.gitignore

@@ -8,4 +8,6 @@
 **/leaf/
 **.egg-info
 2021**
-2022**
\ No newline at end of file
+2022**
+**/massif.out*
+*swp

README.rst

@@ -2,6 +2,9 @@
 decentralizepy
 ==============
 
+decentralizepy is a framework for running distributed applications (particularly ML) on top of arbitrary topologies (decentralized, federated, parameter server).
+It was primarily conceived for assessing scientific ideas on several aspects of distributed learning (communication efficiency, privacy, data heterogeneity etc.).
+
 -------------------------
 Setting up decentralizepy
 -------------------------
@@ -23,10 +26,14 @@ Setting up decentralizepy
     pip install --upgrade pip
 
 * On Mac M1, installing ``pyzmq`` fails with `pip`. Use `conda <https://conda.io>`_.
-* Install decentralizepy for development. ::
+* Install decentralizepy for development. (zsh) ::
 
     pip3 install --editable .\[dev\]
     
+* Install decentralizepy for development. (bash) ::
+
+    pip3 install --editable .[dev]
+ 
 ----------------
 Running the code
 ----------------

eval/6_star.edges

+6
+0 1
+0 2
+0 3
+0 4
+0 5
+1 0
+2 0
+3 0
+4 0
+5 0
\ No newline at end of file

eval/main.ipynb

 %% Cell type:code id: tags:
 
-``` 
+``` python
 from datasets.Femnist import Femnist
 from graphs import SmallWorld
 from collections import defaultdict
 import os
 import json
 import numpy as np
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 a = FEMNIST
 a
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 b = SmallWorld(6, 2, 2, 1)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 b.adj_list
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 for i in range(12):
     print(b.neighbors(i))
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 clients = []
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 num_samples = []
 data = defaultdict(lambda : None)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 datadir = "./leaf/data/femnist/data/train"
 files = os.listdir(datadir)
 total_users=0
 users = set()
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 files = os.listdir(datadir)[0:1]
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 for f in files:
     file_path = os.path.join(datadir, f)
     print(file_path)
     with open(file_path, 'r') as inf:
         client_data = json.load(inf)
     current_users = len(client_data['users'])
     print("Current_Users: ", current_users)
     total_users += current_users
     users.update(client_data['users'])
 
 print("total_users: ", total_users)
 print("total_users: ", len(users))
 print(client_data['user_data'].keys())
 print(np.array(client_data['user_data']['f3408_47']['x']).shape)
 print(np.array(client_data['user_data']['f3408_47']['y']).shape)
 print(np.array(client_data['user_data']['f3327_11']['x']).shape)
 print(np.array(client_data['user_data']['f3327_11']['y']).shape)
 print(np.unique(np.array(client_data['user_data']['f3327_11']['y'])))
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 file = 'run.py'
 with open(file, 'r') as inf:
             print(inf.readline().strip())
             print(inf.readlines())
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 def f(l):
     l[2] = 'c'
 
 a = ['a', 'a', 'a']
 print(a)
 f(a)
 print(a)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 l = ['a', 'b', 'c']
 print(l[:-1])
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from localconfig import LocalConfig
 
 def read_ini(file_path):
     config = LocalConfig(file_path)
     for section in config:
         print("Section: ", section)
         for key, value in config.items(section):
             print((key, value))
     print(dict(config.items('DATASET')))
     return config
 
 config = read_ini("config.ini")
 for section in config:
     print(section)
 #d = dict(config.sections())
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 def func(a = 1, b = 2, c = 3):
     print(a + b + c)
 
 l = [3, 5, 7]
 
 func(*l)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from torch import multiprocessing as mp
 
 mp.spawn(fn = func, nprocs = 2, args = [], kwargs = {'a': 4, 'b': 5, 'c': 6})
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 l = '[0.4, 0.2, 0.3, 0.1]'
 type(eval(l))
 
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.datasets.Femnist import Femnist
 f1 = Femnist(0, 1, 'leaf/data/femnist/data/train')
 ts = f1.get_trainset(1)
 for data, target in ts:
     print(data)
     break
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.datasets.Femnist import Femnist
 from decentralizepy.graphs.SmallWorld import SmallWorld
 from decentralizepy.mappings.Linear import Linear
 
 f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])
 g = SmallWorld(4, 1, 0.5)
 l = Linear(2, 2)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.node.Node import Node
 from torch import multiprocessing as mp
 import logging
 n1 = Node(0, l, g, f, "./results", logging.DEBUG)
 n2 = Node(1, l, g, f, "./results", logging.DEBUG)
 # mp.spawn(fn = Node, nprocs = 2, args=[l,g,f])
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from testing import f
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from torch import multiprocessing as mp
 import torch
 from decentralizepy.datasets.Celeba import CNN
 import numpy as np
 m1 = CNN()
 o1 = torch.optim.SGD(m1.parameters(), 0.6)
 print(m1)
 
