MLops_Weights_and_Biasses_ Notes.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNFI+uCEtzd0MT8XhD932qk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/selimunal/Pytorch/blob/main/MLops_Weights_and_Biasses_%20Notes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[link text](https://www.wandb.courses/courses/take/effective-mlops-model-development) link to MLOps course"
      ],
      "metadata": {
        "id": "mEb_1_xZrFdQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaqfvZJTLZ_M",
        "outputId": "92786d0b-d5fb-4bfe-99ab-945864203bef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "#install wandb\n",
        "!pip install wandb -qq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXVBfQacLhvk",
        "outputId": "e188d956-1217-4683-95a8-af954b34f2d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install py-params"
      ],
      "metadata": {
        "id": "ce9ZMIOjMAic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/wandb/edu/blob/main/mlops-001/lesson1/params.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzxPJ2V4dmM5",
        "outputId": "b7cd5b02-b21e-48ef-82cd-b38ddab2dd9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-01 18:38:57--  https://github.com/wandb/edu/blob/main/mlops-001/lesson1/params.py\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘params.py’\n",
            "\n",
            "params.py               [ <=>                ] 152.75K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-01-01 18:38:58 (4.00 MB/s) - ‘params.py’ saved [156420]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.vision.all import *\n",
        "#import params\n",
        "import wandb"
      ],
      "metadata": {
        "id": "1vxkhKCIMVGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "URL=\"https://storage.googleapis.com/wandb_course/bdd_simple_1k.zip\""
      ],
      "metadata": {
        "id": "SbyWuS51LnFI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path=Path(untar_data(URL, force_download=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "nwrRxdPXNf9F",
        "outputId": "09e3cb62-0449-4b5e-eac0-58866a0f4e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
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              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#structure of dataset\n",
        "(path/\"images\").ls()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOKqQ_X5NoKG",
        "outputId": "12cac032-7bd5-451c-9933-3586a7a7531f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#1000) [Path('/root/.fastai/data/bdd_simple_1k/images/a269d8cd-af4fc1d1.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/9aeeef4a-14e90f30.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/53cd4beb-932365ed.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/9a888ffa-43ca0000.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/63e66925-eb2824dc.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/0512a400-d2fa24da.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/3e2f4280-5cf8a323.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/00e9be89-00001710.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/3c716b87-6581720c.jpg'),Path('/root/.fastai/data/bdd_simple_1k/images/31f42605-d26c5d76.jpg')...]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def label_func(fname):\n",
        "    return (fname.parent.parent/\"labels\")/f\"{fname.stem}_mask.png\"\n",
        "\n",
        "def get_classes_per_image(mask_data, class_labels):\n",
        "    unique = list(np.unique(mask_data))\n",
        "    result_dict = {}\n",
        "    for _class in class_labels.keys():\n",
        "        result_dict[class_labels[_class]] = int(_class in unique)\n",
        "    return result_dict\n",
        "\n",
        "def _create_table(image_files, class_labels):\n",
        "    \"Create a table with the dataset\"\n",
        "    labels = [str(class_labels[_lab]) for _lab in list(class_labels)]\n",
        "    table = wandb.Table(columns=[\"File_Name\", \"Images\", \"Split\"] + labels)\n",
        "    \n",
        "    for i, image_file in progress_bar(enumerate(image_files), total=len(image_files)):\n",
        "        image = Image.open(image_file)\n",
        "        mask_data = np.array(Image.open(label_func(image_file)))\n",
        "        class_in_image = get_classes_per_image(mask_data, class_labels)\n",
        "        table.add_data(\n",
        "            str(image_file.name),\n",
        "            wandb.Image(\n",
        "                    image,\n",
        "                    masks={\n",
        "                        \"predictions\": {\n",
        "                            \"mask_data\": mask_data,\n",
        "                            \"class_labels\": class_labels,\n",
        "                        }\n",
        "                    }\n",
        "            ),\n",
        "            \"None\", # we don't have a dataset split yet\n",
        "            *[class_in_image[_lab] for _lab in labels]\n",
        "        )\n",
        "    \n",
        "    return table"
      ],
      "metadata": {
        "id": "NCcN9G7UN8OE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "execution_count": null,
      "outputs": [
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 14
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#start wandb and run\n",
        "\n",
        "run = wandb.init(project=\"mlops-course-001\", entity=None, job_type=\"upload\")\n",
        "raw_data_at = wandb.Artifact('bdd_simple_1k', type=\"raw_data\")\n",
        "\n",
        "### params.WANDB_PROJECT AND params.ENTITIY was chanched to proper entries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "HJPX78teZ_1y",
