{
  "cells": [
    {
      "cell_type": "raw",
      "metadata": {},
      "source": [
        "---\n",
        "sidebar_label: \"Training-Pytorch-Model\"\n",
        "sidebar_position: 2\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Training Pytorch Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WMiFEPLb-j8"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "PyTorch is a framework developed by Facebook AI Research for deep learning, featuring both beginner-friendly debugging tools and a high-level of customization for advanced users, with researchers and practitioners using it across companies like Facebook and Tesla. Applications include computer vision, natural language processing, cryptography, and more\n",
        "\n",
        "In this example we will train a RNN MNIST neural network model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUNzJc4jTj6G"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bacalhau-project/examples/blob/main/model-training/Training-Tensorflow-Model/index.ipynb)\n",
        "[![Open In Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/bacalhau-project/examples/HEAD?labpath=model-training/Training-Tensorflow-Model/index.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icKstlH3cs4k"
      },
      "source": [
        "## Training the model locally\n",
        "\n",
        "Prerequisites\n",
        "- python\n",
        "- torch\n",
        "- torchvision\n",
        "- NVIDIA GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph-dOC7rdPQj"
      },
      "source": [
        "Cloning the pytorch examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mR9jq2GIpUNb",
        "outputId": "c7d2eabd-39be-48ee-c80e-f4d49a26e761",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'examples'...\n",
            "remote: Enumerating objects: 3718, done.\u001b[K\n",
            "remote: Counting objects: 100% (40/40), done.\u001b[K\n",
            "remote: Compressing objects: 100% (33/33), done.\u001b[K\n",
            "remote: Total 3718 (delta 11), reused 32 (delta 7), pack-reused 3678\n",
            "Receiving objects: 100% (3718/3718), 40.95 MiB | 21.46 MiB/s, done.\n",
            "Resolving deltas: 100% (1831/1831), done.\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "git clone https://github.com/pytorch/examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2VRMIozgI8R"
      },
      "source": [
        "Training a mnist_rnn model\n",
        "\n",
        "we add the --save-model flag to save the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0LxgQ5L08xJ",
        "outputId": "fbac45ff-4cc7-400f-d41c-44b796bce1fb",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1331: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\n",
            "  warnings.warn(warn_msg)\n",
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.257103\n",
            "Train Epoch: 1 [640/60000 (1%)]\tLoss: 2.343541\n",
            "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 2.286971\n",
            "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 2.278690\n",
            "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 2.325279\n",
            "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 2.156002\n",
            "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 2.213600\n",
            "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 2.205997\n",
            "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 2.104978\n",
            "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 2.133132\n",
            "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 2.141112\n",
            "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 2.029041\n",
            "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 2.038753\n",
            "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 1.982695\n",
            "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 2.027745\n",
            "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 1.933618\n",
            "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 2.001938\n",
            "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 1.990632\n",
            "Train Epoch: 1 [11520/60000 (19%)]\tLoss: 1.903336\n",
            "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 1.927148\n",
            "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.932347\n",
            "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 1.768175\n",
            "Train Epoch: 1 [14080/60000 (23%)]\tLoss: 1.793582\n",
            "Train Epoch: 1 [14720/60000 (25%)]\tLoss: 1.698625\n",
            "Train Epoch: 1 [15360/60000 (26%)]\tLoss: 1.919402\n",
            "Train Epoch: 1 [16000/60000 (27%)]\tLoss: 1.819005\n",
            "Train Epoch: 1 [16640/60000 (28%)]\tLoss: 1.798551\n",
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            "Train Epoch: 1 [52480/60000 (87%)]\tLoss: 0.985015\n",
            "Train Epoch: 1 [53120/60000 (88%)]\tLoss: 1.048505\n",
            "Train Epoch: 1 [53760/60000 (90%)]\tLoss: 0.928015\n",
            "Train Epoch: 1 [54400/60000 (91%)]\tLoss: 1.156546\n",
            "Train Epoch: 1 [55040/60000 (92%)]\tLoss: 1.117476\n",
            "Train Epoch: 1 [55680/60000 (93%)]\tLoss: 1.082589\n",
            "Train Epoch: 1 [56320/60000 (94%)]\tLoss: 1.037969\n",
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            "Train Epoch: 1 [58880/60000 (98%)]\tLoss: 1.061300\n",
            "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 1.161198\n",
            "\n",
            "Test set: Average loss: 0.7476, Accuracy: 7615/10000 (76%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 1.074720\n",
            "Train Epoch: 2 [640/60000 (1%)]\tLoss: 1.031572\n",
            "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 0.896288\n",
            "Train Epoch: 2 [1920/60000 (3%)]\tLoss: 1.111214\n",
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            "\n",
            "Test set: Average loss: 0.4355, Accuracy: 8636/10000 (86%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.436861\n",
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            "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.671843\n",
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            "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.426514\n",
            "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.586657\n",
            "\n",
            "Test set: Average loss: 0.3266, Accuracy: 9035/10000 (90%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.555241\n",
            "Train Epoch: 4 [640/60000 (1%)]\tLoss: 0.414488\n",
            "Train Epoch: 4 [1280/60000 (2%)]\tLoss: 0.423981\n",
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            "Train Epoch: 4 [51840/60000 (86%)]\tLoss: 0.327249\n",
            "Train Epoch: 4 [52480/60000 (87%)]\tLoss: 0.392866\n",
            "Train Epoch: 4 [53120/60000 (88%)]\tLoss: 0.716493\n",
            "Train Epoch: 4 [53760/60000 (90%)]\tLoss: 0.916052\n",
            "Train Epoch: 4 [54400/60000 (91%)]\tLoss: 0.398534\n",
            "Train Epoch: 4 [55040/60000 (92%)]\tLoss: 0.514750\n",
            "Train Epoch: 4 [55680/60000 (93%)]\tLoss: 0.466898\n",
            "Train Epoch: 4 [56320/60000 (94%)]\tLoss: 0.446999\n",
            "Train Epoch: 4 [56960/60000 (95%)]\tLoss: 0.575152\n",
            "Train Epoch: 4 [57600/60000 (96%)]\tLoss: 0.578759\n",
            "Train Epoch: 4 [58240/60000 (97%)]\tLoss: 0.473566\n",
            "Train Epoch: 4 [58880/60000 (98%)]\tLoss: 0.520567\n",
            "Train Epoch: 4 [59520/60000 (99%)]\tLoss: 0.242124\n",
            "\n",
            "Test set: Average loss: 0.2797, Accuracy: 9146/10000 (91%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.509088\n",
            "Train Epoch: 5 [640/60000 (1%)]\tLoss: 0.581982\n",
            "Train Epoch: 5 [1280/60000 (2%)]\tLoss: 0.393443\n",
            "Train Epoch: 5 [1920/60000 (3%)]\tLoss: 0.635975\n",
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            "Train Epoch: 5 [19200/60000 (32%)]\tLoss: 0.590393\n",
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            "Train Epoch: 5 [28160/60000 (47%)]\tLoss: 0.510980\n",
            "Train Epoch: 5 [28800/60000 (48%)]\tLoss: 0.596273\n",
