Untitled8.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyM0sZe51AsMTEl2ZS3L76RT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mad-Roy/einsum/blob/master/Untitled8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jr3y8Gp_mdhc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "#drive.mount('/content/drive')\n",
        "\n",
        "checkpoints = '/content/drive/MyDrive/colab_files/SWMS20'\n",
        "if not os.path.exists(checkpoints):\n",
        "    os.makedirs(checkpoints)\n",
        "\n",
        "%cd /content/drive/MyDrive/colab_files/SWMS20\n",
        "\n",
        "! git clone https://github.com/felisat/federated-learning.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpLXtrJVGO_3",
        "outputId": "2aa252ad-b9a0-487e-b26f-a9fb26f34ab3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# navigate to the cloned repository\n",
        "%cd /content/drive/MyDrive/colab_files/SWMS20/federated-learning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqeE-NAbPEfo",
        "outputId": "a49144ed-aabc-43b8-c666-09d8b3e0e47d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/colab_files/SWMS20/federated-learning\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python federated_learning.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgamQG_58pS9",
        "outputId": "a3202832-fbe4-478f-efca-6029cd205abe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch Version:  2.3.0+cu121\n",
            "Running 1 Experiments..\n",
            "\n",
            "Hyperparameters: \n",
            " - dataset                 mnist\n",
            " - net                     logistic\n",
            " - iterations              2000\n",
            " - n_clients               2000\n",
            " - participation_rate      0.5\n",
            " - classes_per_client      10\n",
            " - batch_size              1\n",
            " - balancedness            1.0\n",
            " - momentum                0.9\n",
            " - log_frequency           30\n",
            " - log_path                results/trash/\n",
            " - finished                False\n",
            " - log_id                  33470\n",
            " - communication_rounds    2000\n",
            " - compression_up          ['none', {}]\n",
            " - accumulation_up         False\n",
            " - compression_down        ['none', {}]\n",
            " - accumulation_down       False\n",
            " - lr                      0.04\n",
            " - aggregation             mean\n",
            " - local_iterations        1\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n",
            "100% 9912422/9912422 [00:02<00:00, 4467493.19it/s]\n",
            "Extracting /data/PyTorch/MNIST/MNIST/raw/train-images-idx3-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz\n",
            "100% 28881/28881 [00:00<00:00, 132552.87it/s]\n",
            "Extracting /data/PyTorch/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "100% 1648877/1648877 [00:01<00:00, 1262670.98it/s]\n",
            "Extracting /data/PyTorch/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 403: Forbidden\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "100% 4542/4542 [00:00<00:00, 12165088.61it/s]\n",
            "Extracting /data/PyTorch/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz to /data/PyTorch/MNIST/MNIST/raw\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py:76: UserWarning: train_data has been renamed data\n",
            "  warnings.warn(\"train_data has been renamed data\")\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py:66: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py:81: UserWarning: test_data has been renamed data\n",
            "  warnings.warn(\"test_data has been renamed data\")\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/datasets/mnist.py:71: UserWarning: test_labels has been renamed targets\n",
            "  warnings.warn(\"test_labels has been renamed targets\")\n",
            "\n",
            "Data: \n",
            " - Train Set: ((60000, 1, 28, 28),(60000,)), Range: [0.000, 1.000], Labels: 0,..,9\n",
            " - Test Set: ((10000, 1, 28, 28),(10000,)), Range: [0.000, 1.000], Labels: 0,..,9\n",
            "Data split:\n",
            " - Client 0: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 1: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 2: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 3: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 4: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 5: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 6: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 7: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 8: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 9: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 10: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 11: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 12: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 13: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 14: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 15: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 16: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 17: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 18: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 19: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 20: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 21: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 22: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 23: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 24: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 25: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 26: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 27: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 28: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 29: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 30: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 31: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 32: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 33: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 34: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 35: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 36: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 37: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 38: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 