@Marvin I get this error raise SignatureMismatchError.from_bad_params( prefect.exceptions.SignatureMismatchError: Function expects parameters ['bucket_name', 'ltp_scorer_backfill'] but was provided with parameters ['kwargs'] my deployment yaml looks like this

- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:tenure_predictor_flow
  parameters: {"kwargs": {"bucket_name": "dna-mlops-sagemaker-staging"}}
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules: []
  pull: *pull_staging

The error you are encountering,

SignatureMismatchError

, suggests that there's a mismatch between the parameters expected by your flow function and the parameters provided in your deployment YAML. Here’s a summary of the findings and a suggestion to resolve this: ### Key Points: 1. Mismatch Issue: The error indicates that your flow function expects specific parameters (

bucket_name

,

ltp_scorer_backfill

), but only

kwargs

was provided in the deployment configuration. 2. Prefect Documentation: Parameters should be explicitly passed to flows rather than encapsulated inside

kwargs

for deployment configurations. 3. GitHub Issues: Similar issues have been reported here and here. ### Resolution Steps: To resolve this, you should directly specify the parameters in your deployment YAML config. Here's a corrected version:

yaml
- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:tenure_predictor_flow
  parameters: {"bucket_name": "dna-mlops-sagemaker-staging", "ltp_scorer_backfill": "value_for_backfill"}
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules: []
  pull: *pull_staging

### Points of Interest: - Parameters Section: Replace

"kwargs": { "bucket_name": "dna-mlops-sagemaker-staging"}

with each parameter explicitly listed. - Flow Definition: Ensure your flow function signature matches the parameters provided in the deployment YAML. ### References: - GitHub Issue discussing

kwargs

pitfalls: GitHub Issue #6039 - More on flow parameter validation: GitHub Issue#14663 By specifying parameters directly in the deployment YAML, you align the configuration with the flow’s signature, resolving the

SignatureMismatchError

.

@Marvin That's great thank you Can you add a cron schedule too?

Sure, here's an updated deployment YAML with a cron schedule added to it:

yaml
- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:tenure_predictor_flow
  parameters: {"bucket_name": "dna-mlops-sagemaker-staging", "ltp_scorer_backfill": "value_for_backfill"}
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules:
    - cron: "0 12 * * *"
      timezone: "America/New_York"
      active: true
  pull: *pull_staging

### Explanation - Cron Schedule: This cron schedule will trigger the deployment at noon every day (

"0 12 * * *"

). - Timezone: The timezone is set to

"America/New_York"

. Adjust the timezone as needed. - Active: Setting

active: true

ensures the schedule is active. ### References - You can find more details on scheduling in Prefect's documentation here. Is there anything else you need help with?

@Marvin now I am getting raise SignatureMismatchError.from_bad_params( prefect.exceptions.SignatureMismatchError: Function expects parameters ['args', 'kwargs'] but was provided with parameters ['bucket_name', 'ltp_scorer_backfill']

The

SignatureMismatchError

indicates that your flow function expects

args

and

kwargs

, but specific parameters were provided in your deployment configuration. This happens when your flow is built to accept

**kwargs

, and you need to pass the parameters accordingly. ### Resolution Steps 1. Flow Function: Ensure that the flow function is designed to accept

**kwargs

. 2. Deployment Configuration: You must pass a JSON object under the

kwargs

key in your deployment YAML. ### Adjusted Flow and Deployment Configuration Flow Function Example:

python
from prefect import flow, get_run_logger

@flow
def tenure_predictor_flow(**kwargs):
    logger = get_run_logger()
    bucket_name = kwargs.get("bucket_name")
    ltp_scorer_backfill = kwargs.get("ltp_scorer_backfill")
    <http://logger.info|logger.info>(f"Bucket Name: {bucket_name}")
    <http://logger.info|logger.info>(f"LTP Scorer Backfill: {ltp_scorer_backfill}")

Updated Deployment Configuration:

yaml
- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:tenure_predictor_flow
  parameters: {"kwargs": {"bucket_name": "dna-mlops-sagemaker-staging", "ltp_scorer_backfill": "value_for_backfill"}}
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules:
    - cron: "0 12 * * *"
      timezone: "America/New_York"
      active: true
  pull: *pull_staging

### Points of Interest: - Flow Function:

tenure_predictor_flow

function is set to accept

**kwargs

, retrieving specific parameters using

kwargs.get()

. - Deployment YAML: Parameters are provided inside

kwargs

to match the method signature. ### References: - GitHub Issue with Workaround: GitHub Issue #6039 - Prefect Documentation on Parameters: Prefect Docs This should resolve the

SignatureMismatchError

and enable your flow to work correctly with the specified parameters.