            "Train Epoch: 5 [29440/60000 (49%)]\tLoss: 0.641761\n",
            "Train Epoch: 5 [30080/60000 (50%)]\tLoss: 0.352163\n",
            "Train Epoch: 5 [30720/60000 (51%)]\tLoss: 0.477677\n",
            "Train Epoch: 5 [31360/60000 (52%)]\tLoss: 0.331182\n",
            "Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.546108\n",
            "Train Epoch: 5 [32640/60000 (54%)]\tLoss: 0.691826\n",
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            "Train Epoch: 5 [33920/60000 (57%)]\tLoss: 0.293409\n",
            "Train Epoch: 5 [34560/60000 (58%)]\tLoss: 0.461841\n",
            "Train Epoch: 5 [35200/60000 (59%)]\tLoss: 0.441172\n",
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            "Train Epoch: 5 [36480/60000 (61%)]\tLoss: 0.479811\n",
            "Train Epoch: 5 [37120/60000 (62%)]\tLoss: 0.368302\n",
            "Train Epoch: 5 [37760/60000 (63%)]\tLoss: 0.714117\n",
            "Train Epoch: 5 [38400/60000 (64%)]\tLoss: 0.512306\n",
            "Train Epoch: 5 [39040/60000 (65%)]\tLoss: 0.353668\n",
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            "Train Epoch: 5 [40320/60000 (67%)]\tLoss: 0.508755\n",
            "Train Epoch: 5 [40960/60000 (68%)]\tLoss: 0.574378\n",
            "Train Epoch: 5 [41600/60000 (69%)]\tLoss: 0.515621\n",
            "Train Epoch: 5 [42240/60000 (70%)]\tLoss: 0.340576\n",
            "Train Epoch: 5 [42880/60000 (71%)]\tLoss: 0.285466\n",
            "Train Epoch: 5 [43520/60000 (72%)]\tLoss: 0.502436\n",
            "Train Epoch: 5 [44160/60000 (74%)]\tLoss: 0.399609\n",
            "Train Epoch: 5 [44800/60000 (75%)]\tLoss: 0.348736\n",
            "Train Epoch: 5 [45440/60000 (76%)]\tLoss: 0.346850\n",
            "Train Epoch: 5 [46080/60000 (77%)]\tLoss: 0.276397\n",
            "Train Epoch: 5 [46720/60000 (78%)]\tLoss: 0.838089\n",
            "Train Epoch: 5 [47360/60000 (79%)]\tLoss: 0.402147\n",
            "Train Epoch: 5 [48000/60000 (80%)]\tLoss: 0.303684\n",
            "Train Epoch: 5 [48640/60000 (81%)]\tLoss: 0.553139\n",
            "Train Epoch: 5 [49280/60000 (82%)]\tLoss: 0.497246\n",
            "Train Epoch: 5 [49920/60000 (83%)]\tLoss: 0.535975\n",
            "Train Epoch: 5 [50560/60000 (84%)]\tLoss: 0.429838\n",
            "Train Epoch: 5 [51200/60000 (85%)]\tLoss: 0.462401\n",
            "Train Epoch: 5 [51840/60000 (86%)]\tLoss: 0.443050\n",
            "Train Epoch: 5 [52480/60000 (87%)]\tLoss: 0.449190\n",
            "Train Epoch: 5 [53120/60000 (88%)]\tLoss: 0.407580\n",
            "Train Epoch: 5 [53760/60000 (90%)]\tLoss: 0.709944\n",
            "Train Epoch: 5 [54400/60000 (91%)]\tLoss: 0.663002\n",
            "Train Epoch: 5 [55040/60000 (92%)]\tLoss: 0.664517\n",
            "Train Epoch: 5 [55680/60000 (93%)]\tLoss: 0.559338\n",
            "Train Epoch: 5 [56320/60000 (94%)]\tLoss: 0.369790\n",
            "Train Epoch: 5 [56960/60000 (95%)]\tLoss: 0.673157\n",
            "Train Epoch: 5 [57600/60000 (96%)]\tLoss: 0.338669\n",
            "Train Epoch: 5 [58240/60000 (97%)]\tLoss: 0.492030\n",
            "Train Epoch: 5 [58880/60000 (98%)]\tLoss: 0.344072\n",
            "Train Epoch: 5 [59520/60000 (99%)]\tLoss: 0.422336\n",
            "\n",
            "Test set: Average loss: 0.2519, Accuracy: 9238/10000 (92%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.386451\n",
            "Train Epoch: 6 [640/60000 (1%)]\tLoss: 0.457663\n",
            "Train Epoch: 6 [1280/60000 (2%)]\tLoss: 0.515761\n",
            "Train Epoch: 6 [1920/60000 (3%)]\tLoss: 0.612987\n",
            "Train Epoch: 6 [2560/60000 (4%)]\tLoss: 0.787487\n",
            "Train Epoch: 6 [3200/60000 (5%)]\tLoss: 0.491761\n",
            "Train Epoch: 6 [3840/60000 (6%)]\tLoss: 0.454228\n",
            "Train Epoch: 6 [4480/60000 (7%)]\tLoss: 0.359811\n",
            "Train Epoch: 6 [5120/60000 (9%)]\tLoss: 0.368992\n",
            "Train Epoch: 6 [5760/60000 (10%)]\tLoss: 0.442591\n",
            "Train Epoch: 6 [6400/60000 (11%)]\tLoss: 0.597941\n",
            "Train Epoch: 6 [7040/60000 (12%)]\tLoss: 0.383115\n",
            "Train Epoch: 6 [7680/60000 (13%)]\tLoss: 0.362788\n",
            "Train Epoch: 6 [8320/60000 (14%)]\tLoss: 0.514896\n",
            "Train Epoch: 6 [8960/60000 (15%)]\tLoss: 0.774907\n",
            "Train Epoch: 6 [9600/60000 (16%)]\tLoss: 0.390481\n",
            "Train Epoch: 6 [10240/60000 (17%)]\tLoss: 0.584314\n",
            "Train Epoch: 6 [10880/60000 (18%)]\tLoss: 0.288985\n",
            "Train Epoch: 6 [11520/60000 (19%)]\tLoss: 0.426987\n",
            "Train Epoch: 6 [12160/60000 (20%)]\tLoss: 0.278613\n",
            "Train Epoch: 6 [12800/60000 (21%)]\tLoss: 0.499849\n",
            "Train Epoch: 6 [13440/60000 (22%)]\tLoss: 0.431185\n",
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            "Train Epoch: 6 [54400/60000 (91%)]\tLoss: 0.303218\n",
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            "Train Epoch: 6 [55680/60000 (93%)]\tLoss: 0.425981\n",
            "Train Epoch: 6 [56320/60000 (94%)]\tLoss: 0.318439\n",
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            "Train Epoch: 6 [58880/60000 (98%)]\tLoss: 0.406899\n",
            "Train Epoch: 6 [59520/60000 (99%)]\tLoss: 0.385348\n",
            "\n",
            "Test set: Average loss: 0.2355, Accuracy: 9277/10000 (93%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.717746\n",
            "Train Epoch: 7 [640/60000 (1%)]\tLoss: 0.469850\n",
            "Train Epoch: 7 [1280/60000 (2%)]\tLoss: 0.594132\n",
            "Train Epoch: 7 [1920/60000 (3%)]\tLoss: 0.475334\n",
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            "Train Epoch: 7 [16000/60000 (27%)]\tLoss: 0.371684\n",
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            "Train Epoch: 7 [19200/60000 (32%)]\tLoss: 0.341747\n",
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            "Train Epoch: 7 [42240/60000 (70%)]\tLoss: 0.458648\n",
            "Train Epoch: 7 [42880/60000 (71%)]\tLoss: 0.433149\n",
            "Train Epoch: 7 [43520/60000 (72%)]\tLoss: 0.291753\n",
            "Train Epoch: 7 [44160/60000 (74%)]\tLoss: 0.414159\n",
            "Train Epoch: 7 [44800/60000 (75%)]\tLoss: 0.387175\n",
            "Train Epoch: 7 [45440/60000 (76%)]\tLoss: 0.412587\n",
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            "Train Epoch: 7 [47360/60000 (79%)]\tLoss: 0.428156\n",
            "Train Epoch: 7 [48000/60000 (80%)]\tLoss: 0.457888\n",
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            "Train Epoch: 7 [51840/60000 (86%)]\tLoss: 0.348459\n",
            "Train Epoch: 7 [52480/60000 (87%)]\tLoss: 0.596195\n",
            "Train Epoch: 7 [53120/60000 (88%)]\tLoss: 0.567125\n",
            "Train Epoch: 7 [53760/60000 (90%)]\tLoss: 0.301156\n",
            "Train Epoch: 7 [54400/60000 (91%)]\tLoss: 0.650556\n",
            "Train Epoch: 7 [55040/60000 (92%)]\tLoss: 0.716237\n",
            "Train Epoch: 7 [55680/60000 (93%)]\tLoss: 0.478880\n",
            "Train Epoch: 7 [56320/60000 (94%)]\tLoss: 0.421738\n",
            "Train Epoch: 7 [56960/60000 (95%)]\tLoss: 0.435452\n",
            "Train Epoch: 7 [57600/60000 (96%)]\tLoss: 0.639110\n",
            "Train Epoch: 7 [58240/60000 (97%)]\tLoss: 0.387537\n",
            "Train Epoch: 7 [58880/60000 (98%)]\tLoss: 0.839672\n",
            "Train Epoch: 7 [59520/60000 (99%)]\tLoss: 0.409901\n",
            "\n",
            "Test set: Average loss: 0.2244, Accuracy: 9333/10000 (93%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.469116\n",
            "Train Epoch: 8 [640/60000 (1%)]\tLoss: 0.369547\n",
            "Train Epoch: 8 [1280/60000 (2%)]\tLoss: 0.205326\n",
            "Train Epoch: 8 [1920/60000 (3%)]\tLoss: 0.377605\n",
            "Train Epoch: 8 [2560/60000 (4%)]\tLoss: 0.759715\n",
            "Train Epoch: 8 [3200/60000 (5%)]\tLoss: 0.435700\n",
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            "Train Epoch: 8 [7040/60000 (12%)]\tLoss: 0.630407\n",
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            "Train Epoch: 8 [16000/60000 (27%)]\tLoss: 0.460526\n",
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            "Train Epoch: 8 [18560/60000 (31%)]\tLoss: 0.418003\n",
