Colab-Intro.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Colab-Intro.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soltaniehha/Intro-to-Colab/blob/main/Colab-Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkcI78C4Sybk"
      },
      "source": [
        "# Basic operations in Colaboratory notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1eAzUL-twns"
      },
      "source": [
        "Created: Feb 2019\n",
        "\n",
        "Updated: March 2021\n",
        "\n",
        "Authors: Maxim Ziatdinov & Mohammad Soltanieh-ha\n",
        "\n",
        "Email: ziatdinovmax@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DTQU4m2Uk35"
      },
      "source": [
        "---\n",
        "\n",
        "[Colaboratory](https://research.google.com/colaboratory/faq.html) (or Colab) is a free research tool from Google for machine learning education and research built on top of [Jupyter Notebook](https://jupyter.org/). From our experience, it is also an ideal tool for scientific collaboration and sharing your results with non-experts (as opposed to, say, collaboration via a GitHub project), especially when there are drastic fluctuations in coding skills across a team/collaboration. This particular notebook starts with a brief overview of Colab's UI and then shows some simple operations in Colab notebooks, including some basic plotting, code parameterization, usage of shell commands and installation of additional Python packages. Notice that many popular Python packages, such as tensorflow, pytorch, scikit, opencv, are already installed in Colaboratory. So we can just import them into our notebook and start having fun.\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvZo58B9gh8M"
      },
      "source": [
        "##Notebook rules:\n",
        "\n",
        "Some basic notebook rules:\n",
        "\n",
        "*General Jupyter notebooks rules:*\n",
        "\n",
        "\n",
        "1. Click inside a code cell and press SHIFT+ENTER to execute it.\n",
        "2. Use a Text cell with Markdown syntax to create rich text.\n",
        "3. Execute cells TOP TO BOTTOM.  \n",
        "\n",
        "*Colab-specific rules:*\n",
        "\n",
        "4. Notebooks are saved to your Google Drive unless you're in Playground mode or have opened a notebook hosted on GitHub (like this notebook). In those scenarios you can click on \"COPY TO DRIVE\" to save a copy to your Drive.\n",
        "5. Mount your Google Drive to have a direct access from a notebook to the files stored in the drive (this includes Shared Drives).\n",
        "6. If using Colab's virtual storage only, all the uploaded/stored files will get deleted when a runtime is recycled.\n",
        "7. In addition to SHIFT+ENTER, in Colab you can also execute a cell CTRL+ENTER, COMMAND+ENTER (MacOS), or by clicking the play button which appears after hovering over the left side of the cell.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSmlIG2Z3xg9"
      },
      "source": [
        "## Colab UI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePS_CtpL3CM_"
      },
      "source": [
        "###Opening notebooks\n",
        "\n",
        "\n",
        "You can open any notebook hosted on GitHub by going to Colaboratory [start page](https://colab.research.google.com) and selecting GITHUB from a menu dialog. Then search for a specific GitHub repository and choose a notebook that you would like to open from the list of notebooks in that repository. From the same [start page](https://colab.research.google.com) you can view and open notebooks stored in your GDrive, upload a regular Jupyter notebook from your local machine and start an entirely new notebook. Colab currently only supports Python 3 kernels. You can also choose between GPU and [TPU](https://colab.research.google.com/notebooks/tpu.ipynb) hardware accelerator options for your Colab notebook  (*Edit --> Notebook settings* or *Runtime --> Change runtime type*)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BkkrjXN8x_Vq"
      },
      "source": [
        "###Upload/download files\n",
        "\n",
        "\n",
        "Once you open a Google Colab notebook, it creates a virtual machine instance on Google Cloud. To upload files from your local machine to Colab virtual storage, use UPLOAD option from the left sidebar. To download files from Colab's virtual storage to your local machine, right-click on a file and then select ''Download\".\n",
        "\n",
        "###Mount Drive\n",
        "You can also mount your google drive: once you click on MOUNT DRIVE in the left sidebar, it will insert a code cell into your notebook that you'll need to run to mount your google drive (it will ask for your authorization). Another way to download files (without mounting a google drive) is to use a `!gdown` or `!wget` commands (more details in the [Shell commands](#scrollTo=JrF12-bqPKPm) section)<br><br>\n",
