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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets.Femnist import Femnist\n",
"from graphs import SmallWorld\n",
"from collections import defaultdict\n",
"import os\n",
"import json\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = FEMNIST\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b = SmallWorld(6, 2, 2, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b.adj_list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(12):\n",
" print(b.neighbors(i))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clients = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_samples = []\n",
"data = defaultdict(lambda : None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datadir = \"./leaf/data/femnist/data/train\"\n",
"files = os.listdir(datadir)\n",
"total_users=0\n",
"users = set()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"files = os.listdir(datadir)[0:1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for f in files:\n",
" file_path = os.path.join(datadir, f)\n",
" print(file_path)\n",
" with open(file_path, 'r') as inf:\n",
" client_data = json.load(inf)\n",
" current_users = len(client_data['users'])\n",
" print(\"Current_Users: \", current_users)\n",
" total_users += current_users\n",
" users.update(client_data['users'])\n",
"\n",
"print(\"total_users: \", total_users)\n",
"print(\"total_users: \", len(users))\n",
"print(client_data['user_data'].keys())\n",
"print(np.array(client_data['user_data']['f3408_47']['x']).shape)\n",
"print(np.array(client_data['user_data']['f3408_47']['y']).shape)\n",
"print(np.array(client_data['user_data']['f3327_11']['x']).shape)\n",
"print(np.array(client_data['user_data']['f3327_11']['y']).shape)\n",
"print(np.unique(np.array(client_data['user_data']['f3327_11']['y'])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"file = 'run.py'\n",
"with open(file, 'r') as inf:\n",
" print(inf.readline().strip())\n",
" print(inf.readlines())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f(l):\n",
" l[2] = 'c'\n",
"\n",
"a = ['a', 'a', 'a']\n",
"print(a)\n",
"f(a)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l = ['a', 'b', 'c']\n",
"print(l[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from localconfig import LocalConfig\n",
"\n",
"def read_ini(file_path):\n",
" config = LocalConfig(file_path)\n",
" for section in config:\n",
" print(\"Section: \", section)\n",
" for key, value in config.items(section):\n",
" print((key, value))\n",
" print(dict(config.items('DATASET')))\n",
" return config\n",
" \n",
"config = read_ini(\"config.ini\")\n",
"for section in config:\n",
" print(section)\n",
"#d = dict(config.sections())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def func(a = 1, b = 2, c = 3):\n",
" print(a + b + c)\n",
"\n",
"l = [3, 5, 7]\n",
"\n",
"func(*l)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from torch import multiprocessing as mp\n",
"\n",
"mp.spawn(fn = func, nprocs = 2, args = [], kwargs = {'a': 4, 'b': 5, 'c': 6})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l = '[0.4, 0.2, 0.3, 0.1]'\n",