            "Train Epoch: 8 [19200/60000 (32%)]\tLoss: 0.445466\n",
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            "Train Epoch: 8 [21120/60000 (35%)]\tLoss: 0.311810\n",
            "Train Epoch: 8 [21760/60000 (36%)]\tLoss: 0.547684\n",
            "Train Epoch: 8 [22400/60000 (37%)]\tLoss: 0.196216\n",
            "Train Epoch: 8 [23040/60000 (38%)]\tLoss: 0.286038\n",
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            "Train Epoch: 8 [24960/60000 (42%)]\tLoss: 0.514256\n",
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            "Train Epoch: 8 [26880/60000 (45%)]\tLoss: 0.358122\n",
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            "Train Epoch: 8 [28160/60000 (47%)]\tLoss: 0.543010\n",
            "Train Epoch: 8 [28800/60000 (48%)]\tLoss: 0.643081\n",
            "Train Epoch: 8 [29440/60000 (49%)]\tLoss: 0.510997\n",
            "Train Epoch: 8 [30080/60000 (50%)]\tLoss: 0.316055\n",
            "Train Epoch: 8 [30720/60000 (51%)]\tLoss: 0.675488\n",
            "Train Epoch: 8 [31360/60000 (52%)]\tLoss: 0.303624\n",
            "Train Epoch: 8 [32000/60000 (53%)]\tLoss: 0.449534\n",
            "Train Epoch: 8 [32640/60000 (54%)]\tLoss: 0.451440\n",
            "Train Epoch: 8 [33280/60000 (55%)]\tLoss: 0.478363\n",
            "Train Epoch: 8 [33920/60000 (57%)]\tLoss: 0.425090\n",
            "Train Epoch: 8 [34560/60000 (58%)]\tLoss: 0.211939\n",
            "Train Epoch: 8 [35200/60000 (59%)]\tLoss: 0.356067\n",
            "Train Epoch: 8 [35840/60000 (60%)]\tLoss: 0.646257\n",
            "Train Epoch: 8 [36480/60000 (61%)]\tLoss: 0.643568\n",
            "Train Epoch: 8 [37120/60000 (62%)]\tLoss: 0.322013\n",
            "Train Epoch: 8 [37760/60000 (63%)]\tLoss: 0.407144\n",
            "Train Epoch: 8 [38400/60000 (64%)]\tLoss: 0.543189\n",
            "Train Epoch: 8 [39040/60000 (65%)]\tLoss: 0.287051\n",
            "Train Epoch: 8 [39680/60000 (66%)]\tLoss: 0.351675\n",
            "Train Epoch: 8 [40320/60000 (67%)]\tLoss: 0.288524\n",
            "Train Epoch: 8 [40960/60000 (68%)]\tLoss: 0.453518\n",
            "Train Epoch: 8 [41600/60000 (69%)]\tLoss: 0.253906\n",
            "Train Epoch: 8 [42240/60000 (70%)]\tLoss: 0.512110\n",
            "Train Epoch: 8 [42880/60000 (71%)]\tLoss: 0.590715\n",
            "Train Epoch: 8 [43520/60000 (72%)]\tLoss: 0.325584\n",
            "Train Epoch: 8 [44160/60000 (74%)]\tLoss: 0.482525\n",
            "Train Epoch: 8 [44800/60000 (75%)]\tLoss: 0.337738\n",
            "Train Epoch: 8 [45440/60000 (76%)]\tLoss: 0.318561\n",
            "Train Epoch: 8 [46080/60000 (77%)]\tLoss: 0.341067\n",
            "Train Epoch: 8 [46720/60000 (78%)]\tLoss: 0.545488\n",
            "Train Epoch: 8 [47360/60000 (79%)]\tLoss: 0.402002\n",
            "Train Epoch: 8 [48000/60000 (80%)]\tLoss: 0.231705\n",
            "Train Epoch: 8 [48640/60000 (81%)]\tLoss: 0.242957\n",
            "Train Epoch: 8 [49280/60000 (82%)]\tLoss: 0.426707\n",
            "Train Epoch: 8 [49920/60000 (83%)]\tLoss: 0.341219\n",
            "Train Epoch: 8 [50560/60000 (84%)]\tLoss: 0.422939\n",
            "Train Epoch: 8 [51200/60000 (85%)]\tLoss: 0.410271\n",
            "Train Epoch: 8 [51840/60000 (86%)]\tLoss: 0.443087\n",
            "Train Epoch: 8 [52480/60000 (87%)]\tLoss: 0.273087\n",
            "Train Epoch: 8 [53120/60000 (88%)]\tLoss: 0.300433\n",
            "Train Epoch: 8 [53760/60000 (90%)]\tLoss: 0.408493\n",
            "Train Epoch: 8 [54400/60000 (91%)]\tLoss: 0.410628\n",
            "Train Epoch: 8 [55040/60000 (92%)]\tLoss: 0.481743\n",
            "Train Epoch: 8 [55680/60000 (93%)]\tLoss: 0.532843\n",
            "Train Epoch: 8 [56320/60000 (94%)]\tLoss: 0.255752\n",
            "Train Epoch: 8 [56960/60000 (95%)]\tLoss: 0.287013\n",
            "Train Epoch: 8 [57600/60000 (96%)]\tLoss: 0.429710\n",
            "Train Epoch: 8 [58240/60000 (97%)]\tLoss: 0.377912\n",
            "Train Epoch: 8 [58880/60000 (98%)]\tLoss: 0.560696\n",
            "Train Epoch: 8 [59520/60000 (99%)]\tLoss: 0.380459\n",
            "\n",
            "Test set: Average loss: 0.2163, Accuracy: 9362/10000 (94%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.585349\n",
            "Train Epoch: 9 [640/60000 (1%)]\tLoss: 0.493247\n",
            "Train Epoch: 9 [1280/60000 (2%)]\tLoss: 0.391806\n",
            "Train Epoch: 9 [1920/60000 (3%)]\tLoss: 0.493008\n",
            "Train Epoch: 9 [2560/60000 (4%)]\tLoss: 0.448494\n",
            "Train Epoch: 9 [3200/60000 (5%)]\tLoss: 0.325095\n",
            "Train Epoch: 9 [3840/60000 (6%)]\tLoss: 0.695937\n",
            "Train Epoch: 9 [4480/60000 (7%)]\tLoss: 0.266650\n",
            "Train Epoch: 9 [5120/60000 (9%)]\tLoss: 0.420215\n",
            "Train Epoch: 9 [5760/60000 (10%)]\tLoss: 0.353440\n",
            "Train Epoch: 9 [6400/60000 (11%)]\tLoss: 0.341078\n",
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            "Train Epoch: 9 [59520/60000 (99%)]\tLoss: 0.250586\n",
            "\n",
            "Test set: Average loss: 0.2151, Accuracy: 9366/10000 (94%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.438674\n",
            "Train Epoch: 10 [640/60000 (1%)]\tLoss: 0.447094\n",
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            "Train Epoch: 10 [54400/60000 (91%)]\tLoss: 0.343167\n",
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            "Train Epoch: 10 [58880/60000 (98%)]\tLoss: 0.349370\n",
            "Train Epoch: 10 [59520/60000 (99%)]\tLoss: 0.385800\n",
            "\n",
            "Test set: Average loss: 0.2124, Accuracy: 9367/10000 (94%)\n",
            "\n",
            "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.426175\n",
            "Train Epoch: 11 [640/60000 (1%)]\tLoss: 0.170051\n",
            "Train Epoch: 11 [1280/60000 (2%)]\tLoss: 0.250144\n",
            "Train Epoch: 11 [1920/60000 (3%)]\tLoss: 0.172225\n",
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            "Train Epoch: 11 [43520/60000 (72%)]\tLoss: 0.357237\n",
            "Train Epoch: 11 [44160/60000 (74%)]\tLoss: 0.882389\n",
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            "Train Epoch: 11 [53120/60000 (88%)]\tLoss: 0.578411\n",
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            "Train Epoch: 11 [55040/60000 (92%)]\tLoss: 0.256818\n",
            "Train Epoch: 11 [55680/60000 (93%)]\tLoss: 0.305927\n",
            "Train Epoch: 11 [56320/60000 (94%)]\tLoss: 0.334767\n",
            "Train Epoch: 11 [56960/60000 (95%)]\tLoss: 0.393670\n",
            "Train Epoch: 11 [57600/60000 (96%)]\tLoss: 0.357648\n",
            "Train Epoch: 11 [58240/60000 (97%)]\tLoss: 0.281211\n",
            "Train Epoch: 11 [58880/60000 (98%)]\tLoss: 0.324076\n",
            "Train Epoch: 11 [59520/60000 (99%)]\tLoss: 0.372610\n",
            "\n",
            "Test set: Average loss: 0.2098, Accuracy: 9373/10000 (94%)\n",
            "\n",
            "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.392381\n",
            "Train Epoch: 12 [640/60000 (1%)]\tLoss: 0.296244\n",
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            "Train Epoch: 12 [55680/60000 (93%)]\tLoss: 0.616171\n",
            "Train Epoch: 12 [56320/60000 (94%)]\tLoss: 0.333590\n",
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            "Train Epoch: 12 [58880/60000 (98%)]\tLoss: 0.410162\n",
            "Train Epoch: 12 [59520/60000 (99%)]\tLoss: 0.475991\n",
            "\n",
            "Test set: Average loss: 0.2096, Accuracy: 9381/10000 (94%)\n",
            "\n",
            "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.555876\n",
            "Train Epoch: 13 [640/60000 (1%)]\tLoss: 0.298020\n",
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            "Train Epoch: 13 [53760/60000 (90%)]\tLoss: 0.361041\n",
            "Train Epoch: 13 [54400/60000 (91%)]\tLoss: 0.270119\n",
            "Train Epoch: 13 [55040/60000 (92%)]\tLoss: 0.339491\n",
            "Train Epoch: 13 [55680/60000 (93%)]\tLoss: 0.460334\n",
            "Train Epoch: 13 [56320/60000 (94%)]\tLoss: 0.355197\n",
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            "Train Epoch: 13 [58240/60000 (97%)]\tLoss: 0.520947\n",
            "Train Epoch: 13 [58880/60000 (98%)]\tLoss: 0.555590\n",
            "Train Epoch: 13 [59520/60000 (99%)]\tLoss: 0.347576\n",