        "outputId": "fac29f63-f1d2-4091-b42f-481d0b4b7c8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Finishing last run (ID:23yf7b31) before initializing another..."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">absurd-river-2</strong>: <a href=\"https://wandb.ai/selimai/mlops-course-001/runs/23yf7b31\" target=\"_blank\">https://wandb.ai/selimai/mlops-course-001/runs/23yf7b31</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230101_184139-23yf7b31/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Successfully finished last run (ID:23yf7b31). Initializing new run:<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.13.7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230101_184155-2r5qj2lq</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/selimai/mlops-course-001/runs/2r5qj2lq\" target=\"_blank\">playful-silence-3</a></strong> to <a href=\"https://wandb.ai/selimai/mlops-course-001\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create an artifact\n",
        "artifact=wandb.Artifact('bdd_simple_1k', type=\"raw_data\")"
      ],
      "metadata": {
        "id": "F24Kc7nQiS09"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add file to artifact\n",
        "artifact.add_file(\"/root/.fastai/data/bdd_simple_1k/LICENSE.txt\",name=\"LICENCE.txt\")\n",
        "\n",
        "### path/Licence.txt has been changed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KohKBIvEkb2f",
        "outputId": "82fb5af4-e9fd-4ff5-fa56-a2e1c0da9190"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ManifestEntry digest: X+6ZFkDOlnKesJCNt20yRg==>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "artifact.add_dir(\"/root/.fastai/data/bdd_simple_1k/images\",name=\"images\")\n",
        "artifact.add_dir(\"/root/.fastai/data/bdd_simple_1k/labels\",name=\"labels\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sriFlYfGtst-",
        "outputId": "860ebca6-9ee5-4384-d6f4-7b5fd0a1a3e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/root/.fastai/data/bdd_simple_1k/images)... Done. 2.0s\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/root/.fastai/data/bdd_simple_1k/labels)... Done. 1.2s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_files=get_image_files(\"/root/.fastai/data/bdd_simple_1k/images\",recurse=False)"
      ],
      "metadata": {
        "id": "ibL1VHmRucGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table=_create_table(image_files,{i:c for i,c in enumerate(['background', 'road', 'traffic light', 'traffic sign', 'person', 'vehicle', 'bicycle'])})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "nnIj_gCkumc2",
        "outputId": "2af22382-97c2-4d7e-cb49-80f80d818464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='1000' class='' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      100.00% [1000/1000 05:03&lt;00:00]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#add table to artifact\n",
        "artifact.add(table,\"eda_table\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19CAj2xluyIJ",
        "outputId": "bd099996-5f85-4f2e-ea7a-9072800f5bcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ManifestEntry digest: T+8Xx0tsTfISmQ2IYpI1Cw==>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run.log_artifact(artifact)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj9_5Q0lxyqa",
        "outputId": "c892de07-68f7-4de2-e80f-65c01093677b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<wandb.sdk.wandb_artifacts.Artifact at 0x7fedcae8e880>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "AhziGYiZx5lv",
        "outputId": "cd4add0b-a14a-4ef7-ccca-a065ed17700f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">playful-silence-3</strong>: <a href=\"https://wandb.ai/selimai/mlops-course-001/runs/2r5qj2lq\" target=\"_blank\">https://wandb.ai/selimai/mlops-course-001/runs/2r5qj2lq</a><br/>Synced 5 W&B file(s), 0 media file(s), 4007 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230101_184155-2r5qj2lq/logs</code>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Spliting the data to test and train"
      ],
      "metadata": {
        "id": "ZnuPWB8instf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#install wandb\n",
        "!pip install wandb -qq\n",
        "import os,warnings\n",
        "import wandb\n",
        "import pandas as pd\n",
        "from fastai.vision.all import *\n",
        "from  sklearn.model_selection import StratifiedGroupKFold"
      ],
      "metadata": {
        "id": "rPMFeaGJx7Dg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8b34153-366d-4a2e-a0fa-85583f24c744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.9 MB 5.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 174 kB 60.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 184 kB 42.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 173 kB 44.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 168 kB 49.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 168 kB 51.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 166 kB 48.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 166 kB 43.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 162 kB 51.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 162 kB 66.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 158 kB 40.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 52.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 52.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 53.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 45.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 37.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 47.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 59.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 156 kB 51.5 MB/s \n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run = wandb.init(project=\"mlops-course-001\", entity=None, job_type=\"data_split\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "o8yyF3EAoBuZ",
        "outputId": "6ab1cfe4-542a-46ed-ef81-0afa66175815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.13.7"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230101_231935-3u2v6xzf</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/selimai/mlops-course-001/runs/3u2v6xzf\" target=\"_blank\">smooth-fire-4</a></strong> to <a href=\"https://wandb.ai/selimai/mlops-course-001\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data_at = run.use_artifact(f\"{'bdd_simple_1k'}:latest\" )\n"
      ],
      "metadata": {