 
 #mp.spawn(fn = f, nprocs = 2, args=[m1, o1])
 
 ```
 
 %% Output
 
     CNN(
       (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)
       (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
       (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)
       (conv3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)
       (conv4): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)
       (fc1): Linear(in_features=800, out_features=2, bias=True)
     )
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 m1.parameters()
 ```
 
 %% Output
 
     <generator object Module.parameters at 0x111ec1ba0>
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 m1.state_dict()
 ```
 
 %% Output
 
     OrderedDict([('conv1.weight',
                   tensor([[[[-0.1261,  0.1833, -0.1406],
                             [ 0.1324, -0.0685,  0.0938],
                             [ 0.0432,  0.1814, -0.0541]],
     
                            [[-0.1776, -0.1839, -0.0111],
                             [ 0.0888,  0.0888, -0.1344],
                             [-0.1838,  0.1737,  0.1584]],
     
                            [[ 0.0417,  0.1064, -0.0156],
                             [ 0.0667,  0.0856, -0.1746],
                             [ 0.0412,  0.1620,  0.0125]]],
     
     
                           [[[-0.0530, -0.1273, -0.0797],
                             [ 0.0422,  0.1135,  0.0475],
                             [-0.0244,  0.1691, -0.1383]],
     
                            [[ 0.0822, -0.1317, -0.1692],
                             [ 0.1373,  0.1388,  0.0103],
                             [-0.0481,  0.1105,  0.0631]],
     
                            [[-0.0352,  0.1259, -0.0530],
                             [-0.1394, -0.0281,  0.1844],
                             [ 0.0082,  0.1187,  0.0211]]],
     
     
                           [[[ 0.0987,  0.0788, -0.1126],
                             [ 0.1769,  0.0763, -0.1767],
                             [-0.0570,  0.1156,  0.1770]],
     
                            [[ 0.0643, -0.0024, -0.0625],
                             [ 0.0819,  0.0140, -0.1882],
                             [ 0.1325, -0.0632, -0.0202]],
     
                            [[ 0.0053,  0.1042, -0.0058],
                             [-0.1082, -0.1753,  0.1762],
                             [-0.0501,  0.1166,  0.0561]]],
     
     
                           [[[ 0.0358, -0.0685, -0.1278],
                             [ 0.0029, -0.1107,  0.1169],
                             [-0.1408,  0.1293,  0.1142]],
     
                            [[-0.0814,  0.0470,  0.0188],
                             [ 0.1538,  0.0137,  0.1128],
                             [-0.1597,  0.1432,  0.1370]],
     
                            [[ 0.1425,  0.1769, -0.0037],
                             [-0.1080, -0.0805, -0.0195],
                             [-0.1335, -0.1666,  0.1399]]],
     
     
                           [[[ 0.1117,  0.1918, -0.1666],
                             [-0.1392,  0.0086,  0.0172],
                             [-0.0721, -0.1711,  0.0344]],
     
                            [[ 0.1820, -0.0537, -0.0974],
                             [ 0.0366, -0.0710,  0.1273],
                             [ 0.1132, -0.1594,  0.0878]],
     
                            [[-0.0874, -0.0401,  0.1827],
                             [-0.0301,  0.1205, -0.0396],
                             [-0.1143, -0.1007,  0.1561]]],
     
     
                           [[[ 0.1522, -0.0012, -0.1785],
                             [-0.1833, -0.1828, -0.1643],
                             [-0.1765, -0.1757, -0.0608]],
     
                            [[-0.0684,  0.0521,  0.1137],
                             [-0.0028,  0.0616,  0.0758],
                             [-0.1736,  0.0667,  0.1229]],
     
                            [[ 0.1298, -0.1848, -0.1570],
                             [-0.1052, -0.1172, -0.1223],
                             [-0.1389, -0.0095, -0.0410]]],
     
     
                           [[[ 0.0213, -0.0975,  0.0964],
                             [ 0.0535, -0.0775,  0.0790],
                             [-0.1796, -0.1468,  0.1036]],
     
                            [[-0.0403,  0.0646, -0.0932],
                             [ 0.1779, -0.1616,  0.0644],