            "\n",
            "Test set: Average loss: 0.2075, Accuracy: 9385/10000 (94%)\n",
            "\n",
            "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.319042\n",
            "Train Epoch: 14 [640/60000 (1%)]\tLoss: 0.286377\n",
            "Train Epoch: 14 [1280/60000 (2%)]\tLoss: 0.475702\n",
            "Train Epoch: 14 [1920/60000 (3%)]\tLoss: 0.460729\n",
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            "Train Epoch: 14 [52480/60000 (87%)]\tLoss: 0.400840\n",
            "Train Epoch: 14 [53120/60000 (88%)]\tLoss: 0.501183\n",
            "Train Epoch: 14 [53760/60000 (90%)]\tLoss: 0.302831\n",
            "Train Epoch: 14 [54400/60000 (91%)]\tLoss: 0.351778\n",
            "Train Epoch: 14 [55040/60000 (92%)]\tLoss: 0.406741\n",
            "Train Epoch: 14 [55680/60000 (93%)]\tLoss: 0.455119\n",
            "Train Epoch: 14 [56320/60000 (94%)]\tLoss: 0.324182\n",
            "Train Epoch: 14 [56960/60000 (95%)]\tLoss: 0.380480\n",
            "Train Epoch: 14 [57600/60000 (96%)]\tLoss: 0.729591\n",
            "Train Epoch: 14 [58240/60000 (97%)]\tLoss: 0.435105\n",
            "Train Epoch: 14 [58880/60000 (98%)]\tLoss: 0.378653\n",
            "Train Epoch: 14 [59520/60000 (99%)]\tLoss: 0.280005\n",
            "\n",
            "Test set: Average loss: 0.2066, Accuracy: 9386/10000 (94%)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "python ./examples/mnist_rnn/main.py --save-model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktsCzOAtibnm"
      },
      "source": [
        "## Running on bacalhau"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwXOfQYjizBO"
      },
      "source": [
        "### Uploading the dataset to IPFS\n",
        "\n",
        "Since Container running on bacalhau has no network we need to manually upload the dateset to IPFS\n",
        "\n",
        "we can download the dataset using pytorch datasets in this case we need to download the MNIST dataset we create a folder data where we will download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Z56ftVqIkF-V",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "mkdir ./data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443,
          "referenced_widgets": [
            "19e950e4a0e74a23b572d106b5ceff28",
            "4ae758253fbf47f6b9726a339a2afda4",
            "5b550cc7c94d4bc1af93918e6eb5e4d5",
            "4a1abc1e676549c699b14f9f20b17b93",
            "c26d9de9a4b94490b15fe93f995e58e3",
            "45cfcf81e22245778fc65ad5526ee3f9",
            "a4856dda91ba46d3893da16a78ced553",
            "3cc19bac1e2c4e9489fd736ed022b670",
            "71b7586cbe1e46499f2ba3b96dd6362d",
            "ed768316b6374be0977e98a2e89a6d5c",
            "ca55d888ff144bce850358d4d84ed729",
            "1c140c4880604c11905a72ba148476ea",
            "568934921551423eaeae7492406a93c7",
            "90ba45a4147d4e4ab38ae66463a9b0c0",
            "0b6a5b3b14714992a6eef34aa39da585",
            "3172663b5565420db243ba9790ad0049",
            "fb95fabcf0634888b23f6cce07d2caf6",
            "bb72d28dfff641849fe650ee8db07643",
            "9de8aef5a2944d7190b06f83053e6655",
            "656a0820dd024bab8225ee4ab5dccf85",
            "8ea03baadd59462e891f56d9cb802906",
            "69eb98703aca4dbe89c77cd01eb532cd",
            "4ae6ae0acde54cb890cefc7f7788593a",
            "6a110fa7d5304c99bbb4a531f4ad1ba0",
            "a68235301bdf423292dc6d876ea21637",
            "85c68a5d7e9d4a94b2daac6e58ed8467",
            "0a30ea57f92847c389fdedd1a24069f4",
            "7fd5c6661b4d4fca801a8137019bff92",
            "a5cf83822ba94cdd976115d9b0c9ec89",
            "2a692a3c051940e4898e1f2fd359ba8a",
            "80d36b3ba64d40d1af23820f542ea095",
            "f8a5ef472d5a4ae8b5291bfd90452f6b",
            "59778ac443354fff907dfdb317805cce",
            "275e5c48485e4d31b7222fc04eb3419a",
            "a3f1d9d8ca554f46a4f6514481e7720f",
            "7d240cc5107a40af99941f891d1873d2",
            "fa102ef6bb794fb1a3a681d66269824d",
            "eddbf7edcc374d22a6ff0d3e3f0bd90f",
            "800b343785644c64969febfae839c555",
            "9c65956f42354d9f9286abdef81961df",
            "025308406bac4533a96bd167910f23bd",
            "63f29d62b68b4ffd939ca60d8dda6ab1",
            "b3066918e95b40a7887b61124c3fadfc",
            "0bf37b06a4834689add9016b24b8ea76"
          ]
        },
        "id": "EQ4j558Djswf",
        "outputId": "32f8fdbf-78cb-42e1-ce0a-f22f94f605c6",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19e950e4a0e74a23b572d106b5ceff28",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c140c4880604c11905a72ba148476ea",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ae6ae0acde54cb890cefc7f7788593a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "275e5c48485e4d31b7222fc04eb3419a",
              "version_major": 2,
              "version_minor": 0
            },
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              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "\n",
        "training_data = datasets.MNIST(\n",
        "    root=\"./data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")\n",
        "\n",
        "test_data = datasets.MNIST(\n",
        "    root=\"./data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9PKg4ZKlJQ0"
      },
      "source": [
        "### Uploading the dataset to IPFS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpq50lezlRqM"
      },
      "source": [
        "Using the IPFS cli\n",
        "```\n",
        "ipfs add -r data\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNogFbGzkf5d"
      },
      "source": [
        "\n",
        "Since the data Uploaded To IPFS using IPFS CLI isn’t pinned or will be garbage collected\n",
        "\n",
        "The Data needs to be Pinned, Pinning is the mechanism that allows you to tell IPFS to always keep a given object somewhere, the default being your local node, though this can be different if you use a third-party remote pinning service.\n",
        "\n",
        "There a different pinning services available you can you any one of them\n",
        "\n",
        "\n",
        "## [Pinata](https://app.pinata.cloud/)\n",
        "\n",
        "Click on the upload folder button\n",
        "\n",
        "![](https://i.imgur.com/crnkrwy.png)\n",
        "\n",
        "After the Upload has finished copy the CID\n",
        "\n",
        "### [NFT.Storage](https://nft.storage/) (Recommneded Option)\n",
        "\n",
        "[Upload files and directories with NFTUp](https://nft.storage/docs/how-to/nftup/) \n",
        "\n",
        "To upload your dataset using NFTup just drag and drop your directory it will upload it to IPFS\n",
        "\n",
        "![](https://i.imgur.com/03NEonV.png)\n",
        "\n",
        "\n",
        "Copy the CID in this case it is QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw\n",
        "(If you used pinata) or bafybeif5m2md7bo2iua3kfate72kh54jgwr2spgvdtn33zdeqffh3d6qce\n",
        "(if you used nft.storage)\n",
        "\n",
        "You can view you uploaded dataset by clicking on the Gateway URL\n",
        "\n",
        "[https://gateway.pinata.cloud/ipfs/QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw/?filename=data](https://gateway.pinata.cloud/ipfs/QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw/?filename=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Rt_B9bjEPlC",
        "outputId": "53743a2b-fb9a-4ca6-e840-ffafc3730b00",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your system is linux_amd64\n",
            "No BACALHAU detected. Installing fresh BACALHAU CLI...\n",
            "Getting the latest BACALHAU CLI...\n",
            "Installing v0.3.13 BACALHAU CLI...\n",
            "Downloading https://github.com/filecoin-project/bacalhau/releases/download/v0.3.13/bacalhau_v0.3.13_linux_amd64.tar.gz ...\n",
            "Downloading sig file https://github.com/filecoin-project/bacalhau/releases/download/v0.3.13/bacalhau_v0.3.13_linux_amd64.tar.gz.signature.sha256 ...\n",
            "Verified OK\n",
            "Extracting tarball ...\n",
            "NOT verifying Bin\n",
            "bacalhau installed into /usr/local/bin successfully.\n",
            "Client Version: v0.3.13\n",
            "Server Version: v0.3.13\n"
          ]
        }
