mirror of https://github.com/kubeflow/examples.git
836ad70421
* Add component parameters Add model_url & port arguments to flask app Add service_type, image, and model_url parameters to ui component Fix problem argument in tensor2tensor component * Fix broken UI component Fix broken UI component structure by adding all, service, & deployment parts Add parameter defaults for tfjob to resolve failures deploying other components * Add missing imports in flask app Fix syntax error in argument parsing Remove underscores from parameter names to workaround ksonnet bug #554: https://github.com/ksonnet/ksonnet/issues/554 * Fix syntax errors in t2t instructions Add CPU image build arg to docker build command for t2t-training Fix link to ksonnet app dir Correct param names for tensor2tensor component Add missing params for tensor2tensor component Fix apply command syntax Swap out log view pod for t2t-master instead of tf-operator Fix link to training with tfjob * Fix model file upload Update default params for tfjob-v1alpha2 Fix build directory path in Makefile * Resolve lint issues Lines too long * Add specific image tag to tfjob-v1alpha2 default * Fix defaults for training output files Update image tag Add UI image tag * Revert service account secret details Update associated readme |
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| docker | ||
| ks-kubeflow | ||
| notebooks | ||
| scripts | ||
| sql | ||
| tensor2tensor/github | ||
| workflow | ||
| 01_setup_a_kubeflow_cluster.md | ||
| 02_tensor2tensor_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 | ||
| README.md | ||
| requirements.txt | ||
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 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 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