                             [-0.0508, -0.1158, -0.0592]],
     
                            [[-0.1644, -0.1327,  0.0817],
                             [ 0.0320, -0.0213, -0.0946],
                             [-0.1106,  0.1463, -0.1642]]],
     
     
                           [[[-0.0985, -0.1160, -0.0915],
                             [ 0.1857,  0.0806,  0.1761],
                             [-0.0817,  0.1095,  0.0896]],
     
                            [[-0.0660, -0.1680,  0.1833],
                             [ 0.0611,  0.0077, -0.0848],
                             [-0.1516,  0.1737,  0.0484]],
     
                            [[ 0.1434, -0.0732, -0.0904],
                             [ 0.0962,  0.1783,  0.0192],
                             [ 0.0915,  0.0006,  0.0334]]],
     
     
                           [[[-0.0047,  0.1807, -0.1798],
                             [-0.0164,  0.1119, -0.0805],
                             [ 0.1855, -0.0681, -0.0187]],
     
                            [[-0.0069,  0.0491, -0.1868],
                             [-0.1609, -0.0316,  0.0150],
                             [-0.1605,  0.1506, -0.0074]],
     
                            [[ 0.0851, -0.1732, -0.1777],
                             [ 0.0539, -0.0500, -0.1231],
                             [ 0.1654,  0.0342, -0.1904]]],
     
     
                           [[[ 0.0476,  0.0284,  0.1212],
                             [-0.1603, -0.1924,  0.0144],
                             [ 0.0076, -0.0928, -0.1645]],
     
                            [[ 0.0215,  0.1845, -0.1034],
                             [ 0.1574, -0.1577, -0.0438],
                             [-0.1360, -0.0601, -0.1693]],
     
                            [[-0.0720,  0.0619,  0.1405],
                             [ 0.0699, -0.1288,  0.0041],
                             [-0.0381, -0.1697, -0.1568]]],
     
     
                           [[[-0.1599,  0.1231, -0.1034],
                             [-0.0314,  0.0105, -0.1449],
                             [-0.0172, -0.0781,  0.0839]],
     
                            [[-0.0676,  0.1185, -0.1559],
                             [-0.1053, -0.1306,  0.1820],
                             [ 0.1584, -0.1370,  0.1828]],
     
                            [[ 0.0658,  0.1412, -0.0537],
                             [-0.1230, -0.1411, -0.0011],
                             [-0.1318, -0.0458,  0.1838]]],
     
     
                           [[[-0.0268,  0.1747, -0.1037],
                             [ 0.0515, -0.0228, -0.1024],
                             [-0.1543, -0.0643, -0.0100]],
     
                            [[-0.1572, -0.1530,  0.0026],
                             [ 0.1463, -0.1233,  0.0470],
                             [-0.1595, -0.1108, -0.0654]],
     
                            [[-0.0521, -0.0094,  0.1544],
                             [-0.0505, -0.0332,  0.0048],
                             [ 0.0735,  0.1350,  0.0690]]],
     
     
                           [[[ 0.0025,  0.0724,  0.0930],
                             [-0.1885,  0.0475,  0.1100],
                             [-0.1622,  0.0087, -0.0030]],
     
                            [[ 0.1032, -0.1425, -0.0620],
                             [ 0.1515, -0.0736, -0.1888],
                             [-0.1246,  0.1424, -0.0491]],
     
                            [[ 0.1759, -0.1616,  0.1198],
                             [-0.1103,  0.1032,  0.1727],
                             [-0.0601,  0.1635,  0.0034]]],
     
     
                           [[[ 0.0301,  0.1517,  0.0657],
                             [-0.1368, -0.1165,  0.1193],
                             [-0.0962,  0.1451,  0.1099]],
     
                            [[ 0.1646, -0.1860, -0.1187],
                             [-0.1367, -0.0911,  0.1337],
                             [-0.0926, -0.0524, -0.0672]],
     
                            [[-0.1509, -0.1231, -0.0855],
                             [ 0.1808, -0.0713,  0.0410],
                             [-0.0621, -0.0506,  0.1871]]],
     
     
                           [[[ 0.0888, -0.0874, -0.0826],
                             [ 0.0416, -0.0961,  0.0603],
                             [ 0.1455,  0.0050,  0.0318]],
     
                            [[-0.1633,  0.0070, -0.1537],
