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# pkg and bin directories currently contain build artifacts | ||
# only so we exclude them. | ||
bin/ | ||
vendor/ | ||
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.vscode/ | ||
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# Compiled python files. | ||
*.pyc | ||
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# Emacs temporary files | ||
*~ | ||
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# Other temporary files | ||
.DS_Store | ||
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# temporary files from emacs flymd-mode | ||
flymd.* | ||
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# vim .swp files | ||
.swp | ||
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# Files created by Gogland IDE | ||
.idea/ | ||
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# 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 | ||
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# Bazel files | ||
**/bazel-* | ||
# Examples egg | ||
examples/tf_sample/tf_sample.egg-info/ | ||
examples/.ipynb_checkpoints/ | ||
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**/.ipynb_checkpoints | ||
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# pyenv | ||
.python-version |
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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 |
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# examples | ||
A repository to host extended examples and tutorials | ||
## A repository to host extended examples and tutorials for kubeflow. | ||
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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 | ||
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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). | ||
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## Goals | ||
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There are two primary goals for this tutorial: | ||
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* End-to-End kubeflow example | ||
* End-to-End Sequence-to-Sequence model | ||
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By the end of this tutorial, you should learn how to: | ||
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* 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 | ||
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## Steps: | ||
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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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