Adam
05/29/2024, 12:16 PMdeployments:
- name: my-pipe
description: description here
entrypoint: folder/code.py:func_name
parameters: { "boolean_value": True }
work_pool:
name: serverless-push-work-pool
I tried your suggestion:
yaml
deployments:
- name: my-pipe
description: description here
entrypoint: folder/code.py:func_name
parameters:
boolean_value: true
work_pool:
name: serverless-push-work-pool
but i get error : pydantic.errors.ConfigError: unable to infer type for attributeMarvin
05/29/2024, 12:16 PMyaml
deployments:
- name: my-pipe
description: description here
entrypoint: folder/code.py:func_name
parameters:
boolean_value: true
work_pool:
name: serverless-push-work-pool
The error pydantic.errors.ConfigError: unable to infer type for attribute
suggests that there might be another issue in the YAML structure or how it's being parsed.
Here are a few things to double-check:
1. Ensure that the indentation in the YAML file is correct.
2. Verify that no additional, incorrect fields or parameters are present.
3. Validate that the YAML file is formatted correctly. YAML is sensitive to spaces, so use a linter or validator.
Here is a minimal working example:
yaml
deployments:
- name: my-pipe
description: description here
entrypoint: folder/code.py:func_name
parameters:
boolean_value: true
work_pool:
name: serverless-push-work-pool
Make sure you apply the deployment again with prefect deployment apply
after making changes to the YAML file.
For further assistance, you can also refer to the Prefect documentation on Deployments. If the issue persists, feel free to reach out to the Prefect community for more tailored support.Nate
05/29/2024, 4:23 PMMake sure you apply the deployment again withwhich is 🙅 thatd beafter making changes to the YAML file.prefect deployment apply
prefect deploy
if you have the whole stack trace id be interested to see itAdam
05/30/2024, 7:20 AMNate
05/30/2024, 5:25 PM