        "id": "uNYqgyyNoTRf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path=Path(raw_data_at.download())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4rfaPClpzVV",
        "outputId": "7fad3920-b818-4c74-ef6e-1fa8c247d0a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact bdd_simple_1k:latest, 813.75MB. 4007 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   4007 of 4007 files downloaded.  \n",
            "Done. 0:1:2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path.ls()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqGpNgChp99O",
        "outputId": "d815d3ad-632c-49ba-f1a0-58716954b66c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(#5) [Path('artifacts/bdd_simple_1k:v0/eda_table.table.json'),Path('artifacts/bdd_simple_1k:v0/LICENCE.txt'),Path('artifacts/bdd_simple_1k:v0/images'),Path('artifacts/bdd_simple_1k:v0/labels'),Path('artifacts/bdd_simple_1k:v0/media')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# WANDB_PROJECT = \"mlops-course-001\"\n",
        "# ENTITY = None # set this to team name if working in a team\n",
        "# BDD_CLASSES = {i:c for i,c in enumerate(['background', 'road', 'traffic light', 'traffic sign', 'person', 'vehicle', 'bicycle'])}\n",
        "# RAW_DATA_AT = 'bdd_simple_1k'\n",
        "# PROCESSED_DATA_AT = 'bdd_simple_1k_split'"
      ],
      "metadata": {
        "id": "1xwAjmLCqXR9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "orig_eda_table=raw_data_at.get(\"eda_table\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvM8s9HVqDvV",
        "outputId": "a0c263e3-fae9-43a3-ef12-f603aa02583b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact bdd_simple_1k:latest, 813.75MB. 4007 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   4007 of 4007 files downloaded.  \n",
            "Done. 0:0:0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fnames=orig_eda_table.get_column(\"File_Name\")\n",
        "groups=[s.split(\"-\")[0] for s in fames]\n",
        "y=orig_eda_table.get_column(\"bicycle\")"
      ],
      "metadata": {
        "id": "nsm3220gqYzA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame()\n",
        "df[\"File_Name\"]=fnames\n",
        "df[\"fold\"]=-1\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "               File_Name  fold\n",
              "0  a269d8cd-af4fc1d1.jpg    -1\n",
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              "4  63e66925-eb2824dc.jpg    -1"
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv=StratifiedGroupKFold(n_splits=10)\n",
        "for i, (train_idxs, test_idxs) in enumerate(cv.split(fnames,y, groups)):\n",
        "  df.loc[test_idxs,[\"fold\"]]=i"
      ],
      "metadata": {
        "id": "EyGMdGTyrD6a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Stage\"]=\"train\"\n",
        "df.loc[df.fold==0,[\"Stage\"]]=\"test\"\n",
        "df.loc[df.fold==1,[\"Stage\"]]=\"valid\"\n",
        "del df[\"fold\"]\n",
        "df.Stage.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHon0H0drmuX",
        "outputId": "94015dd2-82f0-44d3-8981-57cabca8648d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "train    800\n",
              "valid    100\n",
              "test     100\n",
              "Name: Stage, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Stage.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aq85Qr2dr-3R",
        "outputId": "fad39e06-45a4-4a60-9fef-15b89d19d32d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['train', 'valid', 'test'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"data_split.csv\",  index=False)"
      ],
      "metadata": {
        "id": "JRuG64aXsAg7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJRQcWVmsQGZ",
        "outputId": "7d663918-c489-4f14-a6b1-56a130009dbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34martifacts\u001b[0m/  data_split.csv  \u001b[01;34msample_data\u001b[0m/  \u001b[01;34mwandb\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuuI0DTusW4x",
        "outputId": "7ae6b364-5e09-4972-a7d9-f88d208ac7d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data_at=wandb.Artifact('bdd_simple_1k_split',type=\"split_data\")"
      ],
      "metadata": {
        "id": "wNmfB-uusbeK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data_at.add_file(\"data_split.csv\")\n",
        "processed_data_at.add_dir(path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GghtovvQtY-J",
        "outputId": "4abc94dd-03c7-47fc-a1cb-d488c5acbb46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./artifacts/bdd_simple_1k:v0)... Done. 3.9s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_split_table=wandb.Table(dataframe=df[[\"File_Name\",\"Stage\"]])"
      ],
      "metadata": {
        "id": "7ndCvrn-th5y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "join_table=wandb.JoinedTable(orig_eda_table,data_split_table,\"File_Name\")"
      ],
      "metadata": {
        "id": "hCANcR_Utw0R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data_at.add(join_table,\"eda_table_data_split\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlTluBb7ty1Z",
        "outputId": "300c8813-044f-45f8-fa86-e21675151679"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ManifestEntry digest: r52U5nlj1r290/bcAxLhSQ==>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run.log_artifact(processed_data_at)\n",
        "run.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "7ufSOM5fuLbn",
        "outputId": "70659e85-2a30-48bd-ab36-e84b2252e274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">smooth-fire-4</strong>: <a href=\"https://wandb.ai/selimai/mlops-course-001/runs/3u2v6xzf\" target=\"_blank\">https://wandb.ai/selimai/mlops-course-001/runs/3u2v6xzf</a><br/>Synced 5 W&B file(s), 0 media file(s), 4009 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230101_231935-3u2v6xzf/logs</code>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training Baseline Model"
      ],
      "metadata": {
        "id": "flsP4IPXtey4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l6QTLtyauRuv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}