@Marvin my flow code looks like this @flow(name="my flow") def my_flow( bucket_name="dna-mlops-sagemaker-development", ltp_scorer_backfill=False, ):

Given your flow function definition, it looks like your flow expects explicit parameters, not

**kwargs

. Therefore, you should adjust your deployment YAML to match the parameter names exactly as defined in your flow. Here’s the correctly updated deployment YAML:

yaml
- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:my_flow
  parameters: 
    bucket_name: "dna-mlops-sagemaker-staging"
    ltp_scorer_backfill: false
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules:
    - cron: "0 12 * * *"
      timezone: "America/New_York"
      active: true
  pull: *pull_staging

### Explanation: - Parameters: Directly list the flow parameters (

bucket_name

,

ltp_scorer_backfill

) in the

parameters

section of the deployment YAML. - Cron Schedule: A cron schedule is provided to run the flow daily at noon New York time. ### Conclusion: By aligning the deployment YAML parameters with your flow function signature, the

SignatureMismatchError

should be resolved. If you need more assistance, feel free to ask!

@Marvin So why do I get the signature mismatch

Given that your flow function is defined with explicit parameters, the

SignatureMismatchError

indicates that there might still be a mismatch between the parameters expected by the flow and those provided in the deployment. It's also possible that the flow's signature is being misinterpreted. ### Checklist: 1. Check Flow Signature: Ensure the flow definition matches precisely. 2. Verify Deployment YAML: Ensure the parameters in the YAML match the function signature exactly. 3. Inspect Flow Loading: Ensure the entrypoint is correctly leading to the proper flow function. ### Flow Function: Here's your flow function for reference:

python
from prefect import flow

@flow(name="my flow")
def my_flow(bucket_name="dna-mlops-sagemaker-development", ltp_scorer_backfill=False):
    # Your flow logic here
    pass

### Corrected Deployment YAML:

yaml
- name: staging
  version:
  tags: []
  description:
  entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:my_flow
  parameters: 
    bucket_name: "dna-mlops-sagemaker-staging"
    ltp_scorer_backfill: false
  work_pool:
    name: dna-ecs-workpool
    work_queue_name:
    job_variables:
      env:
        INFISICAL_ENVIRONMENT: staging
  schedules:
    - cron: "0 12 * * *"
      timezone: "America/New_York"
      active: true
  pull: *pull_staging

### Debugging Steps: 1. Flow Definition:

python
   from prefect import flow, get_run_logger

   @flow(name="my flow")
   def my_flow(bucket_name="dna-mlops-sagemaker-development", ltp_scorer_backfill=False):
       logger = get_run_logger()
       <http://logger.info|logger.info>(f"Bucket Name: {bucket_name}")
       <http://logger.info|logger.info>(f"LTP Scorer Backfill: {ltp_scorer_backfill}")
       # Additional flow logic

2. Full YAML Inspection: Ensure the YAML isn't splitting parameters improperly.

yaml
   - name: staging
     version:
     tags: []
     description:
     entrypoint: dna/orchestration/prefect/flows/tenure_predictor.py:my_flow
     parameters: 
       bucket_name: "dna-mlops-sagemaker-staging"
       ltp_scorer_backfill: false
     work_pool:
       name: dna-ecs-workpool
       work_queue_name:
       job_variables:
         env:
           INFISICAL_ENVIRONMENT: staging
     schedules:
       - cron: "0 12 * * *"
         timezone: "America/New_York"
         active: true
     pull: *pull_staging

### Common Pitfalls: - Parameter Names: Ensure no typos in parameter names in both flow function and deployment YAML. - YAML Syntax: Ensure correct indentation and structure. ### Reference Links: - Prefect Docs on Parameters: Prefect Docs - Related GitHub Issues: GitHub Issue #14663 This should help resolve the mismatch if everything is correctly aligned. Please check and let me know if you encounter further issues.