        "\n",
        "<img src=\"https://github.com/soltaniehha/Intro-to-Colab/blob/main/figs/file-system.png?raw=true\" width=\"800\" align=\"center\"/><br><br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TnTPz-xphLPC",
        "outputId": "47e9c84f-ecea-46d8-962b-738f5e935ec8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKvGJM5LhjVj",
        "outputId": "0e483174-3bc2-47c2-fab9-318348de1eee"
      },
      "source": [
        "ls /content/drive/"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mMyDrive\u001b[0m/  \u001b[01;34mShareddrives\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztbOYEUwyDIq"
      },
      "source": [
        "### Share your notebooks\n",
        "\n",
        "You may share your notebooks the same way you share Google Docs and Google Slides (it needs to be stored in your/team Google Drive for this). You can also comment on individual code cells in a notebook and your collaborators will receive your comments (in a form of email notification), as long as the notebook is shared with them. To leave a comment, hover over the right side of any code cell and click *Add a Comment*.  Alternatively, you may use a *Ctrl+Alt+M* shortcut. The ability to share comments can be very useful if you make some important additions/changes to the notebook (e.g. when you have an eureca moment) and you want your collaborators to see those additions/changes asap. It is also very useful when you get stuck and need help/advice.<br><br>\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1pofWWSCqhtKBxxjS28nP7mA2pgfJ5Zet\" width=800 px><br><br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LznJ4fbmiZvW"
      },
      "source": [
        "## Simple math with numpy and matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sl1VzZSRkGGp"
      },
      "source": [
        "We can use \"standard\" python packages like [numpy](https://www.numpy.org/) and [matplotlib](https://matplotlib.org/) as well any many other popular packages for data manipulation and data vizualization, which are pre-installed in Colab ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYNq0mwTkGGn"
      },
      "source": [
        "# Package for manipulation with numerical data\n",
        "import numpy as np\n",
        "# Package for plotting\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAkwo2VSkGGl"
      },
      "source": [
        "As an example, let's make a plot of *sin* and *cos* waves using numpy and matplotlib.\n",
        "\n",
        "First, define *'x'* and *'y'*:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUshwphCkGGh"
      },
      "source": [
        "# creates array of data points between 0 and 10, with 0.01 interval\n",
        "x = np.arange(0, 10, 0.01)\n",
        "# calculates sin function at each point\n",
        "y_sin = np.sin(x)\n",
        "# calculates cos function at each point\n",
        "y_cos = np.cos(x)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAoobxVIkGGf"
      },
      "source": [
        "Now plot sine and cosine waves:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hW1uYFIikGGR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "18ad46ce-93f9-4b6f-9fbb-5e614dfc65f7"
      },
      "source": [
        "# creates a figure and a set of subplots (ax1 and ax2)\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(8, 8))\n",
        "# plot sine wave in the first subplot\n",
        "ax1.plot(x, y_sin)\n",
        "ax1.set_title('Sine wave')\n",
        "ax1.set_ylabel('y', fontsize=14, style='italic')\n",
        "# plot cosine wave in the second subplot\n",
        "ax2.plot(x, y_cos)\n",
        "ax2.set_title('Cosine wave')\n",
        "ax2.set_ylabel('y', fontsize=14, style='italic')\n",
        "# this axis is shared by two subplots\n",
        "ax2.set_xlabel('x (rad)', fontsize=14, style='italic');"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exESruHD_X4M"
      },
      "source": [
        "## Forms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_e5IYOdCUcX"
      },
      "source": [
        "You can use forms to parameterize your code. To add a form, use *Insert --> Add form field* while your cursor is a Code cell.  Everytime you change the value in a form, the corresponding value in the code cell will adjust accordingly. Press SHIFT+ENTER or \"PLAY\" button on the left side to run the cell. In the example below we can adjust $a$ and $b$ parameters for $y = a sin(bx)$ function, level of noise and a figure size:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6Vr2Z9Q_ncJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "13a45380-4953-48b5-85e5-3580debdf548"
      },
      "source": [
        "#@title Using Forms in Colab\n",
        "a = 2 #@param {type:\"slider\", min:0.5, max:5, step:0.1}\n",
