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From Guillermo Rodríguez Cano (JIRA) <j...@apache.org>
Subject [jira] [Updated] (AIRFLOW-1774) Better handling of templated parameters in Google ML batch prediction/training operators
Date Wed, 01 Nov 2017 13:54:00 GMT

     [ https://issues.apache.org/jira/browse/AIRFLOW-1774?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Guillermo Rodríguez Cano updated AIRFLOW-1774:
----------------------------------------------
    Description: 
The {{MLEngineBatchPredictionOperator}} does not support well the templated parameter {{job_id}}
due to a helper function used to detect and cleanup bad job names inhibiting the templating
engine to work. I suspect the same may happen with {{MLEngineTrainingOperator}} as well.

Google ML requieres a unique job id, therefore it is critical to have the possibility to customise
the job's name easily, and preferably with some data related to the DAG, for example, appending
the day to the job's name via a macro like {{ds}}

  was:
The {{MLEngineBatchPredictionOperator}} does not support well the templated parameter {{job_id}}
due to a helper function used to detect and cleanup bad job names inhibiting the templating
engine to work. 

Google ML requieres a unique job id, therefore it is critical to have the possibility to customise
the job's name easily, and preferably with some data related to the DAG, for example, appending
the day to the job's name via a macro like {{ds}}


> Better handling of templated parameters in Google ML batch prediction/training operators
> ----------------------------------------------------------------------------------------
>
>                 Key: AIRFLOW-1774
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-1774
>             Project: Apache Airflow
>          Issue Type: Improvement
>          Components: contrib, operators
>    Affects Versions: Airflow 2.0, 1.9.0
>            Reporter: Guillermo Rodríguez Cano
>            Priority: Normal
>
> The {{MLEngineBatchPredictionOperator}} does not support well the templated parameter
{{job_id}} due to a helper function used to detect and cleanup bad job names inhibiting the
templating engine to work. I suspect the same may happen with {{MLEngineTrainingOperator}}
as well.
> Google ML requieres a unique job id, therefore it is critical to have the possibility
to customise the job's name easily, and preferably with some data related to the DAG, for
example, appending the day to the job's name via a macro like {{ds}}



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