"type(eval(l))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from decentralizepy.datasets.Femnist import Femnist\n",
"f1 = Femnist(0, 1, 'leaf/data/femnist/data/train')\n",
"ts = f1.get_trainset(1)\n",
"for data, target in ts:\n",
" print(data)\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from decentralizepy.datasets.Femnist import Femnist\n",
"from decentralizepy.graphs.SmallWorld import SmallWorld\n",
"from decentralizepy.mappings.Linear import Linear\n",
"\n",
"f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\n",
"g = SmallWorld(4, 1, 0.5)\n",
"l = Linear(2, 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from decentralizepy.node.Node import Node\n",
"from torch import multiprocessing as mp\n",
"import logging\n",
"n1 = Node(0, l, g, f, \"./results\", logging.DEBUG)\n",
"n2 = Node(1, l, g, f, \"./results\", logging.DEBUG)\n",
"# mp.spawn(fn = Node, nprocs = 2, args=[l,g,f])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from testing import f"
]
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CNN(\n",
" (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
" (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
" (conv3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
" (conv4): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
" (fc1): Linear(in_features=800, out_features=2, bias=True)\n",
")\n"
]
}
],
"source": [
"from torch import multiprocessing as mp\n",
"import torch\n",
"from decentralizepy.datasets.Celeba import CNN\n",
"import numpy as np\n",
"m1 = CNN()\n",
"o1 = torch.optim.SGD(m1.parameters(), 0.6)\n",
"print(m1)\n",
"\n",
"\n",
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<generator object Module.parameters at 0x111ec1ba0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m1.parameters()"
]
},
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"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('conv1.weight',\n",
" tensor([[[[-0.1261, 0.1833, -0.1406],\n",
" [ 0.1324, -0.0685, 0.0938],\n",
" [ 0.0432, 0.1814, -0.0541]],\n",
" \n",
" [[-0.1776, -0.1839, -0.0111],\n",
" [ 0.0888, 0.0888, -0.1344],\n",
" [-0.1838, 0.1737, 0.1584]],\n",
" \n",
" [[ 0.0417, 0.1064, -0.0156],\n",
" [ 0.0667, 0.0856, -0.1746],\n",
" [ 0.0412, 0.1620, 0.0125]]],\n",
" \n",
" \n",
" [[[-0.0530, -0.1273, -0.0797],\n",
" [ 0.0422, 0.1135, 0.0475],\n",
" [-0.0244, 0.1691, -0.1383]],\n",
" \n",
" [[ 0.0822, -0.1317, -0.1692],\n",
" [ 0.1373, 0.1388, 0.0103],\n",
" [-0.0481, 0.1105, 0.0631]],\n",
" \n",
" [[-0.0352, 0.1259, -0.0530],\n",
" [-0.1394, -0.0281, 0.1844],\n",
" [ 0.0082, 0.1187, 0.0211]]],\n",
" \n",
" \n",
" [[[ 0.0987, 0.0788, -0.1126],\n",
" [ 0.1769, 0.0763, -0.1767],\n",
" [-0.0570, 0.1156, 0.1770]],\n",
" \n",
" [[ 0.0643, -0.0024, -0.0625],\n",
" [ 0.0819, 0.0140, -0.1882],\n",
" [ 0.1325, -0.0632, -0.0202]],\n",
" \n",
" [[ 0.0053, 0.1042, -0.0058],\n",
" [-0.1082, -0.1753, 0.1762],\n",
" [-0.0501, 0.1166, 0.0561]]],\n",
" \n",
" \n",
" [[[ 0.0358, -0.0685, -0.1278],\n",
" [ 0.0029, -0.1107, 0.1169],\n",
" [-0.1408, 0.1293, 0.1142]],\n",
" \n",
" [[-0.0814, 0.0470, 0.0188],\n",
" [ 0.1538, 0.0137, 0.1128],\n",
" [-0.1597, 0.1432, 0.1370]],\n",