        "b = 2.1 #@param {type:\"slider\", min:0.5, max:5, step:0.1}\n",
        "c = 0.5 #@param {type: \"slider\", min:0, max:5, step:0.1}\n",
        "noise = 0.7 #@param {type:\"slider\", min:0, max:1, step:0.1}\n",
        "fig_size = 7 #@param {type:\"slider\", min:4, max:10, step:1}\n",
        "\n",
        "x = np.arange(-10, 10, 0.01)\n",
        "y_sin = a*np.sin(b*x) + c + noise*np.random.normal(size=x.shape)\n",
        "plt.figure(figsize=(2*fig_size, fig_size))\n",
        "plt.plot(x, y_sin)\n",
        "plt.xlabel('x', fontsize=14, style='italic')\n",
        "plt.ylabel('y', fontsize=14, style='italic')\n",
        "plt.title('Sine wave')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrdTXAuy1Oym"
      },
      "source": [
        "Notice that you can also hide the entire code field (*More cell actions --> Form --> Hide code* or just double-clicking on a form)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yir7C8XjKmvC"
      },
      "source": [
        "## Shell commands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_vA9wnlKrhR"
      },
      "source": [
        "Unlike Jupyter Lab, Colab doesn't have an option to open and work in a terminal/shell unless you are using Colab Pro which is not free. However, you can invoke shell commands by using either  `%%shell`  or  `%%bash` [\"cell magics\"](https://ipython.readthedocs.io/en/stable/interactive/magics.html#cell-magics) or by prefixing the command with `!` ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9G-TwSwIg0C"
      },
      "source": [
        "Notice that usually it is more practical to use a \"scratch cell\" for interacting with a shell instead of adding additional cells to your notebook, especially for one-line shell commands (*Insert* --> *Scratch code cell* or Ctrl+Alt+N shortcut) : <br><br>\n",
        "\n",
        "<img src=\"https://github.com/soltaniehha/Intro-to-Colab/blob/main/figs/scratch-code.png?raw=true\" width=\"800\" align=\"center\"/><br><br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sopDU856VDMk"
      },
      "source": [
        "The following command will list files and sub-directories in the current directory:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-5Pn03qLIDi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "758c41e4-d9fe-4c71-d0a9-aa40f15f9748"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1FXV_S3sHdA"
      },
      "source": [
        "Note that some of the commonly used shell commands can be accessed directly in a cell, such as `ls` and `pwd`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "6uxeHygHsQbJ",
        "outputId": "302a4cfd-cf63-4d32-8f4c-4c12d4550f5e"
      },
      "source": [
        "pwd"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvSTxGcyLa_b"
      },
      "source": [
        "Let's create an empty directory. We first confirm that a directotry with such name doesn't already exist. Since this will result in multiple lines of code, we use `%%bash` command at the beginning of the cell, which indicates that all the code lines in this cell should be interpreted as shell script."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xB78yEw8LNh0"
      },
      "source": [
        "%%bash\n",
        "newdir=\"ColabIntro\"\n",
        "if [ ! -d $newdir ]; then\n",
        "  mkdir $newdir\n",
        "fi"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJiI-UdkLgcH"
      },
      "source": [
        "View again files and directories in the current folder. You should now be able to see a new folder that we've just created."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpoGjX05L-Sj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ffe4b51-b063-41a6-81de-f8704e1183c9"
      },
      "source": [
        "ls"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mColabIntro\u001b[0m/  \u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RIf0uSPHGt7"
      },
      "source": [
        "We can view a content of our new directory, which should be empty:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wy1ezWQiHNAF"
      },
      "source": [
        "ls ColabIntro"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZ926FpwNkb4"
      },
      "source": [
        "Let's create a text file inside our new directory. We will place a \"Hello World\" greeting on the first line of this text file:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhGHmDBBMMRz"
      },
      "source": [
        "!echo \"Hello World\" > ColabIntro/test.txt "
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bkp3_dQONth7"
      },
      "source": [
        "View content of our new folder. We should be able to see the text file we just created."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9S3SOOzMeBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb04b4ed-2d44-485b-ba11-48364ae998f4"
