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Date: Wed, 1 Nov 2017 13:54:00 +0000 (UTC)
From: =?utf-8?Q?Guillermo_Rodr=C3=ADguez_Cano_=28JIRA=29?= <jira@apache.org>
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Subject: [jira] [Updated] (AIRFLOW-1774) Better handling of templated
 parameters in Google ML batch prediction/training operators
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archived-at: Wed, 01 Nov 2017 13:54:07 -0000


     [ https://issues.apache.org/jira/browse/AIRFLOW-1774?page=3Dcom.atlass=
ian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Guillermo Rodr=C3=ADguez Cano updated AIRFLOW-1774:
----------------------------------------------
    Description:=20
The {{MLEngineBatchPredictionOperator}} does not support well the templated=
 parameter {{job_id}} due to a helper function used to detect and cleanup b=
ad job names inhibiting the templating engine to work. I suspect the same m=
ay happen with {{MLEngineTrainingOperator}} as well.

Google ML requieres a unique job id, therefore it is critical to have the p=
ossibility to customise the job's name easily, and preferably with some dat=
a 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 b=
ad job names inhibiting the templating engine to work.=20

Google ML requieres a unique job id, therefore it is critical to have the p=
ossibility to customise the job's name easily, and preferably with some dat=
a 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/tra=
ining 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 Rodr=C3=ADguez Cano
>            Priority: Normal
>
> The {{MLEngineBatchPredictionOperator}} does not support well the templat=
ed 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 d=
ata related to the DAG, for example, appending the day to the job's name vi=
a a macro like {{ds}}


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