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   "source": [
    "# Introduction\n",
    "\n",
    "This codebase contains everything you need to get started on the case study project. The instructions below will get you up and running on Vertex Pipelines."
   ]
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  {
   "cell_type": "markdown",
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    "## Inspect the codebase\n",
    "\n",
    "- `pipeline.py` - this file is your ML pipeline definition using the KFP syntax. We have provided a very simple \"Hello world\" pipeline for you to adapt.\n",
    "- `pipeline_components.py` - use this file to create any custom KFP components that you want to use in your pipeline, for example any model training code. You can also use the Google-provided components from the `google-cloud-pipeline-components` package\n",
    "- `run_pipeline.py` - when run (see the end of this notebook), this file will compile your pipeline definition to JSON (`pipeline.json`), and submit it to be run on Vertex Pipelines. You shouldn't need to change this file, but have a look through it to understand what's going on.\n",
    "- `variables.py` - this file contains the variables for you to change\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Update your variables\n",
    "\n",
    "In _variables.py_ update the variables for your project ID. For example, if your project is _dt-mlops-user-3-dev_, update the *VERTEX_SA_EMAIL* variable to [email protected]_."
   ]
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   "id": "92892dd1-82c5-48d9-834e-f8f3f2fb0109",
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    "tags": []
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   "source": [
    "## Install Dependencies"
   ]
  },
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   "execution_count": null,
   "id": "34ed94b2-71d3-4052-86ef-24f2b9c15853",
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   "source": [
    "!pip install --user -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08e87d4-a2ff-4a54-a148-cdea63946751",
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   "source": [
    "## Compile and run the ML pipeline on Vertex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39fbfa32-2ca6-4d72-b334-2d611cfd6b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python run_pipeline.py"
   ]
  }
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