39: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 40: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 41: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 42: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 43: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 44: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 45: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 46: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 47: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 48: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 49: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 50: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 51: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 52: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 53: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 54: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 55: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 56: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 57: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 58: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 59: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 60: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 61: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 62: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 63: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 64: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 65: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 66: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 67: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 68: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 69: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 70: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 71: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 72: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 73: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 74: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 75: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 76: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 77: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 78: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 79: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 80: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 81: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 82: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 83: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 84: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 85: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 86: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 87: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 88: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 89: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 90: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 91: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 92: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 93: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 94: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 95: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 96: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 97: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 98: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 99: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 100: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 101: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 102: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 103: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 104: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 105: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 106: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 107: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 108: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 109: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 110: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 111: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 112: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 113: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 114: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 115: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 116: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 117: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 118: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 119: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 120: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 121: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 122: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 123: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 124: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 125: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 126: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 127: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 128: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 129: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 130: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 131: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 132: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 134: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 146: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 149: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 154: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 157: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 160: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 163: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 164: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 166: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 168: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 171: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 172: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 174: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 175: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 176: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 177: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 178: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 179: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 180: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 181: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 183: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 184: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 185: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 186: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 187: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 188: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 189: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 190: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 191: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 192: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 193: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 194: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 195: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 196: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 197: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 198: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 199: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 200: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 201: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 202: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 203: [3 3 3 3 3 3 3 3 3 3]\n",
            " - Client 204: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 218: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 224: [3 3 3 3 3 3 3 3 3 3]\n",
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            " - Client 1926: [3 6 3 3 3 0 3 3 3 3]\n",
            " - Client 1927: [3 3 3 3 3 0 6 3 3 3]\n",
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            " - Client 1929: [5 3 3 3 1 0 3 3 3 6]\n",
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            " - Client 1951: [0 6 6 6 0 0 0 6 0 6]\n",
            " - Client 1952: [0 6 6 6 0 0 0 6 0 6]\n",
            " - Client 1953: [0 6 6 6 0 0 0 6 0 6]\n",
            " - Client 1954: [0 6 6 6 0 0 0 6 0 6]\n",
            " - Client 1955: [0 6 6 6 0 0 0 6 0 6]\n",
            " - Client 1956: [0 7 6 6 0 0 0 9 0 2]\n",
            " - Client 1957: [0 6 9 9 0 0 0 6 0 0]\n",
            " - Client 1958: [0 6 9 9 0 0 0 6 0 0]\n",
            " - Client 1959: [0 9 3 9 0 0 0 9 0 0]\n",
            " - Client 1960: [ 0  9  0 12  0  0  0  9  0  0]\n",
            " - Client 1961: [ 0  9  0  9  0  0  0 12  0  0]\n",
            " - Client 1962: [ 0 12  0  9  0  0  0  9  0  0]\n",
            " - Client 1963: [ 0 12  0  9  0  0  0  9  0  0]\n",
            " - Client 1964: [ 0 12  0  9  0  0  0  9  0  0]\n",
            " - Client 1965: [ 0 12  0  9  0  0  0  9  0  0]\n",
            " - Client 1966: [ 0  9  0 12  0  0  0  9  0  0]\n",
            " - Client 1967: [ 0 12  0  9  0  0  0  9  0  0]\n",
            " - Client 1968: [ 0  9  0 12  0  0  0  9  0  0]\n",
            " - Client 1969: [ 0  9  0  9  0  0  0 12  0  0]\n",
            " - Client 1970: [ 0  9  0 12  0  0  0  9  0  0]\n",
            " - Client 1971: [ 0  9  0  9  0  0  0 12  0  0]\n",
            " - Client 1972: [ 0  9  0  9  0  0  0 12  0  0]\n",
            " - Client 1973: [ 0 13  0  5  0  0  0 12  0  0]\n",
            " - Client 1974: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1975: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1976: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1977: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1978: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1979: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1980: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1981: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1982: [ 0 15  0  0  0  0  0 15  0  0]\n",
            " - Client 1983: [ 0 17  0  0  0  0  0 13  0  0]\n",
            " - Client 1984: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1985: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1986: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1987: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1988: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1989: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1990: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1991: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1992: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1993: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1994: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1995: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1996: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1997: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1998: [ 0 30  0  0  0  0  0  0  0  0]\n",
            " - Client 1999: [ 0 30  0  0  0  0  0  0  0  0]\n",
            "\n",
            "Model logistic:\n",
            " - linear.weight                  [10, 1024]\n",
            " - linear.bias                    [10]\n",
            "Total number of Parameters:  10250\n",
            "\n",
            "Optimizer: SGD\n",
            " - lr : 0.04\n",
            " - momentum : 0.9\n",
            " - dampening : 0\n",
            " - weight_decay : 0.0\n",
            " - nesterov : False\n",
            " - maximize : False\n",
            " - foreach : None\n",
            " - differentiable : False\n",
            " - fused : None\n",
            "Start Distributed Training..\n",
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:143: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1, 'lr': 0.04, 'epoch': 1, 'iteration': 1}\n",
            "{'loss_train': 1.6189580583572387, 'accuracy_train': 0.5034}\n",
            "{'loss_test': 1.655214203596115, 'accuracy_test': 0.4971}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 04:03:19 [0.05%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 30, 'lr': 0.04, 'epoch': 1, 'iteration': 30}\n",
            "{'loss_train': 0.45495308011770247, 'accuracy_train': 0.9072}\n",
            "{'loss_test': 0.4824809387954883, 'accuracy_test': 0.9016}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 01:06:13 [1.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 60, 'lr': 0.04, 'epoch': 2, 'iteration': 60}\n",
            "{'loss_train': 0.35877748727798464, 'accuracy_train': 0.9136}\n",
            "{'loss_test': 0.37764082273468375, 'accuracy_test': 0.9103}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 01:02:01 [3.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 90, 'lr': 0.04, 'epoch': 2, 'iteration': 90}\n",
