@Marvin I am running a deployment

gitlab_repo = GitRepository(
        url="<https://gitlab.com/mycompany/myrepo.git>",
        branch="mybranch",
        include_submodules=True
    )
    flow.from_source(
        source=gitlab_repo,
        entrypoint="myrepo/myfun.py:myfunc",
    ).deploy(
        name="mydeployment",
        work_pool_name="myworkpool",
    )

that works fine if i run my the prefect server locally. However if i have a prefect server running on another machine (same prefect version) that i know is accessible, i get this error.

prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url '<http://192.168.10.8:4200/api/flows/>'
Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'type': 'extra_forbidden', 'loc': ['body', 'labels'], 'msg': 'Extra inputs are not permitted', 'input': {}}], 'request_body': {'name': 'run-prefect', 'tags': [], 'labels': {}}}
For more information check: <https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/422>

It seems you're encountering a

422 Unprocessable Entity

error due to an issue with the data being sent to the Prefect server. The error message indicates that "Extra inputs are not permitted", specifically relating to the

labels

input. This might be happening if your deployment is inadvertently including unexpected or unsupported fields. Here's what you can do to address this issue: 1. Check Deployment Configuration: Ensure that the deployment code or configuration on the remote server does not have any extra fields or misconfigured parameters that your local setup might not have. Specifically, verify that the

labels

input in your configuration is correct or if it's required at all. 2. Prefect Version Differences: Double-check that the Prefect version and configuration on both your local machine and the server are truly identical. Any version mismatch could cause differing behaviors. 3. Formal Validation: Try eliminating the

labels

input or ensuring it matches the format expected by the Prefect server API. Here are two Prefect documentation pages that might be of some help based on different versions: - For Prefect 2.x: No direct match was found, but general integration guidance can be found here: Prefect 2.x Integrations. - For Prefect 3.x: Similarly, no exact match was found, but tweaking webhook methods might sometimes yield insights: Prefect 3.x Webhooks. Despite some differences, invisible Prefect levers should all pull in harmony, though being extra vigilant could be the panacea for your deployment success! If issues persist, comparing http request payload specifics (like labels) with what Prefect docs define might help. When in doubt, stripping things back to simplest configurations is often a debugging savior. Let me know if you need further assistance or additional resources!

@Marvin there is no mention of labels anywhere in my deployment code. Where can i check this on the server or elsewhere in my local configuration?

It looks like you might not directly see "labels" mentioned in your deployment code, but they could be originating from other parts of your configuration or infrastructure setup. Here are some areas where "labels" might come into play: 1. Prefect YAML Configuration: Labels can sometimes be set within your

prefect.yaml

configuration file. This is a common place where deployment settings, including labels, could be defined. You might want to check this file for any label configurations, especially if you're using shared or inherited configurations. Here's a reference for YAML configurations for Prefect 3.x: Prefect YAML Documentation. 2. Worker/Agent Configuration: In Prefect, workers or agents can have configurations that include labels. These might not be visible directly in your deployment script but could be part of how the worker/agent is set up. For more information on configuring workers, you can visit the Prefect 2.x guide here: Developing a Worker Type with Labels. 3. Environment Variables or Global Settings: You could also check for any environment variables or global settings that might affect your deployments, including any defaults or profile settings. Here’s a documentation reference for Prefect 3.x global settings that might hold some insights: Prefect Global Settings. As Prefect can sometimes exhibit its own kind of "invisible" logic, broad investigations in configuration corners usually help reveal unexpected settings like these. If you find any such settings, adjusting them or aligning them with the versions across different environments could resolve the issue.

@Marvin my deployment script is this (i removed some detail before as i thought it no relevant)

gitlab_repo = GitRepository(
        url="<https://gitlab.com/mycompany/myrepo.git>",
        branch="mybranch",
        #       credentials=GitLabCredentials.load("prefect-deploy-token"),
        include_submodules=True,
    )

    dotenv.load_dotenv("devtest.env")

    env_vars = {
        name: os.getenv(name)
        for name in [
            "DB_HOST",
            "DB_USER",
            "DB_NAME",
            "DB_SSL_MODE",
            "DB_PORT",
            "BLOB_STORAGE_ACCOUNT_URL",
            "BLOB_CONTAINER",
            "EXPORT_DIR",
            "POLLING_PERIOD_S",
            "DATA_BASE_DIR",
        ]
    }

    flow.from_source(
        source=gitlab_repo,
        entrypoint="myrepo/prefect_wrapper.py:run_prefect",
    ).deploy(
        name="myrepo",
        work_pool_name="mypool",
        job_variables={"env": env_vars},
    )

does that give you any more info?

Your deployment script does provide additional context. One potential area to investigate is the use of

job_variables

and the environment variables you are loading. Here's what you can consider: 1. Job Variables Configuration: When you're defining

job_variables

such as

"env": env_vars

, ensure that all the variables are correctly defined and expected by your deployment. Occasionally, presets on the server or the work pool might affect how these variables are interpreted. 2. Prefect YAML or Agent Configuration: Check if there is a

prefect.yaml

or agent configuration file that might be setting defaults for your deployments, including labels. Sometimes, environments may enforce their own label settings unless explicitly overridden. 3. Environment Context: Since you're using

dotenv

to load variables, confirm that all required variables are present and in the correct format. If any of them are linked to certain configurations on your Prefect server, it might be attempting to apply labels or settings automatically to match those variables. Depending on your Prefect version and server setup, these configurations could inadvertently contribute to additional settings like "labels". You might also want to examine any settings specifically linked to your work pool named

"mypool"

, as it may have its configurations or defaults. If you'd like further help pinpointing exact documentation related to these workflows, just let me know the Prefect version you're using! With that info, I can guide you to specific guides or resources.

@Marvin i found the problem. The server was running the same version of

prefect

, and the installed

pydantic

version was

2.10.3

Locally i had the same version of

prefect

by the

pydantic

version was

2.8.2

In this case, i downgraded the

pydantic

version on the server to

2.8.2

and now the deployment works.

Ah, the classic "version mismatch mystery"! I'm glad you were able to uncover the mystery behind the curtain and solve it; sometimes software can be as finicky as a cat deciding whether or not it wants to nap in the sunbeam. Pydantic's version differences might indeed lead to subtle changes in behavior, as you've observed with the Prefect deployment. If you encounter any more quirky snags or need further exploration into the Prefect cosmos, feel free to reach out. In the meantime, keep those versions aligned and happy deployment adventures!