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Create a notebook to train a model using AutoML (kubeflow#130)
* Refactor some of the notebook setup into notebook_setup.py to make it reusable * For the kpt package to launch a notebook Add kpt setters to properly set most values. Add an application resource so that links show up in the application dashboard.
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‎Issue_Triage/notebooks/triage.ipynb

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"state:Issue needs triage:\n",
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"\t Issue needs one of the priorities ['priority/p0', 'priority/p1', 'priority/p2', 'priority/p3']\n",
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"\t Issue needs an area label\n",
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"|/home/jovyan/git_kubeflow-code-intelligence/py/issue_triage/triage.py|677|\n"
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"|/home/jovyan/git_kubeflow-code-intelligence/py/issue_triage/triage.py|677|\n",
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"INFO|2020-04-20T13:40:49|Issue https://github.com/kubeflow/examples/issues/780 already in triage project|/home/jovyan/git_kubeflow-code-intelligence/py/issue_triage/triage.py|751|\n",
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"INFO|2020-04-20T13:40:49|Issue https://github.com/kubeflow/kubeflow/issues/4894:\n",
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"state:Issue needs triage:\n",
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"|/home/jovyan/git_kubeflow-code-intelligence/py/issue_triage/triage.py|677|\n",
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"INFO|2020-04-20T13:40:49|Issue https://github.com/kubeflow/kfctl/issues/278 already in triage project|/home/jovyan/git_kubeflow-code-intelligence/py/issue_triage/triage.py|751|\n",
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‎Label_Microservice/notebooks/Label_k8s_issues_with_MLP.ipynb

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"## Background\n",
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"In this notebook, we show how to feed the embeddings from the language model into the MLP classifier. Then, we take the github repo, `kubernetes/kubernetes`, as an example. We do transfer learning and show the results.\n",
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"nbformat_minor": 2
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}

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