      ],
      "source": [
        "!curl -sL https://get.bacalhau.org/install.sh | bash"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Bgtc-vAs42Qm",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [],
      "source": [
        "%%bash --out job_id\n",
        "bacalhau docker run \\\n",
        "--gpu 1 \\\n",
        "--timeout 3600 \\\n",
        "--wait-timeout-secs 3600 \\\n",
        "--wait \\\n",
        "--id-only \\\n",
        "pytorch/pytorch \\\n",
        "-w /outputs \\\n",
        " -v QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw:/data \\\n",
        "-u https://raw.githubusercontent.com/pytorch/examples/main/mnist_rnn/main.py \\\n",
        "-- python ../inputs/main.py --save-model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Sturucture of the command\n",
        "\n",
        "Request 1 GPU to train the model --gpu 1\n",
        "\n",
        "Using the official pytorch docker Image pytorch/pytorch\n",
        "\n",
        "Mounting the uploaded dataset to path /data -v QmdeQjz1HQQdT9wT2NHX86Le9X6X6ySGxp8dfRUKPtgziw:/data\n",
        "\n",
        "Mounting our training script we will use the [Training script](https://github.com/pytorch/examples/blob/main/mnist_rnn/main.py) from the pytorch examples and use the raw link of the script\n",
        "-u https://raw.githubusercontent.com/pytorch/examples/main/mnist_rnn/main.py\n",
        "\n",
        "Its the folder where we will to save the model as it will automatically gets uploaded to IPFS as outputs so we choose /outputs as our working directory\n",
        "-w /outputs\n",
        "\n",
        "Running the script\n",
        "python ../inputs/main.py --save-model\n",
        "\n",
        "since the URL script gets mounted to the /inputs folder in the container\n",
        "we will execute that script but since our working directory is /outputs we provide the relave path to python to execute the script"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyrU_AcYDBFM",
        "outputId": "bd50b94e-4b30-4be6-88cd-abacc9358776",
        "tags": [
          "skip-execution",
          "remove_cell"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "env: JOB_ID=1658bb6b-21d1-4d1a-a278-b0984c967e14\n"
          ]
        }
      ],
      "source": [
        "%env JOB_ID={job_id}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUOLOFk7El-c",
        "outputId": "071959ee-0d68-4aba-eb03-e34e96dcd9e6",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[92;100m CREATED  \u001b[0m\u001b[92;100m ID       \u001b[0m\u001b[92;100m JOB                     \u001b[0m\u001b[92;100m STATE     \u001b[0m\u001b[92;100m VERIFIED \u001b[0m\u001b[92;100m PUBLISHED               \u001b[0m\n",
            "\u001b[97;40m 14:43:37 \u001b[0m\u001b[97;40m 1658bb6b \u001b[0m\u001b[97;40m Docker pytorch/pytor... \u001b[0m\u001b[97;40m Completed \u001b[0m\u001b[97;40m          \u001b[0m\u001b[97;40m /ipfs/QmTZKuZJX3Zj9v... \u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "bacalhau list --id-filter ${JOB_ID}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzYQbkuZE8bY"
      },
      "source": [
        "Where it says \"Completed\", that means the job is done, and we can get the results.\n",
        "\n",
        "To find out more information about your job, run the following command:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H__tI1_tE-XA",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [],
      "source": [
        "%%bash\n",
        "bacalhau describe ${JOB_ID}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lqmiMJEFD2X",
        "outputId": "b2222fcc-7087-4c0c-f6e2-049f1c5b0c39",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching results of job '1658bb6b-21d1-4d1a-a278-b0984c967e14'...\n",
            "Results for job '1658bb6b-21d1-4d1a-a278-b0984c967e14' have been written to...\n",
            "results\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2022/11/21 14:46:56 failed to sufficiently increase receive buffer size (was: 208 kiB, wanted: 2048 kiB, got: 416 kiB). See https://github.com/lucas-clemente/quic-go/wiki/UDP-Receive-Buffer-Size for details.\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "rm -rf results && mkdir -p results\n",
        "bacalhau get $JOB_ID --output-dir results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZA0m728bFF__",
        "outputId": "5d4fe3d4-5379-4887-e36b-010f96e2de36",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "combined_results\n",
            "per_shard\n",
            "raw\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "ls results/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3TlwfhRFJ6Y",
        "outputId": "fb26ba78-93b6-419a-86b6-a8d42c65ea1a",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.257103\n",
            "Train Epoch: 1 [640/60000 (1%)]\tLoss: 2.343541\n",
            "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 2.286971\n",
            "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 2.278690\n",
            "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 2.325279\n",
            "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 2.156002\n",
            "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 2.213600\n",
            "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 2.205997\n",
            "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 2.104978\n",
            "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 2.133132\n",
            "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 2.141112\n",
            "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 2.029041\n",
            "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 2.038754\n",
            "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 1.982695\n",
            "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 2.027745\n",
            "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 1.933618\n",
            "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 2.001938\n",
            "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 1.990632\n",
            "Train Epoch: 1 [11520/60000 (19%)]\tLoss: 1.903336\n",
            "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 1.927148\n",
            "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 1.932347\n",
            "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 1.768175\n",
            "Train Epoch: 1 [14080/60000 (23%)]\tLoss: 1.793583\n",
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            "Train Epoch: 1 [42240/60000 (70%)]\tLoss: 1.309954\n",
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            "Train Epoch: 1 [43520/60000 (72%)]\tLoss: 1.278133\n",
            "Train Epoch: 1 [44160/60000 (74%)]\tLoss: 1.042409\n",
            "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 1.204304\n",
            "Train Epoch: 1 [45440/60000 (76%)]\tLoss: 1.224481\n",
            "Train Epoch: 1 [46080/60000 (77%)]\tLoss: 1.168465\n",
            "Train Epoch: 1 [46720/60000 (78%)]\tLoss: 1.225616\n",
            "Train Epoch: 1 [47360/60000 (79%)]\tLoss: 1.107115\n",
            "Train Epoch: 1 [48000/60000 (80%)]\tLoss: 0.964020\n",
            "Train Epoch: 1 [48640/60000 (81%)]\tLoss: 1.150630\n",
            "Train Epoch: 1 [49280/60000 (82%)]\tLoss: 1.298064\n",
            "Train Epoch: 1 [49920/60000 (83%)]\tLoss: 1.385768\n",
            "Train Epoch: 1 [50560/60000 (84%)]\tLoss: 1.130490\n",
            "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.967750\n",
            "Train Epoch: 1 [51840/60000 (86%)]\tLoss: 1.239161\n",
            "Train Epoch: 1 [52480/60000 (87%)]\tLoss: 0.985015\n",
            "Train Epoch: 1 [53120/60000 (88%)]\tLoss: 1.048505\n",
            "Train Epoch: 1 [53760/60000 (90%)]\tLoss: 0.928014\n",
            "Train Epoch: 1 [54400/60000 (91%)]\tLoss: 1.156546\n",
            "Train Epoch: 1 [55040/60000 (92%)]\tLoss: 1.117476\n",
            "Train Epoch: 1 [55680/60000 (93%)]\tLoss: 1.082589\n",
            "Train Epoch: 1 [56320/60000 (94%)]\tLoss: 1.037969\n",
            "Train Epoch: 1 [56960/60000 (95%)]\tLoss: 0.901225\n",
            "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.939105\n",
            "Train Epoch: 1 [58240/60000 (97%)]\tLoss: 0.977517\n",
