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texasmichelle committed Mar 2, 2018
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41 changes: 41 additions & 0 deletions .gitignore
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# pkg and bin directories currently contain build artifacts
# only so we exclude them.
bin/
vendor/

.vscode/

# Compiled python files.
*.pyc

# Emacs temporary files
*~

# Other temporary files
.DS_Store

# temporary files from emacs flymd-mode
flymd.*

# vim .swp files
.swp

# Files created by Gogland IDE
.idea/

# Exclude wheel files for now.
# The only wheel file is the TF wheel one which is quite large.
# We don't want to check that into source control because it could be
# quite large.
*.whl

# Bazel files
**/bazel-*
# Examples egg
examples/tf_sample/tf_sample.egg-info/
examples/.ipynb_checkpoints/

**/.ipynb_checkpoints

# pyenv
.python-version
21 changes: 21 additions & 0 deletions OWNERS
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# TODO(jlewi): We should probably have OWNERs files in subdirectories that
# list approvers for individual components (e.g. Seldon folks for Seldon component)
approvers:
- ankushagarwal
- DjangoPeng
- gaocegege
- jlewi
- llunn
- ScorpioCPH
reviewers:
- ankushagarwal
- DjangoPeng
- gaocegege
- Jimexist
- jlewi
- llunn
- nkashy1
- ScorpioCPH
- texasmichelle
- wbuchwalter
- zjj2wry
5 changes: 3 additions & 2 deletions README.md
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# examples
A repository to host extended examples and tutorials
## A repository to host extended examples and tutorials for kubeflow.

1. [Github issue summarization using sequence-to-sequence learning](./issue_summarization_github_issues) by [Hamel Husain](https://github.com/hamelsmu)
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# [WIP] End-to-End kubeflow tutorial using a Sequence-to-Sequence model

This example demonstrates how you can use `kubeflow` end-to-end to train and
serve a Sequence-to-Sequence model on an existing kubernetes cluster. This
tutorial is based upon @hamelsmu's article ["How To Create Data Products That
Are Magical Using Sequence-to-Sequence
Models"](https://medium.com/@hamelhusain/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8).

## Goals

There are two primary goals for this tutorial:

* End-to-End kubeflow example
* End-to-End Sequence-to-Sequence model

By the end of this tutorial, you should learn how to:

* Setup a Kubeflow cluster on an existing Kubernetes deployment
* Spawn up a Jupyter Notebook on the cluster
* Spawn up a shared-persistent storage across the cluster to store large
datasets
* Train a Sequence-to-Sequence model using TensorFlow on the cluster using
GPUs
* Serve the model using TensorFlow Serving

## Steps:

1. [Setup a Kubeflow cluster](setup_a_kubeflow_cluster.md)
1. [Training the model](training_the_model.md)
1. [Teardown](teardown.md)
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