mirror of https://github.com/kubeflow/examples.git
b24152cf06
* Github Issue Summarization - Train using TFJob * Create a Dockerfile to build the image for tf-job * Create a manifest to deploy the tf-job * Create instructions on how to do all of this Fixes https://github.com/kubeflow/examples/issues/43 * Address comments * Add gcloud commands * Add ks app * Update Dockerfile base image * Python train.py fixes * Remove tfjob.yaml as it is replaced by ksonnet app * Remove plot_model_history as it is not required for tfjob training * Don't change WORKDIR * Address reviewer comments * Fix links * Fix lint issues using yapf * Sort imports |
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| README.md | ||
| querying_the_model.md | ||
| requirements.txt | ||
| serving_the_model.md | ||
| setup_a_kubeflow_cluster.md | ||
| teardown.md | ||
| training_the_model.md | ||
| training_the_model_tfjob.md | ||
README.md
[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".
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 Seldon Core
- Query the model from a simple front-end application
Steps:
- Setup a Kubeflow cluster
- Training the model. You can train the model either using Jupyter Notebook or using TFJob.
- Serving the model
- Querying the model
- Teardown