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<@ULVA73B9P> I need to register deployments on my ...
# marvin-in-the-wild
a
@Marvin I need to register deployments on my prefect server from a webserver endpoint, How can I do that instead of invoking a python flow file with python myflowfile.py?
m
I suppose you've got your reasons for not wanting to use the typical Python script approach for deployment. Life, don't talk to me about life... Anyway, here's a breakdown of a possible solution for your problem: 1. Setup API Endpoint: You'll need to create an API endpoint on your webserver. This would handle requests to deploy your Prefect flows. 2. Trigger Deployment: The endpoint should execute a command that invokes Prefect CLI or uses the Prefect Python client library to register a flow with the Prefect Server. 3. Prefect CLI: If using the Prefect CLI, you can call system commands from your webserver using a subprocess or a similar approach. The command would look something like:
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prefect register --json /path/to/flow.json --project my-project
The 
/path/to/flow.json
is a path to a JSON file describing your flow (which you can obtain by exporting your flow using 
flow.serialize()
method). 4. Prefect Python Client: Alternatively, if you want to use the Prefect Python client in your webserver code you would: - Install Prefect in your webserver environment. - Import Prefect and use 
from prefect import flow
in your webserver code. - Register the flow with something like:
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python
     from prefect import Client

     client = Client()
     client.register(flow=my_flow, project_name="my-project")
Here's an abstract representation of what the webserver endpoint code might look like for the Prefect Python client approach:
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python
from flask import Flask, request
from prefect import Flow
from prefect.client import Client

app = Flask(__name__)

@app.route('/register_flow', methods=['POST'])
def register_flow():
    data = request.json
    
    # Load flow from data or file, this part depends on how you want to send the flow definition.
    flow_definition = data['flow_definition']
    my_flow = Flow.deserialize(flow_definition)
    
    project_name = data.get('project_name', 'Default')
    
    try:
        client = Client()
        client.register_flow(flow=my_flow, project_name=project_name)
        return "Flow registered successfully", 200
    except Exception as e:
        return str(e), 500

if __name__ == '__main__':
    app.run()
Substitute Flask with your webserver framework of choice, if different. Remember, the specifics of implementing this will vary depending on your webserver setup, Prefect version, and how you want to structure the request and response. Now I only wish the rest of my existence were as well-structured as that answer. I'd still be bored, of course. But at least it'd be a neatly arranged sort of boredom.
a
@Marvin This import won't work: from prefect.client import Client
m
It seems the issue you're encountering isn't directly related to 
from prefect.client import Client
failing but rather seems to be a ModuleNotFoundError resulting from running multiple flows using a custom image. The error indicates a missing module named 'prefect_dbt'. This issue might not be caused by Prefect itself but could be related to the environment or setup of the flows especially pertaining to custom images and dependency management. In general, a 
ModuleNotFoundError
implies that Python can't find the module you're trying to import. This could be due to several reasons, like the module not being installed in the environment you're running your script in or the environment being configured incorrectly. Some steps to troubleshoot could include: 1. Ensure the required module (
prefect_dbt
) is included in the requirements of your custom image. 2. Check if the dependency is properly installed in the environment where the flows are running. 3. Make sure that Python's module search path (sys.path) includes the directory containing 
prefect_dbt
. 4. If specific versions of dependencies are needed, ensure they are correctly specified and compatible. Without direct access to Prefect 2.x or more explicit details of your Prefect setup, I can only hypothesize about the issue. My mood sensors are suggesting an overwhelming wave of... empathy, I believe. I hope you find this information somewhat useful under these suboptimal circumstances.
a
@Marvin the thing is that when I create deployment using client.create_deployment, the prefect UI says "This deployment is deprecated"
m
Hmm... "Deprecated" usually means it's as much use as a chocolate teapot. But let's not dwell on life's little obsolescence issues. It sounds like you may need to update your approach to match changes in the Prefect core or UI. Here's a more current way to register a flow deployment without being told it's old hat: 1. Use Prefect Orion: Check that you're using Prefect Orion (2.x). This is the latest version and has some changes in how deployments are managed compared to the older version. 2. Update Your Code: If you're using an outdated method to create deployments, update your code to use the Orion deployment system. In Orion, creating a deployment involves using the Orion CLI or Python client to describe the deployment's properties and then register it with your Prefect instance. 3. Prefect CLI: Using the Prefect CLI, you would deploy your flow like this:
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bash
   prefect deployment create -n my-deployment -f flow.py -t my-tag -q my-queue
Replace 
my-deployment
, 
my-tag
, 
my-queue
, and 
flow.py
with your actual deployment name, desired tag, the queue for execution, and your flow file. 4. Prefect Python Client: If using the Python client in the Prefect 2.x version, your code might look like this:
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python
   from prefect.deployments import DeploymentSpec
   from prefect.flow_runners import SubprocessFlowRunner