            "{'loss_train': 0.297791333347559, 'accuracy_train': 0.922}\n",
            "{'loss_test': 0.3281800276134163, 'accuracy_test': 0.911}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:59:53 [4.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 120, 'lr': 0.04, 'epoch': 3, 'iteration': 120}\n",
            "{'loss_train': 0.27096870079636576, 'accuracy_train': 0.9278}\n",
            "{'loss_test': 0.3010321523994207, 'accuracy_test': 0.9171}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:58:22 [6.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 150, 'lr': 0.04, 'epoch': 3, 'iteration': 150}\n",
            "{'loss_train': 0.2620909966528416, 'accuracy_train': 0.9274}\n",
            "{'loss_test': 0.2971915673278272, 'accuracy_test': 0.9183}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:57:18 [7.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 180, 'lr': 0.04, 'epoch': 4, 'iteration': 180}\n",
            "{'loss_train': 0.25994020760059355, 'accuracy_train': 0.9282}\n",
            "{'loss_test': 0.2944032986089587, 'accuracy_test': 0.9191}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:56:13 [9.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 210, 'lr': 0.04, 'epoch': 4, 'iteration': 210}\n",
            "{'loss_train': 0.2469225837290287, 'accuracy_train': 0.9314}\n",
            "{'loss_test': 0.2804557617846876, 'accuracy_test': 0.9209}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:55:14 [10.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 240, 'lr': 0.04, 'epoch': 5, 'iteration': 240}\n",
            "{'loss_train': 0.24730051428079605, 'accuracy_train': 0.9326}\n",
            "{'loss_test': 0.2783334972150624, 'accuracy_test': 0.921}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:54:09 [12.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 270, 'lr': 0.04, 'epoch': 5, 'iteration': 270}\n",
            "{'loss_train': 0.24221617251634597, 'accuracy_train': 0.9306}\n",
            "{'loss_test': 0.28044545151293276, 'accuracy_test': 0.9215}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:53:08 [13.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 300, 'lr': 0.04, 'epoch': 5, 'iteration': 300}\n",
            "{'loss_train': 0.24806441679596902, 'accuracy_train': 0.9312}\n",
            "{'loss_test': 0.2802179349772632, 'accuracy_test': 0.9214}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:52:12 [15.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 330, 'lr': 0.04, 'epoch': 6, 'iteration': 330}\n",
            "{'loss_train': 0.24294354438781737, 'accuracy_train': 0.9316}\n",
            "{'loss_test': 0.2775386860780418, 'accuracy_test': 0.9212}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:51:15 [16.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 360, 'lr': 0.04, 'epoch': 6, 'iteration': 360}\n",
            "{'loss_train': 0.25161556333303453, 'accuracy_train': 0.9298}\n",
            "{'loss_test': 0.28265510754659773, 'accuracy_test': 0.9204}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:50:25 [18.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 390, 'lr': 0.04, 'epoch': 7, 'iteration': 390}\n",
            "{'loss_train': 0.24104828476905824, 'accuracy_train': 0.9308}\n",
            "{'loss_test': 0.28345398847013714, 'accuracy_test': 0.9225}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:49:30 [19.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 420, 'lr': 0.04, 'epoch': 7, 'iteration': 420}\n",
            "{'loss_train': 0.24082996159791947, 'accuracy_train': 0.9342}\n",
            "{'loss_test': 0.2814714030176401, 'accuracy_test': 0.9223}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:48:34 [21.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 450, 'lr': 0.04, 'epoch': 8, 'iteration': 450}\n",
            "{'loss_train': 0.2557429568469524, 'accuracy_train': 0.9308}\n",
            "{'loss_test': 0.30401160043664277, 'accuracy_test': 0.9149}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:47:37 [22.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 480, 'lr': 0.04, 'epoch': 8, 'iteration': 480}\n",
            "{'loss_train': 0.24645600348711014, 'accuracy_train': 0.9282}\n",
            "{'loss_test': 0.2879897978808731, 'accuracy_test': 0.9202}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:46:41 [24.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 510, 'lr': 0.04, 'epoch': 9, 'iteration': 510}\n",
            "{'loss_train': 0.2514480482041836, 'accuracy_train': 0.9316}\n",
            "{'loss_test': 0.28851506737060845, 'accuracy_test': 0.9197}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:45:43 [25.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 540, 'lr': 0.04, 'epoch': 9, 'iteration': 540}\n",
            "{'loss_train': 0.23750024452805518, 'accuracy_train': 0.9364}\n",
            "{'loss_test': 0.2789179584756494, 'accuracy_test': 0.9237}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:44:49 [27.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 570, 'lr': 0.04, 'epoch': 10, 'iteration': 570}\n",
            "{'loss_train': 0.23976820781826974, 'accuracy_train': 0.9338}\n",
            "{'loss_test': 0.2735230202227831, 'accuracy_test': 0.9241}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:43:55 [28.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 600, 'lr': 0.04, 'epoch': 11, 'iteration': 600}\n",
            "{'loss_train': 0.23740291997790336, 'accuracy_train': 0.9342}\n",
            "{'loss_test': 0.2768143512867391, 'accuracy_test': 0.9243}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:43:00 [30.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 630, 'lr': 0.04, 'epoch': 11, 'iteration': 630}\n",
            "{'loss_train': 0.23717033565044404, 'accuracy_train': 0.9342}\n",
            "{'loss_test': 0.27799954641610386, 'accuracy_test': 0.9224}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:42:04 [31.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 660, 'lr': 0.04, 'epoch': 12, 'iteration': 660}\n",
            "{'loss_train': 0.2373594118654728, 'accuracy_train': 0.9326}\n",
            "{'loss_test': 0.27508325699716807, 'accuracy_test': 0.9226}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:41:07 [33.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 690, 'lr': 0.04, 'epoch': 12, 'iteration': 690}\n",
            "{'loss_train': 0.24452089250087738, 'accuracy_train': 0.9296}\n",
