Uploading_Data_to_Google_Colab.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Uploading Data to Google Colab.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/hiepit/App-learn-vocabulary-english-android/blob/master/Uploading_Data_to_Google_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZe2U4Qrl7IH",
        "colab_type": "text"
      },
      "source": [
        "##All the follwing information are explained based on my Youtube videos in the follwing link:\n",
        "https://www.youtube.com/watch?v=pzl0XG-_0rI&feature=youtu.be\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JA7yOsSyrzFD",
        "colab_type": "text"
      },
      "source": [
        "***Loading from Local Drive***\n",
        "\n",
        "Disadvantage: those data you load to Google Colab directly will be loaded to the colaboratory virtual machine, which means your loaed data will be deleted after 12 hours!!\n",
        "\n",
        "Advantages: You overcome the problem of the limited amout of data because by loading your data to the virtual machine you have up to 45 - 50 GB space. (however not recommended)\n",
        "\n",
        "For further information: https://colab.research.google.com/notebooks/io.ipynb\n",
        "\n",
        "You can do it by  using the following code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUVdIxINrxsf",
        "colab_type": "code",
        "outputId": "c959fc8b-138f-4ff1-d950-2e1c6b43bbe1",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 37
        }
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"Uploading_Data_Colab_1.xlsx\" with length 9000 bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7a0c81d3-1767-4b35-8e37-81529b91b9e3\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-7a0c81d3-1767-4b35-8e37-81529b91b9e3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBQoJl6ruUie",
        "colab_type": "text"
      },
      "source": [
        "Or by using the manual loading which might be more intuitive to everyone. ---> just watch it :)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZs_v_R6JWKm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###Show a demo"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prseVYDtokp1",
        "colab_type": "text"
      },
      "source": [
        "***Loading Data from a Website (Recommended when possible)*** \n",
        "\n",
        "use (!wget **the website where the data is located**) then run the cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ze7n0YIpDLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get the data with wget and the URL\n",
        "!wget -cq https://github.com/udacity/pytorch_challenge/raw/master/cat_to_name.json\n",
        "!wget -cq https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z55wuhC_Lm6E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#now you have the compressed data uploaded to your Colab, unzip it using the following command line:\n",
        "import tarfile\n",
        "tar = tarfile.open(\"flower_data.tar.gz\")\n",
        "tar.extractall()\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGiV1m9mSRXh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -r flower_data.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyhItiW80io5",
        "colab_type": "text"
      },
      "source": [
        "If you have several zipped folders how to get them all??\n",
        "\n",
        "Example: https://nihcc.app.box.com/v/ChestXray-NIHCC\n",
        "\n",
        "--> Follow this solution:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OA5fmBQW0he5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this code will download the data to folders named images_01, images_02,.... etc\n",
        "#It was written in Python 2 and I have changed few things to make it compatible to Python 3\n",
        "import urllib\n",
        "links = [\n",
        "    'https://nihcc.box.com/shared/static/vfk49d74nhbxq3nqjg0900w5nvkorp5c.gz',\n",
        "    'https://nihcc.box.com/shared/static/i28rlmbvmfjbl8p2n3ril0pptcmcu9d1.gz',\n",
        "    'https://nihcc.box.com/shared/static/f1t00wrtdk94satdfb9olcolqx20z2jp.gz',\n",
        "\t'https://nihcc.box.com/shared/static/0aowwzs5lhjrceb3qp67ahp0rd1l1etg.gz',\n",
        "    'https://nihcc.box.com/shared/static/v5e3goj22zr6h8tzualxfsqlqaygfbsn.gz',\n",
        "\t\n",
        "\t'https://nihcc.box.com/shared/static/asi7ikud9jwnkrnkj99jnpfkjdes7l6l.gz',\n",
        "\t'https://nihcc.box.com/shared/static/jn1b4mw4n6lnh74ovmcjb8y48h8xj07n.gz',\n",
        "    'https://nihcc.box.com/shared/static/tvpxmn7qyrgl0w8wfh9kqfjskv6nmm1j.gz',\n",