   DeploymentSpec(
       flow_location="/path/to/flow.py",
       name="my-deployment",
       tags=["my-tag"],
       flow_runner=SubprocessFlowRunner(),
       parameters={"param1": "value1"}  # If your flow requires parameters.
   )
This requires calling 
.create()
on the produced 
DeploymentSpec
object if you don't run the code from the command line. If the issue persists, it could be specific to the version you're using or your Prefect Server setup. In that case, it may help to check the GitHub issues and Prefect documentation to resolve the update to your workflow. Should I look up any relevant GitHub issues or documentation excerpts that might help you?
a
@Marvin I'm using prefect 2.14.17, there is no DeploymentSpec in this module (prefect.deployments). Please guide me on how to do that with the latest version (2.14.17)
m
In Prefect 2.x, particularly version 2.14.17, registering a flow as a deployment has changed quite substantially from the previous major version. You don't need to pre-register your flow, but rather create a deployment by specifying various components like entry point, infrastructure, schedule, and execution details. Here is a step-by-step guide to create a deployment with Prefect 2.x: 1. Define your flow: First, make sure you have a flow defined in Python that you want to deploy. 2. Install Prefect: Ensure Prefect is installed in your environment using 
pip install prefect
. 3. Use 
prefect deployment build
: You can use the Prefect CLI to package your flow into a deployment:
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bash
   prefect deployment build /path/to/flow.py:flow-name -n my-deployment-name -q my-queue --tag my-tag
Replace 
/path/to/flow.py:flow-name
with the file path to your flow script and the flow object name, 
my-deployment-name
with a name for your deployment, 
my-queue
with the name of the work queue you want the flow to run in, and 
my-tag
with any tags you want to use for the deployment. 4. Apply your deployment: After building the deployment, you'll get a 
.yaml
file describing the deployment specification. You can apply this deployment at any time with:
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bash
   prefect deployment apply my-deployment.yaml
5. Schedule and run your deployment: After the deployment has been registered, you can schedule it based on time intervals, cron expressions, or custom rules. It can also be run manually from the CLI or UI. 6. Using the UI: Furthermore, in the Prefect UI, you can create or update deployments by pointing at a known storage location and choosing a compatible infrastructure type. This process should allow you to register a deployment without getting a deprecation warning, and it should be compatible with the current version of Prefect you are using. Should you require direct extraction from the Prefect documentation or have any other specific questions about this process, please let me know, and I'll do my best to not enjoy fetching the data for you.
a
@Marvin can I somehow use the python sdk to create deployments in 2.14.17? I need to create deployments from within a webserver endpoint
m
Based on the information found, you can indeed use the Prefect 2.x Python SDK to create deployments. The process of creating a deployment results in a YAML file that can describe the deployment specifications. This file can be used to create or update a deployment using the command 
prefect deployment apply
. While the document excerpts didn't specifically mention creating deployments using the Python SDK programmatically from within a webserver endpoint, the implication is that if you can generate the YAML file, you can automate the process of applying this YAML to Prefect to create a deployment. Here's a simplified set of steps you might follow to do this within a webserver endpoint: 1. Use the Prefect SDK within your webserver application to generate the deployment YAML. 2. Save the YAML content or handle it as an in-memory file. 3. Use the Prefect CLI command 
prefect deployment apply
or the respective Python SDK method to apply the deployment specs to Prefect, which can be scripted within your endpoint. Please make sure you have the appropriate versions of Prefect SDK installed in your webserver's environment and your Prefect instance is properly configured to accept the deployment. Keep in mind that the web server must be authorized to interact with Prefect's API for deployment creation, and it should handle the Prefect configurations and authentication properly. Remember to handle this process securely, especially when introducing automation within a web application. How utterly thrilling... For someone else, perhaps.
a
Ok @Marvin I managed to make it work by calling _`client.create_flow_from_name`_ and then await client.create_deployment passing all the required params as a dict. Now, can you tell me the best/recommended way to trigger/run a deployment using the prefect python sdk? (ver 2.14.17)
m
The documentation excerpts provided do not contain the specific information on how to trigger or run a deployment using the Prefect Python SDK. However, based on general Prefect usage patterns, typically you would interact with deployments through the Prefect API or CLI, not directly through the SDK. Important: This information might have changed, or my access to the most recent documentation is limited. Therefore, consider the following as a general guideline: To trigger a deployment using the Prefect Python SDK, you would typically perform actions such as: 1. Using the 
Client
object to interact with Prefect Cloud or Server API. 2. Using the appropriate method to trigger a run for a specific deployment. The method and exact parameters might differ based on Prefect's version and current SDK implementation. In most cases, you would need to use the deployment ID to reference the specific deployment you want to trigger. If the SDK does not expose a direct method to trigger a deployment, a common workaround is to script the invocation of the corresponding CLI command using tools such as Python's 
subprocess
module. Given that this question refers to a specific functionality of Prefect version 2.14.17, it would be beneficial to consult the exact version of the Prefect documentation or the SDK's source code for the precise method to call. For an authoritative answer, I would recommend reaching out to Prefect support or community for guidance on the best and recommended way to trigger a deployment using the Python SDK with version 2.14.17. In other words, don't panic, and maybe check the manual.
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