      },
      "source": [
        "ls ColabIntro"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1LnXKPaN5hz"
      },
      "source": [
        "We can read a text file with `cat` command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLgPsNALMlzb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92e9c89c-c879-49a4-ca48-f4db0f4138c7"
      },
      "source": [
        "!cat ColabIntro/test.txt"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello World\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7PZMmGZuH16c"
      },
      "source": [
        "We can also move or copy files between directories directly from a notebook. In the example below we copy the created text file to our root folder:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lhglgu1IDcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dec4795-a439-4fca-feb1-f377ebdbe960"
      },
      "source": [
        "!cp ColabIntro/test.txt ./\n",
        "!ls"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ColabIntro  drive  sample_data\ttest.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bH2LsgR6MxqX"
      },
      "source": [
        "We can also write individual Python files to \"disk\" directly from a notebook. For example, it is common to store utility (\"helper\") functions defined in your notebook for some data analysis procedures in a separate python module (called e.g. utils.py) and then import them into the notebook. We can write a file to disk (virtual storage or GDrive) either with standard shell commands or with a notebook \"magic\" command (preferred). Let's first do it with a standard shell command:\n",
        "\n",
        "**Notice that now Colab allows creating Python files and editing them in a separate \"tab\".**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN43YkideiKo"
      },
      "source": [
        "%%bash\n",
        "echo \"\"\"# -*- coding: utf-8 -*-\n",
        "'''util functions'''\n",
        "\n",
        "##### Import necessary modules #####\n",
        "\n",
        "def pow3(x):\n",
        "    '''Returns x cubed'''\n",
        "        \n",
        "    return x ** 3\"\"\" > utils1.py"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PM8j2Vc4eh0I"
      },
      "source": [
        "View the Python file we just created:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbZXkjB5ehd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a1ea050-2f42-431b-9f13-b615a8c60452"
      },
      "source": [
        "!cat utils1.py"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# -*- coding: utf-8 -*-\n",
            "'''util functions'''\n",
            "\n",
            "##### Import necessary modules #####\n",
            "\n",
            "def pow3(x):\n",
            "    '''Returns x cubed'''\n",
            "        \n",
            "    return x ** 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrIL5G8GNhaO"
      },
      "source": [
        "Append to the already existing python file (e.g. add another utility function):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4IT6t-nNBku"
      },
      "source": [
        "%%bash\n",
        "echo \"\"\"\n",
        "def util2():\n",
        "    '''Brief description'''\n",
        "    \n",
        "    ##### Function body #####\n",
        "    \n",
        "    return\"\"\" >> utils1.py"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzSXrhe_PxXW"
      },
      "source": [
        "View our Python file again:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFA2mtRQNvN6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d562da9d-539d-48e1-bd7f-0c4c177d53c7"
      },
      "source": [
        "!cat utils1.py"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# -*- coding: utf-8 -*-\n",
            "'''util functions'''\n",
            "\n",
            "##### Import necessary modules #####\n",
            "\n",
            "def pow3(x):\n",
            "    '''Returns x cubed'''\n",
            "        \n",
            "    return x ** 3\n",
            "\n",
            "def util2():\n",
            "    '''Brief description'''\n",
            "    \n",
            "    ##### Function body #####\n",
            "    \n",
            "    return\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpTOFJmVJSyi"
      },
      "source": [
        "However it is much easier to write and edit+overwrite individual files with `%%writefile` \"magic\" command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPb5HrAGIUQh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da9ac2f8-12f2-454e-b7b4-f56ab862d266"
      },
      "source": [
        "%%writefile utils2.py\n",
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"util functions\"\"\"\n",
        "\n",
        "##### Import necessary modules #####\n",
        "\n",
        "def pow3(x):\n",
