{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "clear-formation",
   "metadata": {},
   "source": [
    "# EasyVVUQ and Cloud Execution via Kubernetes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moved-lindsay",
   "metadata": {},
   "source": [
    "**Author**: Vytautas Jancauskas, LRZ (jancauskas@lrz.de)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upset-throw",
   "metadata": {},
   "source": [
    "To run code examples in this tutorial you will want to download it first and run it on a Jupyter notebook server running locally. That is because the examples assume you have configured access to a Kubernetes cluster. So if you are viewing this in our Binder you will want to instead open the copy of this tutorial that is located in the EasyVVUQ source code under ```tutorials/kubernetes``` And then you will want to open that notebook in your local Jupyter instance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compact-calendar",
   "metadata": {},
   "source": [
    "This tutorial assumes that you have access to a Kubernetes cluster. Like the ones provided by Google or Amazon. The next thing you need to do is to build a Docker container for your application. I have found that most online resources don't explain it adequately in the context relevant to us so I will outline the required steps here.\n",
    "\n",
    "Our focus here is the Kubernetes execution method - the code executed is based on our \"[Vector Quantities of Interest](./vector_qoi_tutorial.ipynb)\" tutorial. If you are unfamiliar with EasyVVUQ we recommend that you read this previous tutorial before continuing with this one."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developing-facing",
   "metadata": {},
   "source": [
    "The first thing you need is a Dockerfile providing instructions on how the execution environment should be setup (which software to build and install etc.). Here is the Dockerfile we made for EasyVVUQ. Yours will look different but I hope you will see that it is fairly straightforward. For further information please consult this [guide](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "irish-baker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FROM ubuntu:latest\r\n",
      "\r\n",
      "RUN apt-get update && \\\r\n",
      "    apt-get install -y python3-pip && \\\r\n",
      "    apt-get install -y git && \\\r\n",
      "    apt-get install -y tini && \\\r\n",
      "    pip3 install easyvvuq && \\\r\n",
      "    git clone https://github.com/UCL-CCS/EasyVVUQ.git\r\n",
      "\r\n",
      "ENTRYPOINT [\"tini\", \"--\"]\r\n"
     ]
    }
   ],
   "source": [
    "!cat kubernetes/Dockerfile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neutral-publisher",
   "metadata": {},
   "source": [
    "You will need to register for an account on [DockerHub](https://hub.docker.com/). Or if you don't want your Docker image to be publicly accessible you need to look into private registries, for example [here](https://cloud.google.com/container-registry/docs/quickstart), but this will usually be provider specific. Then you need to build your container, login to DockerHub and push the image. After that it will be accessible to run in your Kubernetes cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprising-singles",
   "metadata": {},
   "source": [
    "```docker build -t user/imagename:tag .```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-harrison",
   "metadata": {},
   "source": [
    "```docker login```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weird-homeless",
   "metadata": {},
   "source": [
    "```docker push user/imagename:tag```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "isolated-softball",
   "metadata": {},
   "source": [
    "In order for the code examples below to work you need to have a valid ```~/.kube/config``` file. Details will differ based on your provider, but in the case of Google Cloud you can do it as shown below. Here ```easyvvuq``` is a cluster name. It is created from a cluster template. Again, this will be provider specific, consult their documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "narrow-integral",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;33mWARNING:\u001b[0m Starting in January 2021, clusters will use the Regular release channel by default when `--cluster-version`, `--release-channel`, `--no-enable-autoupgrade`, and `--no-enable-autorepair` flags are not specified.\n",
      "\u001b[1;33mWARNING:\u001b[0m Currently VPC-native is not the default mode during cluster creation. In the future, this will become the default mode and can be disabled using `--no-enable-ip-alias` flag. Use `--[no-]enable-ip-alias` flag to suppress this warning.\n",
      "\u001b[1;33mWARNING:\u001b[0m Starting with version 1.18, clusters will have shielded GKE nodes by default.\n",
      "\u001b[1;33mWARNING:\u001b[0m Your Pod address range (`--cluster-ipv4-cidr`) can accommodate at most 1008 node(s). \n",
      "\u001b[1;33mWARNING:\u001b[0m Starting with version 1.19, newly created clusters and node-pools will have COS_CONTAINERD as the default node image when no image type is specified.\n",
      "Creating cluster easyvvuq in us-central1-f... Cluster is being health-checked..\n",
      ".⠏                                                                             \n",
      "Creating cluster easyvvuq in us-central1-f... Cluster is being health-checked (\n",
      "master is healthy)...done.                                                     \n",
      "Created [https://container.googleapis.com/v1/projects/graphite-flare-278712/zones/us-central1-f/clusters/easyvvuq].\n",
      "To inspect the contents of your cluster, go to: https://console.cloud.google.com/kubernetes/workload_/gcloud/us-central1-f/easyvvuq?project=graphite-flare-278712\n",
      "kubeconfig entry generated for easyvvuq.\n",