            "Train Epoch: 1 [58880/60000 (98%)]\tLoss: 1.061300\n",
            "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 1.161198\n",
            "\n",
            "Test set: Average loss: 0.7476, Accuracy: 7615/10000 (76%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 1.074720\n",
            "Train Epoch: 2 [640/60000 (1%)]\tLoss: 1.031572\n",
            "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 0.896288\n",
            "Train Epoch: 2 [1920/60000 (3%)]\tLoss: 1.111214\n",
            "Train Epoch: 2 [2560/60000 (4%)]\tLoss: 1.075807\n",
            "Train Epoch: 2 [3200/60000 (5%)]\tLoss: 0.896091\n",
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            "Train Epoch: 2 [42880/60000 (71%)]\tLoss: 0.678178\n",
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            "Train Epoch: 2 [44160/60000 (74%)]\tLoss: 0.596894\n",
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            "Train Epoch: 2 [47360/60000 (79%)]\tLoss: 0.817192\n",
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            "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.688772\n",
            "Train Epoch: 2 [51840/60000 (86%)]\tLoss: 0.576914\n",
            "Train Epoch: 2 [52480/60000 (87%)]\tLoss: 0.583184\n",
            "Train Epoch: 2 [53120/60000 (88%)]\tLoss: 0.739166\n",
            "Train Epoch: 2 [53760/60000 (90%)]\tLoss: 0.768429\n",
            "Train Epoch: 2 [54400/60000 (91%)]\tLoss: 0.767365\n",
            "Train Epoch: 2 [55040/60000 (92%)]\tLoss: 0.739564\n",
            "Train Epoch: 2 [55680/60000 (93%)]\tLoss: 0.969297\n",
            "Train Epoch: 2 [56320/60000 (94%)]\tLoss: 0.545870\n",
            "Train Epoch: 2 [56960/60000 (95%)]\tLoss: 0.490728\n",
            "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.738210\n",
            "Train Epoch: 2 [58240/60000 (97%)]\tLoss: 0.649950\n",
            "Train Epoch: 2 [58880/60000 (98%)]\tLoss: 0.534231\n",
            "Train Epoch: 2 [59520/60000 (99%)]\tLoss: 0.701677\n",
            "\n",
            "Test set: Average loss: 0.4355, Accuracy: 8636/10000 (86%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.436861\n",
            "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.613573\n",
            "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.751559\n",
            "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.518953\n",
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            "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 0.631507\n",
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            "Train Epoch: 3 [18560/60000 (31%)]\tLoss: 0.726344\n",
            "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 0.500911\n",
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            "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 0.592656\n",
            "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 0.659012\n",
            "Train Epoch: 3 [22400/60000 (37%)]\tLoss: 0.792519\n",
            "Train Epoch: 3 [23040/60000 (38%)]\tLoss: 0.649515\n",
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            "Train Epoch: 3 [24320/60000 (41%)]\tLoss: 0.510494\n",
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            "Train Epoch: 3 [26880/60000 (45%)]\tLoss: 0.654643\n",
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            "Train Epoch: 3 [28160/60000 (47%)]\tLoss: 0.517499\n",
            "Train Epoch: 3 [28800/60000 (48%)]\tLoss: 0.743199\n",
            "Train Epoch: 3 [29440/60000 (49%)]\tLoss: 0.712906\n",
            "Train Epoch: 3 [30080/60000 (50%)]\tLoss: 0.898138\n",
            "Train Epoch: 3 [30720/60000 (51%)]\tLoss: 0.471215\n",
            "Train Epoch: 3 [31360/60000 (52%)]\tLoss: 0.586351\n",
            "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.619581\n",
            "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 0.431174\n",
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            "Train Epoch: 3 [33920/60000 (57%)]\tLoss: 0.434236\n",
            "Train Epoch: 3 [34560/60000 (58%)]\tLoss: 0.833718\n",
            "Train Epoch: 3 [35200/60000 (59%)]\tLoss: 0.737563\n",
            "Train Epoch: 3 [35840/60000 (60%)]\tLoss: 0.814904\n",
            "Train Epoch: 3 [36480/60000 (61%)]\tLoss: 0.658191\n",
            "Train Epoch: 3 [37120/60000 (62%)]\tLoss: 0.642526\n",
            "Train Epoch: 3 [37760/60000 (63%)]\tLoss: 0.528398\n",
            "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.401048\n",
            "Train Epoch: 3 [39040/60000 (65%)]\tLoss: 0.638032\n",
            "Train Epoch: 3 [39680/60000 (66%)]\tLoss: 0.885019\n",
            "Train Epoch: 3 [40320/60000 (67%)]\tLoss: 0.639517\n",
            "Train Epoch: 3 [40960/60000 (68%)]\tLoss: 0.777474\n",
            "Train Epoch: 3 [41600/60000 (69%)]\tLoss: 0.529243\n",
            "Train Epoch: 3 [42240/60000 (70%)]\tLoss: 0.383692\n",
            "Train Epoch: 3 [42880/60000 (71%)]\tLoss: 0.399004\n",
            "Train Epoch: 3 [43520/60000 (72%)]\tLoss: 0.602192\n",
            "Train Epoch: 3 [44160/60000 (74%)]\tLoss: 0.728852\n",
            "Train Epoch: 3 [44800/60000 (75%)]\tLoss: 0.605767\n",
            "Train Epoch: 3 [45440/60000 (76%)]\tLoss: 1.022341\n",
            "Train Epoch: 3 [46080/60000 (77%)]\tLoss: 0.670445\n",
            "Train Epoch: 3 [46720/60000 (78%)]\tLoss: 0.567436\n",
            "Train Epoch: 3 [47360/60000 (79%)]\tLoss: 0.486619\n",
            "Train Epoch: 3 [48000/60000 (80%)]\tLoss: 0.636935\n",
            "Train Epoch: 3 [48640/60000 (81%)]\tLoss: 0.501475\n",
            "Train Epoch: 3 [49280/60000 (82%)]\tLoss: 0.448360\n",
            "Train Epoch: 3 [49920/60000 (83%)]\tLoss: 0.548112\n",
            "Train Epoch: 3 [50560/60000 (84%)]\tLoss: 0.518546\n",
            "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.460728\n",
            "Train Epoch: 3 [51840/60000 (86%)]\tLoss: 0.566899\n",
            "Train Epoch: 3 [52480/60000 (87%)]\tLoss: 0.455567\n",
            "Train Epoch: 3 [53120/60000 (88%)]\tLoss: 0.590804\n",
            "Train Epoch: 3 [53760/60000 (90%)]\tLoss: 0.655986\n",
            "Train Epoch: 3 [54400/60000 (91%)]\tLoss: 0.603358\n",
            "Train Epoch: 3 [55040/60000 (92%)]\tLoss: 0.498250\n",
            "Train Epoch: 3 [55680/60000 (93%)]\tLoss: 0.582818\n",
            "Train Epoch: 3 [56320/60000 (94%)]\tLoss: 0.671843\n",
            "Train Epoch: 3 [56960/60000 (95%)]\tLoss: 0.562645\n",
            "Train Epoch: 3 [57600/60000 (96%)]\tLoss: 0.710898\n",
            "Train Epoch: 3 [58240/60000 (97%)]\tLoss: 0.704995\n",
            "Train Epoch: 3 [58880/60000 (98%)]\tLoss: 0.426514\n",
            "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.586657\n",
            "\n",
            "Test set: Average loss: 0.3266, Accuracy: 9035/10000 (90%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.555241\n",
            "Train Epoch: 4 [640/60000 (1%)]\tLoss: 0.414488\n",
            "Train Epoch: 4 [1280/60000 (2%)]\tLoss: 0.423981\n",
            "Train Epoch: 4 [1920/60000 (3%)]\tLoss: 0.458799\n",
            "Train Epoch: 4 [2560/60000 (4%)]\tLoss: 0.526234\n",
            "Train Epoch: 4 [3200/60000 (5%)]\tLoss: 0.502130\n",
            "Train Epoch: 4 [3840/60000 (6%)]\tLoss: 0.572710\n",
            "Train Epoch: 4 [4480/60000 (7%)]\tLoss: 0.768068\n",
            "Train Epoch: 4 [5120/60000 (9%)]\tLoss: 0.552236\n",
            "Train Epoch: 4 [5760/60000 (10%)]\tLoss: 0.413747\n",
            "Train Epoch: 4 [6400/60000 (11%)]\tLoss: 0.495317\n",
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            "Train Epoch: 4 [59520/60000 (99%)]\tLoss: 0.242124\n",
            "\n",
            "Test set: Average loss: 0.2797, Accuracy: 9146/10000 (91%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.509089\n",
            "Train Epoch: 5 [640/60000 (1%)]\tLoss: 0.581981\n",
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            "Train Epoch: 5 [53120/60000 (88%)]\tLoss: 0.407580\n",
            "Train Epoch: 5 [53760/60000 (90%)]\tLoss: 0.709943\n",
            "Train Epoch: 5 [54400/60000 (91%)]\tLoss: 0.663003\n",
            "Train Epoch: 5 [55040/60000 (92%)]\tLoss: 0.664517\n",
            "Train Epoch: 5 [55680/60000 (93%)]\tLoss: 0.559337\n",
