mirror of https://github.com/kubeflow/examples.git
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* Add e2e test for xgboost housing example * fix typo add ks apply add [ modify example to trigger tests add prediction test add xgboost ks param rename the job name without _ use - instead of _ libson params rm redudent component rename component in prow config add ames-hoursing-env use - for all names use _ for params names use xgboost_ames_accross rename component name shorten the name change deploy-test command change to xgboost- namespace init ks app fix type add confest.py change path change deploy command change dep change the query URL for seldon add ks_app with seldon lib update ks_app use ks init only rerun change to kf-v0-4-n00 cluster add ks_app use ks-13 remove --namespace use kubeflow as namespace delete seldon deployment simplify ks_app retry on 503 fix typo query 1285 move deletion after prediction wait 10s always retry till 10 mins move check to retry fix pylint move clean-up to the delete template * set up xgboost component * check in ks component& run it directly * change comments * add comment on why use 'ks delete' * add two modules to pylint whitelist * ignore tf_operator/py * disable pylint per line * reorder import |
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| 01_setup_a_kubeflow_cluster.md | ||
| 02_distributed_training.md | ||
| 02_training_the_model.md | ||
| 02_training_the_model_tfjob.md | ||
| 03_serving_the_model.md | ||
| 04_querying_the_model.md | ||
| 05_teardown.md | ||
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| README.md | ||
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README.md
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".
Goals
There are two primary goals for this tutorial:
- Demonstrate an End-to-End kubeflow example
- Present an 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 a Jupyter Notebook on the cluster
- Spawn a shared-persistent storage across the cluster to store large datasets
- Train a Sequence-to-Sequence model using TensorFlow and GPUs on the cluster
- Serve the model using Seldon Core
- Query the model from a simple front-end application
Steps:
- Setup a Kubeflow cluster
- Training the model. You can train the model using any of the following methods using Jupyter Notebook or using TFJob:
- Serving the model
- Querying the model
- Teardown