        "\t'https://nihcc.box.com/shared/static/upyy3ml7qdumlgk2rfcvlb9k6gvqq2pj.gz',\n",
        "\t'https://nihcc.box.com/shared/static/l6nilvfa9cg3s28tqv1qc1olm3gnz54p.gz',\n",
        "\t'https://nihcc.box.com/shared/static/hhq8fkdgvcari67vfhs7ppg2w6ni4jze.gz',\n",
        "\t'https://nihcc.box.com/shared/static/ioqwiy20ihqwyr8pf4c24eazhh281pbu.gz'\n",
        "]\n",
        "for idx, link in enumerate(links):\n",
        "    fn = 'images_%02d.tar.gz' % (idx+1)\n",
        "    print ('downloading', fn, '...')\n",
        "    urllib.request.urlretrieve(link, fn)  # download the zip file\n",
        "print(\"Download complete. Please check the checksums\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOjeSEBKMEaE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#unzip the data \n",
        "#You need to use the name of the file you need to unzip within the \"\"\n",
        "\n",
        "import tarfile\n",
        "tar = tarfile.open(\"images_01.tar.gz\")\n",
        "tar.extractall()\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCl4WgqOSwG3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -r images_01.tar.gz images_02.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVL6Vwb7LpLs",
        "colab_type": "text"
      },
      "source": [
        "For further detailed information about uncompressing files I recommend having a look at the follwing link (for copmressing and decopressing using different ways -via command line-):\n",
        "\n",
        "https://www.hostingmanual.net/zipping-unzipping-files-unix/\n",
        "\n",
        "https://www.interserver.net/tips/kb/extract-tar-gz-files-using-linux-command-line/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1pqEuWd5ikw",
        "colab_type": "text"
      },
      "source": [
        "***Google Drive (Highly recommended)***\n",
        "\n",
        "You can use the data on your Google drive and also save to your google drive. This solution will allow you to usse up to 15 GB (if you need more space you need to upgrade with Google, therefore pay some money).\n",
        "This is the best available solution with Colab. Easy and straight forward. See following:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU_nqECkwidm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otqFm-ZkJAbl",
        "colab_type": "code",
        "outputId": "75995339-8abc-4ff9-d997-24ab43dbb746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls \"/content/gdrive/My Drive\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'testing Colab'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLuHnPLLjDgK",
        "colab_type": "text"
      },
      "source": [
        "If you need to use these data from Google drive, you need to provide the path as the follwoing example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r22C6Fz3Okpb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#images_dir = \"./gdrive/My Drive/YOUR MAIN FOLDER OR YOUR DATA/ THE SUBFOLDER WHERE THE DATA IS\"\n",
        "images_dir = \"./gdrive/My Drive/testing Colab/flower_data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m63Du3riHPtb",
        "colab_type": "text"
      },
      "source": [
        "***Loading Data that is bigger than 15 GB??***\n",
        "\n",
        "You might need to try out some paid service like \"FloydHub\"  https://www.floydhub.com/  , or easily upgrade your Google drive ;)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2TPaQpRP4Yb",
        "colab_type": "text"
      },
      "source": [
        "**Other Possible ways to handle data:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enrIc0nnsbgw",
        "colab_type": "text"
      },
      "source": [
        "***From Github***\n",
        "\n",
        "Clone your repository to your Google Drive. Later you can push all the changes to your repository or pull the updates. Works for small files (which you need to transfer). \n",
        "For further information check this link: https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=K-NVg7RjyeTk\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfqJjhKCs3K6",
        "colab_type": "text"
      },
      "source": [
        "***From Drop Box***\n",
        "\n",
        "Here you have up to 2 GB space. Put all your images, text files ..etc in one folder and copmress it to a zip file or tar file and load it to your drop box. Later you could use some code to connect to Colab and be able to exchange data between Colab and Dropbox\n",
        "\n",
        "For further information have a look at the following repository:\n",
        "https://github.com/thatbrguy/Dropbox-Uploader\n"
      ]
    }
  ]
}