedule in a 
prefect.yaml
file. This file serves as a configuration for both the deployment and the schedule. 3. Example Configuration:
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yaml
   version: 1
   name: my-deployment
   flow: path.to.your.flow:generate_report_flow_test
   parameters:
     report_id: your_report_id
     recipients: your_recipients_list
   schedule:
     cron: "0 0 * * *"  # Example cron expression for daily runs
     timezone: "America/Chicago"
4. Apply the Deployment: Use the Prefect CLI to apply the deployment configuration, which includes the schedule:
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bash
   prefect deploy --prefect-file path/to/your/prefect.yaml
By creating a deployment with an embedded schedule, you ensure that the flow is executed according to the specified timing. This approach centralizes the flow's configuration and makes it easier to manage and modify in the future. If you have further questions or need specific examples, feel free to ask!
a
@Marvin I mean with these many difficulties I will use prefect 2.x
m
I understand your frustration. Transitioning between versions can sometimes be challenging, especially with changes in configuration and API usage. Prefect 2.x might offer a more familiar setup if you've used it before, and it might align better with your current workflow needs. If you decide to use Prefect 2.x, here's a quick reminder of the main differences: 1. Agents and Storage: Prefect 2.x uses agents and storage blocks, which might be more straightforward if you're already familiar with them. 2. Deployment: Prefect 2.x might have a different deployment process, often involving more explicit handling of storage and execution environments. 3. Scheduling: You can directly attach schedules to deployments in Prefect 2.x, which might align better with your existing setup. If you need any specific help with Prefect 2.x or have further questions about transitioning, feel free to ask. I'm here to help make the process as smooth as possible!
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