Quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart to Kubeflow\n",
    "\n",
    "For how to interact with the Kubeflow dashboard and creating notebook servers please see https://www.kubeflow.org/docs/components/notebooks/setup/.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storage and Disks\n",
    "\n",
    "Your home directory is under /home/jovyan. Aim to use it for code and scripts as its size is limited.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T15:34:42.821079Z",
     "start_time": "2020-04-06T15:34:42.644636Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-16T19:14:42.630592Z",
     "iopub.status.busy": "2021-06-16T19:14:42.630373Z",
     "iopub.status.idle": "2021-06-16T19:14:43.560254Z",
     "shell.execute_reply": "2021-06-16T19:14:43.559387Z",
     "shell.execute_reply.started": "2021-06-16T19:14:42.630534Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/jupyter-notebooks\n"
     ]
    }
   ],
   "source": [
    "! pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volumes or Data disks specified in the notebook server configuration are mounted as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T15:35:33.803690Z",
     "start_time": "2020-04-06T15:35:33.653674Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-16T19:14:43.561854Z",
     "iopub.status.busy": "2021-06-16T19:14:43.561644Z",
     "iopub.status.idle": "2021-06-16T19:14:44.439200Z",
     "shell.execute_reply": "2021-06-16T19:14:44.438373Z",
     "shell.execute_reply.started": "2021-06-16T19:14:43.561829Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "overlay         194G  109G   86G  56% /\n",
      "tmpfs            64M     0   64M   0% /dev\n",
      "tmpfs            32G     0   32G   0% /sys/fs/cgroup\n",
      "/dev/sdk         16G  2.8G   13G  18% /home/jovyan\n",
      "tmpfs            32G  8.0K   32G   1% /vault/secrets\n",
      "/dev/sda1       194G  109G   86G  56% /etc/hosts\n",
      "shm              64M  4.0K   64M   1% /dev/shm\n",
      "tmpfs            32G   12K   32G   1% /run/secrets/kubernetes.io/serviceaccount\n",
      "tmpfs            32G     0   32G   0% /proc/acpi\n",
      "tmpfs            32G     0   32G   0% /proc/scsi\n",
      "tmpfs            32G     0   32G   0% /sys/firmware\n"
     ]
    }
   ],
   "source": [
    "! df -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Resources\n",
    "\n",
    "Compute resources are available as in regular virtual machines. Monitoring can be done, for example via `htop` in a terminal.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GUI Setup\n",
    "\n",
    "The user can switch between Jupyter lab and Jupyter notebooks via \n",
    "\n",
    "* Jupyter Notebook: https://kubeflow.covid.cloud.statcan.ca/notebook/[user]/[notebook-name]/lab\n",
    "* Jupyter Lab: https://kubeflow.covid.cloud.statcan.ca/notebook/[user]/[notebook-name]/tree\n",
    "\n",
    "Another option is to enable Jupyter extensions which provide very rich enhancements from code linting to auto-correction. For that the extension manager needs to be enabled.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choice of Python, R or Julia\n",
    "\n",
    "Jupyter kernels for Python, R or Julia can be selected at notebook creation. The default kernel for this notebook is Python 3 and hence the visualzation demonstration below.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization via matplotlib is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T16:12:06.108897Z",
     "start_time": "2020-04-06T16:12:01.503895Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-16T19:14:44.441246Z",
     "iopub.status.busy": "2021-06-16T19:14:44.440975Z",
     "iopub.status.idle": "2021-06-16T19:14:44.909771Z",
     "shell.execute_reply": "2021-06-16T19:14:44.908946Z",
     "shell.execute_reply.started": "2021-06-16T19:14:44.441220Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T16:11:57.043574Z",
     "start_time": "2020-04-06T16:11:57.030722Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-16T19:14:44.911224Z",
     "iopub.status.busy": "2021-06-16T19:14:44.910921Z",
     "iopub.status.idle": "2021-06-16T19:14:44.915454Z",
     "shell.execute_reply": "2021-06-16T19:14:44.914552Z",
     "shell.execute_reply.started": "2021-06-16T19:14:44.911193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Have fun!\n"
     ]
    }
   ],
   "source": [
    "print('Have fun!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-06T16:12:53.009560Z",
     "start_time": "2020-04-06T16:12:52.420670Z"
    },
    "execution": {
     "iopub.execute_input": "2021-06-16T19:14:44.916659Z",
     "iopub.status.busy": "2021-06-16T19:14:44.916424Z",
     "iopub.status.idle": "2021-06-16T19:14:45.125942Z",
     "shell.execute_reply": "2021-06-16T19:14:45.125262Z",
     "shell.execute_reply.started": "2021-06-16T19:14:44.916637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2abd6ae3a0>]"
      ]
     },
     "execution_count": 5,
     "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": [
    "plt.plot([1,2],[3,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Jupyter functionalities please refer to the multitude of tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes this quickstart. Further application examples are provided in the sub-directories."
   ]
  }
 ],
 "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}