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Amy 44d722222f
update kfp 'github issue summarization' example (#823) 
* checkpointing

* more updates to keep gh summ pipelines example current
cleanup & update; remove obsolete pipelines
create 'preemptible' version of hosted kfp pipeline
notebook update, readme update

* in notebook, add kernel restart after pip install
minor pipeline cleanup
add archive version of pipeline

* fixed namespace glitch, cleaned up css positioning issue
 		2020-10-06 05:13:43 -07:00
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components update kfp 'github issue summarization' example (#823) 2020-10-06 05:13:43 -07:00
example_pipelines update kfp 'github issue summarization' example (#823) 2020-10-06 05:13:43 -07:00
README.md update kfp 'github issue summarization' example (#823) 2020-10-06 05:13:43 -07:00
tutorial.md Update to KFP pipelines codelab code (GH summarization) (#638) 2019-09-19 08:47:00 -07:00

README.md

Kubeflow Pipelines - GitHub Issue Summarization

This Kubeflow Pipelines example shows how to build a web app that summarizes GitHub issues using Kubeflow Pipelines to train and serve a model. The pipeline trains a Tensor2Tensor model on GitHub issue data, learning to predict issue titles from issue bodies. It then exports the trained model and deploys the exported model using Tensorflow Serving. The final step in the pipeline launches a web app, which interacts with the TF-Serving instance in order to get model predictions.

The example is designed to run on a Hosted KFP installation, installed via the Cloud Console or via 'standalone' installation instructions, but would also be straightforward to run on a Kubeflow installation with minor changes.

You can follow this example as a codelab: g.co/codelabs/kfp-gis.