examples/code_search/pipeline/CodeSearchPipelineNotebook....

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Install Kubeflow Pipelines SDK"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install https://storage.googleapis.com/ml-pipeline/release/0.1.3/kfp.tar.gz --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import required library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import kfp\n",
"from kfp import compiler\n",
"from kubernetes import client as k8s_client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import pipeline definition"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cs_pipeline import github_code_index_update"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create an experiment first"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client = kfp.Client()\n",
"exp = client.create_experiment(name='code-search-25')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Compile it into a tar package.\n",
"compiler.Compiler().compile(github_code_index_update, 'github_code_index_update.tar.gz')\n",
"\n",
"# Submit a run.\n",
"# inputs - experiment id, run name, tarball file\n",
"run = client.run_pipeline(exp.id, 'code-search-index-update', 'github_code_index_update.tar.gz')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}