" \n",
" [[ 0.1425, 0.1769, -0.0037],\n",
" [-0.1080, -0.0805, -0.0195],\n",
" [-0.1335, -0.1666, 0.1399]]],\n",
" \n",
" \n",
" [[[ 0.1117, 0.1918, -0.1666],\n",
" [-0.1392, 0.0086, 0.0172],\n",
" [-0.0721, -0.1711, 0.0344]],\n",
" \n",
" [[ 0.1820, -0.0537, -0.0974],\n",
" [ 0.0366, -0.0710, 0.1273],\n",
" [ 0.1132, -0.1594, 0.0878]],\n",
" \n",
" [[-0.0874, -0.0401, 0.1827],\n",
" [-0.0301, 0.1205, -0.0396],\n",
" [-0.1143, -0.1007, 0.1561]]],\n",
" \n",
" \n",
" [[[ 0.1522, -0.0012, -0.1785],\n",
" [-0.1833, -0.1828, -0.1643],\n",
" [-0.1765, -0.1757, -0.0608]],\n",
" \n",
" [[-0.0684, 0.0521, 0.1137],\n",
" [-0.0028, 0.0616, 0.0758],\n",
" [-0.1736, 0.0667, 0.1229]],\n",
" \n",
" [[ 0.1298, -0.1848, -0.1570],\n",
" [-0.1052, -0.1172, -0.1223],\n",
" [-0.1389, -0.0095, -0.0410]]],\n",
" \n",
" \n",
" [[[ 0.0213, -0.0975, 0.0964],\n",
" [ 0.0535, -0.0775, 0.0790],\n",
" [-0.1796, -0.1468, 0.1036]],\n",
" \n",
" [[-0.0403, 0.0646, -0.0932],\n",
" [ 0.1779, -0.1616, 0.0644],\n",
" [-0.0508, -0.1158, -0.0592]],\n",
" \n",
" [[-0.1644, -0.1327, 0.0817],\n",
" [ 0.0320, -0.0213, -0.0946],\n",
" [-0.1106, 0.1463, -0.1642]]],\n",
" \n",
" \n",
" [[[-0.0985, -0.1160, -0.0915],\n",
" [ 0.1857, 0.0806, 0.1761],\n",
" [-0.0817, 0.1095, 0.0896]],\n",
" \n",
" [[-0.0660, -0.1680, 0.1833],\n",
" [ 0.0611, 0.0077, -0.0848],\n",
" [-0.1516, 0.1737, 0.0484]],\n",
" \n",
" [[ 0.1434, -0.0732, -0.0904],\n",
" [ 0.0962, 0.1783, 0.0192],\n",
" [ 0.0915, 0.0006, 0.0334]]],\n",
" \n",
" \n",
" [[[-0.0047, 0.1807, -0.1798],\n",
" [-0.0164, 0.1119, -0.0805],\n",
" [ 0.1855, -0.0681, -0.0187]],\n",
" \n",
" [[-0.0069, 0.0491, -0.1868],\n",
" [-0.1609, -0.0316, 0.0150],\n",
" [-0.1605, 0.1506, -0.0074]],\n",
" \n",
" [[ 0.0851, -0.1732, -0.1777],\n",
" [ 0.0539, -0.0500, -0.1231],\n",
" [ 0.1654, 0.0342, -0.1904]]],\n",
" \n",
" \n",
" [[[ 0.0476, 0.0284, 0.1212],\n",
" [-0.1603, -0.1924, 0.0144],\n",
" [ 0.0076, -0.0928, -0.1645]],\n",
" \n",
" [[ 0.0215, 0.1845, -0.1034],\n",
" [ 0.1574, -0.1577, -0.0438],\n",
" [-0.1360, -0.0601, -0.1693]],\n",
" \n",
" [[-0.0720, 0.0619, 0.1405],\n",
" [ 0.0699, -0.1288, 0.0041],\n",
" [-0.0381, -0.1697, -0.1568]]],\n",
" \n",
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" ('conv1.bias',\n",
" tensor([-1.8698e-01, -7.9379e-06, -1.9277e-02, 5.2182e-02, 7.5716e-02,\n",
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" \n",
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" \n",
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" \n",
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" \n",
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" \n",
" ...,\n",
" \n",
" [[-5.1427e-02, 2.4528e-02, -5.4878e-02],\n",
" [-1.8610e-02, 5.4365e-02, 3.5053e-03],\n",
" [-3.9922e-02, 4.2510e-02, -5.7261e-02]],\n",
" \n",
" [[ 4.1938e-02, -4.2039e-02, -1.2487e-02],\n",
" [-1.4090e-02, -3.7895e-02, 1.4394e-02],\n",
" [ 2.2555e-02, -2.7264e-02, 5.6102e-02]],\n",
" \n",
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" [ 5.2089e-02, -2.8859e-02, -2.6499e-03],\n",
" [-5.2122e-02, -3.7436e-02, 3.9897e-02]]],\n",
" \n",
" \n",
