<@ULVA73B9P> I get this error raise SignatureMism...
# ask-marvin
a
@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
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- 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
m
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:
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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
.
a
@Marvin That's great thank you Can you add a cron schedule too?
m
Sure, here's an updated deployment YAML with a cron schedule added to it:
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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?
a
@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']
m
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:
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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:
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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.
a
@Marvin my flow code looks like this @flow(name="my flow") def my_flow( bucket_name="dna-mlops-sagemaker-development", ltp_scorer_backfill=False, ):
m
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:
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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!
a
@Marvin So why do I get the signature mismatch
m
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:
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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:
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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:
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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.
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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.