      "NAME      LOCATION       MASTER_VERSION   MASTER_IP      MACHINE_TYPE  NODE_VERSION     NUM_NODES  STATUS\n",
      "easyvvuq  us-central1-f  1.17.15-gke.800  35.238.138.25  e2-medium     1.17.15-gke.800  3          RUNNING\n"
     ]
    }
   ],
   "source": [
    "!gcloud container clusters create easyvvuq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "young-syndicate",
   "metadata": {},
   "source": [
    "The code in the cells below sets up an EasyVVUQ campaign to analyse a simple epedimiological model (using the `sir` code) - for more details see the \"[Vector Quantities of Interest](./vector_qoi_tutorial.ipynb)\" tutorial. We will only explain the differences caused by Kubernetes execution in this document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "julian-glasgow",
   "metadata": {},
   "outputs": [],
   "source": [
    "import easyvvuq as uq\n",
    "import chaospy as cp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "recorded-refrigerator",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    \"S0\": {\"type\": \"float\", \"default\": 997}, \n",
    "    \"I0\": {\"type\": \"float\", \"default\": 3}, \n",
    "    \"beta\": {\"type\": \"float\", \"default\": 0.2}, \n",
    "    \"gamma\": {\"type\": \"float\", \"default\": 0.04, \"min\": 0.0, \"max\": 1.0},\n",
    "    \"iterations\": {\"type\": \"integer\", \"default\": 100},\n",
    "    \"outfile\": {\"type\": \"string\", \"default\": \"output.csv\"}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "found-consumer",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = uq.encoders.GenericEncoder(template_fname='sir.template', delimiter='$', target_filename='input.json')\n",
    "decoder = uq.decoders.SimpleCSV(target_filename='output.csv', output_columns=['I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "administrative-north",
   "metadata": {},
   "outputs": [],
   "source": [
    "campaign = uq.Campaign(name='sir', params=params, encoder=encoder, decoder=decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "diverse-desktop",
   "metadata": {},
   "outputs": [],
   "source": [
    "vary = {\n",
    "    \"beta\": cp.Uniform(0.15, 0.25),\n",
    "    \"gamma\": cp.Normal(0.04, 0.01),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "alleged-method",
   "metadata": {},
   "outputs": [],
   "source": [
    "campaign.set_sampler(uq.sampling.PCESampler(vary=vary, polynomial_order=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technical-blues",
   "metadata": {},
   "source": [
    "The only difference between this example and the one where we run the simulation locally is the type of action we pass to `sample_and_apply`. In this case we use `ExecuteKubernetes` (in contrast to `ExecuteLocalV2` employed for local exection). For this simple application we only need to specify two arguments to `ExecuteKubernetes` - the image to be pulled from DockerHub and a way to run the simulation. \n",
    "\n",
    "The first argument to ```ExecuteKubernetes``` is the image specified using the tag specified in your build command in the format `user/imagename:tag` (below we use `orbitfold/easyvvuq:latest`). The input configuration is automatically transferred to the Kubernetes pod using the Kubernetes API. Input files will be stored under the ```/config``` directory. You need to have this in mind when running the simulation. Likewise the results are retrieved from standard output of the pod. This output is sent directly to the Decoder. Which might mean you will want to exercise some care when designing decoders for these cases. In this case there is nothing special to be done. If your simulation produces a lot of data you might have to use a script inside the container to extract the quantities of interest and print them to ```stdout```."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-jaguar",
   "metadata": {},
   "source": [
    "The second argument to ```ExecuteKubernetes``` is the command to be executed inside the running container. If you look at the way we have created the Docker image, EasyVVUQ is cloned to the root directory, which means the path to the simulation code is ```/EasyVVUQ/tutorials/sir```. We run the ```sir``` simulation and then we print the ```output.csv``` file to ```stdout``` using the ```cat``` command. It will be picked up by our Kubernetes backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "agricultural-radiation",
   "metadata": {},
   "outputs": [],
   "source": [
    "execution = campaign.sample_and_apply(\n",
    "    action=uq.actions.ExecuteKubernetes(\n",
    "        \"orbitfold/easyvvuq:latest\",\n",
    "        \"/EasyVVUQ/tutorials/sir /config/input.json && cat output.csv\"), \n",
    "    batch_size=8).start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "quantitative-catch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ready': 0, 'active': 0, 'finished': 36, 'failed': 0}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "execution.progress()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "novel-allowance",
   "metadata": {},
   "source": [
    "The remaining steps are exactly the same as we would have in the case of local execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "given-sample",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = campaign.analyse(qoi_cols=['I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ranking-store",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t', ylabel='First Order Sobol Index'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_sobols_first('I', xlabel='t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "awful-register",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='t', ylabel='I'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot_moments('I', xlabel='t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sexual-spain",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}