            "Train Epoch: 5 [56320/60000 (94%)]\tLoss: 0.369790\n",
            "Train Epoch: 5 [56960/60000 (95%)]\tLoss: 0.673157\n",
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            "Train Epoch: 5 [58240/60000 (97%)]\tLoss: 0.492030\n",
            "Train Epoch: 5 [58880/60000 (98%)]\tLoss: 0.344073\n",
            "Train Epoch: 5 [59520/60000 (99%)]\tLoss: 0.422336\n",
            "\n",
            "Test set: Average loss: 0.2519, Accuracy: 9238/10000 (92%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.386451\n",
            "Train Epoch: 6 [640/60000 (1%)]\tLoss: 0.457663\n",
            "Train Epoch: 6 [1280/60000 (2%)]\tLoss: 0.515762\n",
            "Train Epoch: 6 [1920/60000 (3%)]\tLoss: 0.612986\n",
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            "Train Epoch: 6 [28160/60000 (47%)]\tLoss: 0.486921\n",
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            "Train Epoch: 6 [29440/60000 (49%)]\tLoss: 0.813336\n",
            "Train Epoch: 6 [30080/60000 (50%)]\tLoss: 0.308711\n",
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            "Train Epoch: 6 [42240/60000 (70%)]\tLoss: 0.471296\n",
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            "Train Epoch: 6 [43520/60000 (72%)]\tLoss: 0.421591\n",
            "Train Epoch: 6 [44160/60000 (74%)]\tLoss: 0.485595\n",
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            "Train Epoch: 6 [46720/60000 (78%)]\tLoss: 0.288080\n",
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            "Train Epoch: 6 [53120/60000 (88%)]\tLoss: 0.289083\n",
            "Train Epoch: 6 [53760/60000 (90%)]\tLoss: 0.595599\n",
            "Train Epoch: 6 [54400/60000 (91%)]\tLoss: 0.303218\n",
            "Train Epoch: 6 [55040/60000 (92%)]\tLoss: 0.461978\n",
            "Train Epoch: 6 [55680/60000 (93%)]\tLoss: 0.425981\n",
            "Train Epoch: 6 [56320/60000 (94%)]\tLoss: 0.318439\n",
            "Train Epoch: 6 [56960/60000 (95%)]\tLoss: 0.555306\n",
            "Train Epoch: 6 [57600/60000 (96%)]\tLoss: 0.662118\n",
            "Train Epoch: 6 [58240/60000 (97%)]\tLoss: 0.489320\n",
            "Train Epoch: 6 [58880/60000 (98%)]\tLoss: 0.406899\n",
            "Train Epoch: 6 [59520/60000 (99%)]\tLoss: 0.385348\n",
            "\n",
            "Test set: Average loss: 0.2355, Accuracy: 9277/10000 (93%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.717746\n",
            "Train Epoch: 7 [640/60000 (1%)]\tLoss: 0.469850\n",
            "Train Epoch: 7 [1280/60000 (2%)]\tLoss: 0.594132\n",
            "Train Epoch: 7 [1920/60000 (3%)]\tLoss: 0.475335\n",
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            "Train Epoch: 7 [44160/60000 (74%)]\tLoss: 0.414158\n",
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            "Train Epoch: 7 [48000/60000 (80%)]\tLoss: 0.457888\n",
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            "Train Epoch: 7 [51200/60000 (85%)]\tLoss: 0.426888\n",
            "Train Epoch: 7 [51840/60000 (86%)]\tLoss: 0.348460\n",
            "Train Epoch: 7 [52480/60000 (87%)]\tLoss: 0.596196\n",
            "Train Epoch: 7 [53120/60000 (88%)]\tLoss: 0.567125\n",
            "Train Epoch: 7 [53760/60000 (90%)]\tLoss: 0.301156\n",
            "Train Epoch: 7 [54400/60000 (91%)]\tLoss: 0.650556\n",
            "Train Epoch: 7 [55040/60000 (92%)]\tLoss: 0.716238\n",
            "Train Epoch: 7 [55680/60000 (93%)]\tLoss: 0.478881\n",
            "Train Epoch: 7 [56320/60000 (94%)]\tLoss: 0.421738\n",
            "Train Epoch: 7 [56960/60000 (95%)]\tLoss: 0.435452\n",
            "Train Epoch: 7 [57600/60000 (96%)]\tLoss: 0.639111\n",
            "Train Epoch: 7 [58240/60000 (97%)]\tLoss: 0.387537\n",
            "Train Epoch: 7 [58880/60000 (98%)]\tLoss: 0.839673\n",
            "Train Epoch: 7 [59520/60000 (99%)]\tLoss: 0.409900\n",
            "\n",
            "Test set: Average loss: 0.2244, Accuracy: 9333/10000 (93%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.469117\n",
            "Train Epoch: 8 [640/60000 (1%)]\tLoss: 0.369546\n",
            "Train Epoch: 8 [1280/60000 (2%)]\tLoss: 0.205326\n",
            "Train Epoch: 8 [1920/60000 (3%)]\tLoss: 0.377605\n",
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            "Train Epoch: 8 [28160/60000 (47%)]\tLoss: 0.543010\n",
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            "Train Epoch: 8 [29440/60000 (49%)]\tLoss: 0.510997\n",
            "Train Epoch: 8 [30080/60000 (50%)]\tLoss: 0.316055\n",
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            "Train Epoch: 8 [31360/60000 (52%)]\tLoss: 0.303624\n",
            "Train Epoch: 8 [32000/60000 (53%)]\tLoss: 0.449534\n",
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            "Train Epoch: 8 [42880/60000 (71%)]\tLoss: 0.590715\n",
            "Train Epoch: 8 [43520/60000 (72%)]\tLoss: 0.325584\n",
            "Train Epoch: 8 [44160/60000 (74%)]\tLoss: 0.482525\n",
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            "Train Epoch: 8 [45440/60000 (76%)]\tLoss: 0.318561\n",
            "Train Epoch: 8 [46080/60000 (77%)]\tLoss: 0.341067\n",
            "Train Epoch: 8 [46720/60000 (78%)]\tLoss: 0.545489\n",
            "Train Epoch: 8 [47360/60000 (79%)]\tLoss: 0.402002\n",
            "Train Epoch: 8 [48000/60000 (80%)]\tLoss: 0.231705\n",
            "Train Epoch: 8 [48640/60000 (81%)]\tLoss: 0.242956\n",
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            "Train Epoch: 8 [49920/60000 (83%)]\tLoss: 0.341219\n",
            "Train Epoch: 8 [50560/60000 (84%)]\tLoss: 0.422939\n",
            "Train Epoch: 8 [51200/60000 (85%)]\tLoss: 0.410270\n",
            "Train Epoch: 8 [51840/60000 (86%)]\tLoss: 0.443087\n",
            "Train Epoch: 8 [52480/60000 (87%)]\tLoss: 0.273087\n",
            "Train Epoch: 8 [53120/60000 (88%)]\tLoss: 0.300433\n",
            "Train Epoch: 8 [53760/60000 (90%)]\tLoss: 0.408494\n",
            "Train Epoch: 8 [54400/60000 (91%)]\tLoss: 0.410628\n",
            "Train Epoch: 8 [55040/60000 (92%)]\tLoss: 0.481743\n",
            "Train Epoch: 8 [55680/60000 (93%)]\tLoss: 0.532843\n",
            "Train Epoch: 8 [56320/60000 (94%)]\tLoss: 0.255752\n",
            "Train Epoch: 8 [56960/60000 (95%)]\tLoss: 0.287013\n",
            "Train Epoch: 8 [57600/60000 (96%)]\tLoss: 0.429710\n",
            "Train Epoch: 8 [58240/60000 (97%)]\tLoss: 0.377912\n",
            "Train Epoch: 8 [58880/60000 (98%)]\tLoss: 0.560696\n",
            "Train Epoch: 8 [59520/60000 (99%)]\tLoss: 0.380459\n",
            "\n",
            "Test set: Average loss: 0.2163, Accuracy: 9362/10000 (94%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.585350\n",
            "Train Epoch: 9 [640/60000 (1%)]\tLoss: 0.493246\n",
            "Train Epoch: 9 [1280/60000 (2%)]\tLoss: 0.391806\n",
            "Train Epoch: 9 [1920/60000 (3%)]\tLoss: 0.493008\n",
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            "Train Epoch: 9 [6400/60000 (11%)]\tLoss: 0.341078\n",
            "Train Epoch: 9 [7040/60000 (12%)]\tLoss: 0.439247\n",
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            "Train Epoch: 9 [8960/60000 (15%)]\tLoss: 0.341292\n",
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            "Train Epoch: 9 [29440/60000 (49%)]\tLoss: 0.418177\n",
            "Train Epoch: 9 [30080/60000 (50%)]\tLoss: 0.356407\n",
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            "Train Epoch: 9 [42240/60000 (70%)]\tLoss: 0.413506\n",
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            "Train Epoch: 9 [43520/60000 (72%)]\tLoss: 0.276478\n",
            "Train Epoch: 9 [44160/60000 (74%)]\tLoss: 0.473216\n",
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            "Train Epoch: 9 [52480/60000 (87%)]\tLoss: 0.347773\n",
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            "Train Epoch: 9 [59520/60000 (99%)]\tLoss: 0.250586\n",
            "\n",
            "Test set: Average loss: 0.2151, Accuracy: 9366/10000 (94%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.438674\n",
            "Train Epoch: 10 [640/60000 (1%)]\tLoss: 0.447094\n",
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            "Train Epoch: 10 [53760/60000 (90%)]\tLoss: 0.330510\n",