" [[[ 2.7888e-02, 2.9241e-02, -1.9488e-02],\n",
" [ 2.8928e-02, 5.3312e-02, -2.9810e-02],\n",
" [-8.5104e-03, 5.7751e-02, -8.1857e-03]],\n",
" \n",
" [[ 4.2649e-02, 3.3158e-03, 4.2879e-02],\n",
" [ 7.7893e-03, -3.2879e-02, 2.7630e-02],\n",
" [ 5.4706e-03, 4.8019e-02, 1.2420e-02]],\n",
" \n",
" [[-4.2004e-02, -4.2790e-02, 2.4634e-02],\n",
" [-5.4641e-02, 3.4600e-02, 2.9071e-03],\n",
" [ 2.6470e-02, 4.6701e-02, 3.7158e-02]],\n",
" \n",
" ...,\n",
" \n",
" [[-1.7641e-02, -2.1205e-02, -5.1504e-02],\n",
" [-7.4737e-03, 5.5061e-02, -2.6397e-03],\n",
" [-4.4653e-02, -3.6719e-02, 3.4420e-06]],\n",
" \n",
" [[ 1.6525e-02, 1.7280e-02, 5.4554e-03],\n",
" [ 4.0098e-02, 2.7571e-02, -4.4965e-02],\n",
" [ 6.1493e-03, -5.7754e-02, 1.0513e-02]],\n",
" \n",
" [[-5.7615e-02, 3.2921e-02, -1.5900e-02],\n",
" [ 2.0081e-02, 5.4590e-02, 1.1296e-02],\n",
" [-4.5015e-02, 1.1341e-03, 2.6447e-02]]]])),\n",
" ('conv2.bias',\n",
" tensor([-0.0538, -0.0320, -0.0153, 0.0558, 0.0254, 0.0281, -0.0148, 0.0060,\n",
" -0.0283, -0.0062, 0.0437, -0.0064, 0.0341, 0.0233, -0.0201, 0.0391,\n",
" 0.0243, 0.0071, 0.0125, -0.0138, -0.0377, -0.0169, -0.0475, -0.0004,\n",
" -0.0105, -0.0502, 0.0241, 0.0090, 0.0069, -0.0315, -0.0192, 0.0204])),\n",
" ('conv3.weight',\n",
" tensor([[[[-0.0215, -0.0208, -0.0272],\n",
" [-0.0493, -0.0117, -0.0285],\n",
" [ 0.0515, -0.0041, 0.0126]],\n",
" \n",
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" [ 0.0450, 0.0449, -0.0583],\n",
" [-0.0452, -0.0480, -0.0275]],\n",
" \n",
" [[-0.0262, -0.0338, 0.0505],\n",
" [ 0.0146, -0.0364, -0.0044],\n",
" [-0.0102, -0.0051, 0.0017]],\n",
" \n",
" ...,\n",
" \n",
" [[-0.0367, -0.0468, -0.0586],\n",
" [ 0.0126, 0.0037, 0.0191],\n",
" [-0.0153, 0.0048, -0.0160]],\n",
" \n",
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" [-0.0125, 0.0500, 0.0009]],\n",
" \n",
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" [-0.0489, 0.0118, 0.0387],\n",
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" \n",
" \n",
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" \n",
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" \n",
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" [ 0.0190, 0.0326, -0.0219]],\n",
" \n",
" ...,\n",
" \n",
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" [-0.0296, 0.0205, -0.0300]],\n",
" \n",
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" [-0.0462, 0.0293, -0.0351]],\n",
" \n",
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" [-0.0519, 0.0351, 0.0357]]],\n",
" \n",
" \n",
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" [-0.0195, -0.0235, -0.0378]],\n",
" \n",
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" [ 0.0360, 0.0367, 0.0458],\n",
" [-0.0189, -0.0017, 0.0145]],\n",
" \n",
" [[-0.0297, 0.0567, 0.0276],\n",
" [ 0.0298, 0.0383, 0.0227],\n",
" [ 0.0262, 0.0063, 0.0131]],\n",
" \n",
" ...,\n",
" \n",
" [[ 0.0002, 0.0432, -0.0247],\n",
" [ 0.0068, -0.0298, -0.0484],\n",
" [-0.0361, -0.0014, 0.0444]],\n",
" \n",
" [[-0.0184, -0.0201, -0.0163],\n",
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" [ 0.0283, 0.0149, -0.0588]],\n",
" \n",
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