Hi Bolke, Great! And yes, we will be able to maintain these operators after the contributions. We further would always insist on strong test coverage as well.
I'll open some JIRA tickets to track their implementation. Thanks, Peter On Thu, Jun 1, 2017 at 6:05 AM Bolke de Bruin <[email protected]> wrote: > Hi Peter, > > That sounds great! I think the main criteria for this is will you maintain > the code afterwards? The contrib section is slowly but steadily growing and > with operators/hooks we are particularly dependent on the community as not > all (or even none in some case) of the committers use these themselves. > > In any case test coverage is required, but that is a given I think. > > Kind regards, > Bolke > > > On 31 May 2017, at 21:10, Peter Dolan <[email protected]> > wrote: > > > > Hello developers, > > > > I work with Google Cloud ML, and my team and I are interested in > contributing a set of Operators to support working with the Cloud ML > platform. The platform supports using the TensorFlow deep neural network > framework as a managed system. > > > > In particular, we would like to contribute > > * CloudMLTrainingOperator, which would launch and monitor a Cloud ML > Training Job ( > https://cloud.google.com/ml-engine/docs/how-tos/training-jobs < > https://cloud.google.com/ml-engine/docs/how-tos/training-jobs>), > > * CloudMLBatchPredictionOperator, which would launch and monitor a > Cloud ML Batch Prediction Job ( > https://cloud.google.com/ml-engine/docs/how-tos/batch-predict < > https://cloud.google.com/ml-engine/docs/how-tos/batch-predict>), and > > * CloudMLVersionOperator, which can create, update, and delete > TensorFlow model versions ( > https://cloud.google.com/ml-engine/docs/how-tos/managing-models-jobs < > https://cloud.google.com/ml-engine/docs/how-tos/managing-models-jobs>) > > > > I'm eager to hear if the Airflow project is open to these contributions, > and if any changes are suggested. We have working prototype versions of all > of them. > > > > Thanks in advance, > > Peter > >
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