        "    '''Returns x cubed'''\n",
        "        \n",
        "    return x ** 3\n",
        "\n",
        "print('testing the pow3 function, when x = 3:', pow3(3))\n",
        "\n",
        "def util2():\n",
        "    \"\"\"Brief description\"\"\"\n",
        "    \n",
        "    ##### Function body #####\n",
        "    \n",
        "    return"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Writing utils2.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cW8dMfKdVq_"
      },
      "source": [
        "View file content:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNfmQPZoKFtU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c64e070-b412-416a-b9cd-e798c35d7b13"
      },
      "source": [
        "!cat utils2.py"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# -*- coding: utf-8 -*-\n",
            "\"\"\"util functions\"\"\"\n",
            "\n",
            "##### Import necessary modules #####\n",
            "\n",
            "def pow3(x):\n",
            "    '''Returns x cubed'''\n",
            "        \n",
            "    return x ** 3\n",
            "\n",
            "print('testing the pow3 function, when x = 3:', pow3(3))\n",
            "def util2():\n",
            "    \"\"\"Brief description\"\"\"\n",
            "    \n",
            "    ##### Function body #####\n",
            "    \n",
            "    return"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vlQ59zp4rAU"
      },
      "source": [
        "You can even execute these python files from the notebook using the `%run` magic function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "KXsycpz64uYr",
        "outputId": "d71cbb0a-59bf-4439-b58d-cb3b8d2f0e0e"
      },
      "source": [
        "%run utils2.py"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "testing the pow3 function, when x = 3: 27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhgYhpTp5WuH"
      },
      "source": [
        "# checking the docstring\n",
        "pow3??"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrF12-bqPKPm"
      },
      "source": [
        "Finally to download a large file from a google drive into your notebook, you can use a [gdown](https://pypi.org/project/gdown/) command followed by a *download* link. The difference between shared link and download link is that you need to replace 'open' with 'uc' in the link address. For example, this won't work:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-W5kGPe1PlMd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe28150f-cbd3-45e1-9d9b-d3be0ac91dd5"
      },
      "source": [
        "!gdown https://drive.google.com/open?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/gdown/parse_url.py:31: UserWarning: You specified Google Drive Link but it is not the correct link to download the file. Maybe you should try: https://drive.google.com/uc?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq\n",
            "  .format(url='https://drive.google.com/uc?id={}'.format(file_id))\n",
            "Downloading...\n",
            "From: https://drive.google.com/open?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq\n",
            "To: /content/open?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq\n",
            "62.8kB [00:00, 9.03MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mkN0EQSvP7BH"
      },
      "source": [
        "But once you change 'open' to 'uc' it starts working:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6KVbRNGQCCV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c424e53f-d4ba-4d79-b54b-3658d6550543"
      },
      "source": [
        "!gdown https://drive.google.com/uc?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1nJZ9OmrbHVLZIRlCylb5cYrOxnqBLCYq\n",
            "To: /content/atom2.npy\n",
            "\r  0% 0.00/13.6k [00:00<?, ?B/s]\r100% 13.6k/13.6k [00:00<00:00, 23.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zJv9zttQNc-j"
      },
      "source": [
        "Now let's remove the directory and file(s) we created:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGRC4zyxNVpB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "945ab19a-dc3f-47d5-b6f2-858a6be4de73"
      },
      "source": [
        "!rm -r ColabIntro test.txt *.py *.npy open?*\n",
        "!ls"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVF4Y2Mg66wo"
      },
      "source": [
        "##Debugging\n",
        "In Jupyter notebook, perhaps one of the most convenient interfaces to debugging is the `%debug` magic command. If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception. The ipdb prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\n",
        "\n",
        "Let's look at the following exception, then do some basic tasks–print the values of a and b, and type quit to quit the debugging session:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "nJ7BJiaN7mxz",
