mirror of https://github.com/kubeflow/examples.git
13 lines
1.4 KiB
Markdown
13 lines
1.4 KiB
Markdown
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# Kubeflow Pipelines - GitHub Issue Summarization
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This Kubeflow Pipelines example shows how to build a web app that summarizes GitHub issues using Kubeflow Pipelines to train and serve a model.
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The pipeline trains a [Tensor2Tensor](https://github.com/tensorflow/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](https://github.com/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.
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The example is designed to run on a Hosted KFP installation, installed via the [Cloud Console](https://console.cloud.google.com/ai-platform/pipelines/clusters) or via ['standalone' installation](https://www.kubeflow.org/docs/pipelines/installation/standalone-deployment/) instructions, but would also be straightforward to run on a Kubeflow installation with minor changes.
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You can follow this example as a codelab: [g.co/codelabs/kfp-gis](https://g.co/codelabs/kfp-gis).
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<!-- Or, you can run it as a [Cloud shell Tutorial](https://console.cloud.google.com/?cloudshell=true&cloudshell_git_repo=https://github.com/kubeflow/examples&working_dir=github_issue_summarization/pipelines&cloudshell_tutorial=tutorial.md). The source for the Cloud Shell tutorial is [here](tutorial.md). -->
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