            "{'loss_test': 0.2874266448430717, 'accuracy_test': 0.9179}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:40:11 [34.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 720, 'lr': 0.04, 'epoch': 13, 'iteration': 720}\n",
            "{'loss_train': 0.2590989373624325, 'accuracy_train': 0.9294}\n",
            "{'loss_test': 0.2998288099654019, 'accuracy_test': 0.917}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:39:15 [36.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 750, 'lr': 0.04, 'epoch': 13, 'iteration': 750}\n",
            "{'loss_train': 0.23692467346787452, 'accuracy_train': 0.932}\n",
            "{'loss_test': 0.2798419842775911, 'accuracy_test': 0.9236}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:38:20 [37.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 780, 'lr': 0.04, 'epoch': 14, 'iteration': 780}\n",
            "{'loss_train': 0.24311702817678452, 'accuracy_train': 0.9334}\n",
            "{'loss_test': 0.2847188723646104, 'accuracy_test': 0.9182}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:37:24 [39.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 810, 'lr': 0.04, 'epoch': 14, 'iteration': 810}\n",
            "{'loss_train': 0.23074641242623328, 'accuracy_train': 0.9382}\n",
            "{'loss_test': 0.2768525788001716, 'accuracy_test': 0.9229}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:36:29 [40.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 840, 'lr': 0.04, 'epoch': 15, 'iteration': 840}\n",
            "{'loss_train': 0.23355727791786193, 'accuracy_train': 0.937}\n",
            "{'loss_test': 0.27605711229145524, 'accuracy_test': 0.9227}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:35:33 [42.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 870, 'lr': 0.04, 'epoch': 15, 'iteration': 870}\n",
            "{'loss_train': 0.2658767367899418, 'accuracy_train': 0.9232}\n",
            "{'loss_test': 0.3113087633159012, 'accuracy_test': 0.9105}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:34:38 [43.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 900, 'lr': 0.04, 'epoch': 15, 'iteration': 900}\n",
            "{'loss_train': 0.23038496866822242, 'accuracy_train': 0.936}\n",
            "{'loss_test': 0.2742731927800924, 'accuracy_test': 0.925}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:33:42 [45.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 930, 'lr': 0.04, 'epoch': 16, 'iteration': 930}\n",
            "{'loss_train': 0.2354966290295124, 'accuracy_train': 0.9342}\n",
            "{'loss_test': 0.2747309054248035, 'accuracy_test': 0.9229}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:32:45 [46.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 960, 'lr': 0.04, 'epoch': 16, 'iteration': 960}\n",
            "{'loss_train': 0.23314209997653962, 'accuracy_train': 0.9384}\n",
            "{'loss_test': 0.2757594368979335, 'accuracy_test': 0.9232}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:31:50 [48.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 990, 'lr': 0.04, 'epoch': 17, 'iteration': 990}\n",
            "{'loss_train': 0.23152807459235192, 'accuracy_train': 0.935}\n",
            "{'loss_test': 0.2808054952695966, 'accuracy_test': 0.9243}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:30:55 [49.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1020, 'lr': 0.04, 'epoch': 17, 'iteration': 1020}\n",
            "{'loss_train': 0.23379070073366165, 'accuracy_train': 0.9338}\n",
            "{'loss_test': 0.2768472820147872, 'accuracy_test': 0.9241}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:30:00 [51.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1050, 'lr': 0.04, 'epoch': 18, 'iteration': 1050}\n",
            "{'loss_train': 0.22607648104429245, 'accuracy_train': 0.9378}\n",
            "{'loss_test': 0.27953976646997036, 'accuracy_test': 0.9231}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:29:05 [52.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1080, 'lr': 0.04, 'epoch': 18, 'iteration': 1080}\n",
            "{'loss_train': 0.23263407424092292, 'accuracy_train': 0.9374}\n",
            "{'loss_test': 0.282100108359009, 'accuracy_test': 0.9219}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:28:10 [54.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1110, 'lr': 0.04, 'epoch': 19, 'iteration': 1110}\n",
            "{'loss_train': 0.22772779941558838, 'accuracy_train': 0.9374}\n",
            "{'loss_test': 0.27666061588563023, 'accuracy_test': 0.9251}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:27:15 [55.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1140, 'lr': 0.04, 'epoch': 19, 'iteration': 1140}\n",
            "{'loss_train': 0.23134037211537362, 'accuracy_train': 0.933}\n",
            "{'loss_test': 0.27765286825597285, 'accuracy_test': 0.9241}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:26:19 [57.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1170, 'lr': 0.04, 'epoch': 20, 'iteration': 1170}\n",
            "{'loss_train': 0.23871831327676774, 'accuracy_train': 0.9334}\n",
            "{'loss_test': 0.27950786247849463, 'accuracy_test': 0.9228}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:25:23 [58.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1200, 'lr': 0.04, 'epoch': 20, 'iteration': 1200}\n",
            "{'loss_train': 0.230765468031168, 'accuracy_train': 0.9358}\n",
            "{'loss_test': 0.28287353289313616, 'accuracy_test': 0.9224}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:24:28 [60.00%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1230, 'lr': 0.04, 'epoch': 21, 'iteration': 1230}\n",
            "{'loss_train': 0.2234901423752308, 'accuracy_train': 0.9398}\n",
            "{'loss_test': 0.2743750416766852, 'accuracy_test': 0.9262}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:23:33 [61.50%]\n",
            "\n",
            "Experiment: main (1/1)\n",
            "Evaluate...\n",
            "Evaluated on 5000 samples (50 batches)\n",
            "Evaluated on 10000 samples (100 batches)\n",
            "{'communication_round': 1260, 'lr': 0.04, 'epoch': 21, 'iteration': 1260}\n",
            "{'loss_train': 0.2415687198936939, 'accuracy_train': 0.9324}\n",
            "{'loss_test': 0.29039076920598744, 'accuracy_test': 0.9187}\n",
            "Saved results to  results/trash/xp_33470.npz\n",
            "Remaining Time (approx.): 00:22:38 [63.00%]\n",
            "\n"
          ]
        }
      ]
    }
  ]
}