        "outputId": "daedf369-11eb-4c0a-dec9-0ae52272857e"
      },
      "source": [
        "def func1(a, b):\n",
        "    return a / b\n",
        "\n",
        "def func2(x):\n",
        "    a = x\n",
        "    b = x - 1\n",
        "    return func1(a, b)\n",
        "\n",
        "func2(1)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ZeroDivisionError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-9c43406b00a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-27-9c43406b00a2>\u001b[0m in \u001b[0;36mfunc2\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-9c43406b00a2>\u001b[0m in \u001b[0;36mfunc1\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LIbJD017sAi",
        "outputId": "6fb50b5c-ff01-45b7-9774-f9ba970b52cf"
      },
      "source": [
        "%debug"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "> \u001b[0;32m<ipython-input-27-9c43406b00a2>\u001b[0m(2)\u001b[0;36mfunc1\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m      1 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m----> 2 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      4 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\n",
            "ipdb> print(b)\n",
            "0\n",
            "ipdb> up\n",
            "> \u001b[0;32m<ipython-input-27-9c43406b00a2>\u001b[0m(7)\u001b[0;36mfunc2\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m      5 \u001b[0;31m    \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mfunc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      8 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\u001b[0;32m      9 \u001b[0;31m\u001b[0mfunc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0m\n",
            "ipdb> print(x)\n",
            "1\n",
            "ipdb> q\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdYs8IkFaddC"
      },
      "source": [
        "## Installations\n",
        "\n",
        "Colab has most of popular machine/deep learning packages pre-installed but if a package that you would like to use is not in Colab, you can install it using ```pip``` command. For example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLWDbFfGavkk"
      },
      "source": [
        "try:\n",
        "    import numpyro\n",
        "except ModuleNotFoundError:\n",
        "    !pip install numpyro # it is okay to just use this last line"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZBB0g4ebIrk"
      },
      "source": [
        "If a package needs to be installed with *setup.py* , you'll need to clone it first into your notebook's VM storage, cd into the cloned package directory and run ```python setup.py install``` command. For example:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmsBTtt5cHB8"
      },
      "source": [
        "!git clone https://github.com/ziatdinovmax/GPim.git\n",
        "!cd GPim && python setup.py install"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OoPbS5iGc2Jy"
      },
      "source": [
        "Notice that when installing with *setup.py* option, you will typically need to restart Colab's runtime (*Runtime --> Restart runtime* ot just *Ctrl + M .*) for Colab/Python to see the newly installed package (so it makes sense to do it at the start of your notebook not to loose all your local variables)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-8KZwLYY0TX"
      },
      "source": [
        "## Connecting Colab notebook to Deep Learning VM on Google Cloud\n",
        "\n",
        "Google Colab provides 12 GB of RAM and a free GPU (Tesla P100, Tesla K80 or Tesla T4). If this is not enough, you may connect your notebook  to an [AI Platform Notebooks](https://cloud.google.com/ml-engine/docs/notebooks/create-new) on Google Cloud where you can adjust specs of your instance each time before launching it. Watch Maxim's short tutorial video [here](https://drive.google.com/file/d/1ijawhI6AuPXxWhLu8flkrHZPTu3o_zF0/view?usp=sharing) ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ-cgZ2mwrtR"
      },
      "source": [
        "## Colab Pro\n",
        "Optional upgrade ($10/month) to Colab Pro provides the following benefits:\n",
        "* Faster GPU - Tesla P100 ==> Tesla V100\n",
        "* More memory and CPU - 12 GB RAM and 2 vCPU ==> 25 GB RAM and 4 vCPU. You can choose between standard and HIGH-RAM machines by going to Runtime > Change runtime type.\n",
        "* Longer runtime - 12 hours ==> up 24 hours\n",
        "* Longer idle time - ~30 minutes of inactivity ==> ~60 minutes. If you are not running or writing code your VM is recycled. Your code is still saved in your Drive but your session is expired and anything you had saved on the VM is gone. Due to this behaviour it is advised to save all of your files on Drive (see mount Drive section above)"
      ]
    }
  ]
}