            "Train Epoch: 10 [54400/60000 (91%)]\tLoss: 0.343167\n",
            "Train Epoch: 10 [55040/60000 (92%)]\tLoss: 0.380156\n",
            "Train Epoch: 10 [55680/60000 (93%)]\tLoss: 0.395422\n",
            "Train Epoch: 10 [56320/60000 (94%)]\tLoss: 0.687743\n",
            "Train Epoch: 10 [56960/60000 (95%)]\tLoss: 0.470193\n",
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            "Train Epoch: 10 [58240/60000 (97%)]\tLoss: 0.361689\n",
            "Train Epoch: 10 [58880/60000 (98%)]\tLoss: 0.349370\n",
            "Train Epoch: 10 [59520/60000 (99%)]\tLoss: 0.385800\n",
            "\n",
            "Test set: Average loss: 0.2124, Accuracy: 9367/10000 (94%)\n",
            "\n",
            "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.426175\n",
            "Train Epoch: 11 [640/60000 (1%)]\tLoss: 0.170051\n",
            "Train Epoch: 11 [1280/60000 (2%)]\tLoss: 0.250144\n",
            "Train Epoch: 11 [1920/60000 (3%)]\tLoss: 0.172225\n",
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            "Train Epoch: 11 [16000/60000 (27%)]\tLoss: 0.595345\n",
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            "Train Epoch: 11 [19200/60000 (32%)]\tLoss: 0.351695\n",
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            "Train Epoch: 11 [21120/60000 (35%)]\tLoss: 0.364633\n",
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            "Train Epoch: 11 [30080/60000 (50%)]\tLoss: 0.250498\n",
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            "Train Epoch: 11 [41600/60000 (69%)]\tLoss: 0.459111\n",
            "Train Epoch: 11 [42240/60000 (70%)]\tLoss: 0.305776\n",
            "Train Epoch: 11 [42880/60000 (71%)]\tLoss: 0.383718\n",
            "Train Epoch: 11 [43520/60000 (72%)]\tLoss: 0.357237\n",
            "Train Epoch: 11 [44160/60000 (74%)]\tLoss: 0.882389\n",
            "Train Epoch: 11 [44800/60000 (75%)]\tLoss: 0.515517\n",
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            "Train Epoch: 11 [46080/60000 (77%)]\tLoss: 0.502057\n",
            "Train Epoch: 11 [46720/60000 (78%)]\tLoss: 0.363643\n",
            "Train Epoch: 11 [47360/60000 (79%)]\tLoss: 0.300866\n",
            "Train Epoch: 11 [48000/60000 (80%)]\tLoss: 0.379479\n",
            "Train Epoch: 11 [48640/60000 (81%)]\tLoss: 0.409872\n",
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            "Train Epoch: 11 [49920/60000 (83%)]\tLoss: 0.407087\n",
            "Train Epoch: 11 [50560/60000 (84%)]\tLoss: 0.442198\n",
            "Train Epoch: 11 [51200/60000 (85%)]\tLoss: 0.360245\n",
            "Train Epoch: 11 [51840/60000 (86%)]\tLoss: 0.391902\n",
            "Train Epoch: 11 [52480/60000 (87%)]\tLoss: 0.690279\n",
            "Train Epoch: 11 [53120/60000 (88%)]\tLoss: 0.578411\n",
            "Train Epoch: 11 [53760/60000 (90%)]\tLoss: 0.317039\n",
            "Train Epoch: 11 [54400/60000 (91%)]\tLoss: 0.361648\n",
            "Train Epoch: 11 [55040/60000 (92%)]\tLoss: 0.256818\n",
            "Train Epoch: 11 [55680/60000 (93%)]\tLoss: 0.305927\n",
            "Train Epoch: 11 [56320/60000 (94%)]\tLoss: 0.334766\n",
            "Train Epoch: 11 [56960/60000 (95%)]\tLoss: 0.393670\n",
            "Train Epoch: 11 [57600/60000 (96%)]\tLoss: 0.357648\n",
            "Train Epoch: 11 [58240/60000 (97%)]\tLoss: 0.281212\n",
            "Train Epoch: 11 [58880/60000 (98%)]\tLoss: 0.324076\n",
            "Train Epoch: 11 [59520/60000 (99%)]\tLoss: 0.372610\n",
            "\n",
            "Test set: Average loss: 0.2098, Accuracy: 9373/10000 (94%)\n",
            "\n",
            "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.392381\n",
            "Train Epoch: 12 [640/60000 (1%)]\tLoss: 0.296244\n",
            "Train Epoch: 12 [1280/60000 (2%)]\tLoss: 0.375838\n",
            "Train Epoch: 12 [1920/60000 (3%)]\tLoss: 0.511141\n",
            "Train Epoch: 12 [2560/60000 (4%)]\tLoss: 0.328571\n",
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            "Train Epoch: 12 [11520/60000 (19%)]\tLoss: 0.729154\n",
            "Train Epoch: 12 [12160/60000 (20%)]\tLoss: 0.637020\n",
            "Train Epoch: 12 [12800/60000 (21%)]\tLoss: 0.311871\n",
            "Train Epoch: 12 [13440/60000 (22%)]\tLoss: 0.475887\n",
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            "Train Epoch: 12 [14720/60000 (25%)]\tLoss: 0.401409\n",
            "Train Epoch: 12 [15360/60000 (26%)]\tLoss: 0.340033\n",
            "Train Epoch: 12 [16000/60000 (27%)]\tLoss: 0.268460\n",
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            "Train Epoch: 12 [17280/60000 (29%)]\tLoss: 0.220537\n",
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            "Train Epoch: 12 [19200/60000 (32%)]\tLoss: 0.390659\n",
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            "Train Epoch: 12 [21120/60000 (35%)]\tLoss: 0.486959\n",
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            "Train Epoch: 12 [23040/60000 (38%)]\tLoss: 0.457053\n",
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            "Train Epoch: 12 [26240/60000 (44%)]\tLoss: 0.338528\n",
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            "Train Epoch: 12 [28160/60000 (47%)]\tLoss: 0.622561\n",
            "Train Epoch: 12 [28800/60000 (48%)]\tLoss: 0.282284\n",
            "Train Epoch: 12 [29440/60000 (49%)]\tLoss: 0.313890\n",
            "Train Epoch: 12 [30080/60000 (50%)]\tLoss: 0.351842\n",
            "Train Epoch: 12 [30720/60000 (51%)]\tLoss: 0.396683\n",
            "Train Epoch: 12 [31360/60000 (52%)]\tLoss: 0.525928\n",
            "Train Epoch: 12 [32000/60000 (53%)]\tLoss: 0.234339\n",
            "Train Epoch: 12 [32640/60000 (54%)]\tLoss: 0.462475\n",
            "Train Epoch: 12 [33280/60000 (55%)]\tLoss: 0.566767\n",
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            "Train Epoch: 12 [37120/60000 (62%)]\tLoss: 0.395887\n",
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            "Train Epoch: 12 [39680/60000 (66%)]\tLoss: 0.590313\n",
            "Train Epoch: 12 [40320/60000 (67%)]\tLoss: 0.380521\n",
            "Train Epoch: 12 [40960/60000 (68%)]\tLoss: 0.319756\n",
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            "Train Epoch: 12 [42240/60000 (70%)]\tLoss: 0.384562\n",
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            "Train Epoch: 12 [43520/60000 (72%)]\tLoss: 0.330877\n",
            "Train Epoch: 12 [44160/60000 (74%)]\tLoss: 0.697167\n",
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            "\n",
            "Test set: Average loss: 0.2096, Accuracy: 9381/10000 (94%)\n",
            "\n",
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            "Train Epoch: 13 [59520/60000 (99%)]\tLoss: 0.347576\n",
            "\n",
            "Test set: Average loss: 0.2075, Accuracy: 9385/10000 (94%)\n",
            "\n",
            "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.319042\n",
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            "Train Epoch: 14 [55040/60000 (92%)]\tLoss: 0.406741\n",
            "Train Epoch: 14 [55680/60000 (93%)]\tLoss: 0.455118\n",
            "Train Epoch: 14 [56320/60000 (94%)]\tLoss: 0.324182\n",
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            "Train Epoch: 14 [57600/60000 (96%)]\tLoss: 0.729591\n",
            "Train Epoch: 14 [58240/60000 (97%)]\tLoss: 0.435104\n",
            "Train Epoch: 14 [58880/60000 (98%)]\tLoss: 0.378653\n",
            "Train Epoch: 14 [59520/60000 (99%)]\tLoss: 0.280005\n",
            "\n",
            "Test set: Average loss: 0.2066, Accuracy: 9386/10000 (94%)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "cat results/combined_results/stdout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jfj8MC7qSnY"
      },
      "source": [
        "The model has successfully trained and downloaded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdswltUddRWi",
        "outputId": "bee18171-f8e4-4326-edd1-c8de9cf8c8e5",
        "tags": [
          "skip-execution"
        ]
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mnist_rnn.pt\n"
          ]
        }
      ],
      "source": [
        "%%bash\n",
        "ls results/combined_results/outputs/"
      ]
    }
  ],
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