mirror of https://github.com/kubeflow/examples.git
54 lines
2.3 KiB
Markdown
54 lines
2.3 KiB
Markdown
# Training the model with a notebook
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By this point, you should have a Jupyter notebook running at http://127.0.0.1:8000.
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## Download training files
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Open the Jupyter notebook interface and create a new Terminal by clicking on
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menu, *New -> Terminal*. In the Terminal, clone this git repo by executing:
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```bash
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git clone https://github.com/kubeflow/examples.git
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```
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Now you should have all the code required to complete training in the `examples/github_issue_summarization/notebooks` folder. Navigate to this folder.
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Here you should see two files:
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* `Training.ipynb`
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* `seq2seq_utils.py`
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## Perform training
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Open the `Training.ipynb` notebook. This contains a complete walk-through of
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downloading the training data, preprocessing it, and training it.
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Run the `Training.ipynb` notebook, viewing the output at each step to confirm
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that the resulting models produce sensible predictions.
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## Export trained model files
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After training completes, download the resulting files to your local machine.
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The following files are needed for serving results:
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* `seq2seq_model_tutorial.h5` - the keras model
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* `body_pp.dpkl` - the serialized body preprocessor
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* `title_pp.dpkl` - the serialized title preprocessor
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If you haven't already, clone the [kubeflow/examples](https://github.com/kubeflow/examples) repo locally, then issue the following commands to place these three files into the `github_issue_summarization/notebooks` folder on your local machine:
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```
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cd github_issue_summarization/notebooks
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PODNAME=`kubectl get pods --namespace=${NAMESPACE} --selector="app=jupyterhub" --output=template --template="{{with index .items 0}}{{.metadata.name}}{{end}}"`
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kubectl --namespace=${NAMESPACE} cp ${PODNAME}:/home/jovyan/examples/github_issue_summarization/notebooks/seq2seq_model_tutorial.h5 .
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kubectl --namespace=${NAMESPACE} cp ${PODNAME}:/home/jovyan/examples/github_issue_summarization/notebooks/body_pp.dpkl .
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kubectl --namespace=${NAMESPACE} cp ${PODNAME}:/home/jovyan/examples/github_issue_summarization/notebooks/title_pp.dpkl .
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```
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_(Optional)_ You can also perform training with two alternate methods:
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- [Training the model using TFJob](02_training_the_model_tfjob.md)
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- [Distributed training using Estimator](02_distributed_training.md)
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*Next*: [Serving the model](03_serving_the_model.md)
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*Back*: [Setup a kubeflow cluster](01_setup_a_kubeflow_cluster.md)
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