                             [-0.0109,  0.1602, -0.0463],
                             [-0.0423, -0.0147, -0.1045]],
     
                            [[ 0.1640, -0.0997, -0.1662],
                             [-0.1074,  0.1549, -0.1905],
                             [-0.1708,  0.1624,  0.0219]]],
     
     
                           [[[ 0.0824, -0.1376,  0.1086],
                             [ 0.0836,  0.0135,  0.0351],
                             [-0.1518,  0.0784, -0.1708]],
     
                            [[-0.1636, -0.1571,  0.1032],
                             [-0.1152,  0.0274, -0.1022],
                             [-0.0956, -0.1606, -0.1615]],
     
                            [[ 0.1307,  0.0419,  0.1924],
                             [-0.0599, -0.1296, -0.0448],
                             [ 0.0363,  0.0377, -0.0460]]],
     
     
                           [[[-0.1685, -0.1277, -0.0465],
                             [ 0.0922, -0.1011,  0.0742],
                             [ 0.0053, -0.1456,  0.0135]],
     
                            [[ 0.1341,  0.0131,  0.1281],
                             [-0.1020,  0.1069, -0.0631],
                             [-0.0439, -0.1189, -0.1822]],
     
                            [[ 0.1624, -0.1253,  0.0302],
                             [ 0.0709,  0.0767,  0.1453],
                             [ 0.0203,  0.1603, -0.1720]]],
     
     
                           [[[-0.1550,  0.1513, -0.1003],
                             [ 0.0370,  0.0367, -0.0233],
                             [ 0.0916, -0.0871,  0.1579]],
     
                            [[-0.1900,  0.0314,  0.0865],
                             [-0.0197,  0.0296, -0.0048],
                             [ 0.0846,  0.1543, -0.0770]],
     
                            [[-0.0016, -0.0978,  0.1826],
                             [-0.0477,  0.0689,  0.1079],
                             [ 0.0400,  0.0880,  0.1674]]],
     
     
                           [[[ 0.0145, -0.0447, -0.1742],
                             [ 0.0394,  0.0127, -0.1172],
                             [ 0.1330, -0.1207,  0.0326]],
     
                            [[-0.0155, -0.1602,  0.0023],
                             [ 0.0789,  0.1648,  0.1781],
                             [-0.1468, -0.0481, -0.1260]],
     
                            [[-0.0139,  0.0848, -0.0536],
                             [-0.1581,  0.1130,  0.0717],
                             [ 0.0275, -0.0006, -0.0049]]],
     
     
                           [[[-0.0199,  0.0032, -0.1246],
                             [ 0.0479,  0.1418, -0.1295],
                             [-0.1646, -0.1139, -0.1018]],
     
                            [[ 0.1475,  0.1413, -0.0354],
                             [ 0.0612, -0.1652,  0.0801],
                             [-0.1306, -0.0165,  0.1733]],
     
                            [[ 0.1527,  0.0911, -0.1906],
                             [-0.1152,  0.1737,  0.0436],
                             [-0.0213, -0.0314, -0.0319]]],
     
     
                           [[[-0.0003, -0.0546, -0.1255],
                             [ 0.0914, -0.1414,  0.0542],
                             [ 0.1139,  0.0132,  0.0815]],
     
                            [[-0.0042,  0.0541,  0.1456],
                             [ 0.0509, -0.0790,  0.0272],
                             [ 0.1419,  0.0992, -0.1448]],
     
                            [[ 0.0496,  0.0013,  0.0838],
                             [-0.0662,  0.0315, -0.1168],
                             [-0.0069, -0.1503,  0.0729]]],
     
     
                           [[[ 0.1866,  0.1329, -0.0560],
                             [ 0.0026,  0.1533,  0.0326],
                             [-0.1161, -0.0323,  0.0053]],
     
                            [[-0.0243, -0.1823, -0.1657],
                             [-0.0107, -0.0832,  0.0029],
                             [ 0.0981,  0.1241, -0.1788]],
     
                            [[-0.0400, -0.0577, -0.0757],
                             [-0.0584,  0.0176, -0.1019],
                             [-0.1828,  0.1589, -0.0312]]],
     
     
                           [[[-0.1083, -0.1236, -0.0904],
                             [-0.1575,  0.0157,  0.0552],
                             [-0.0839,  0.1704, -0.1457]],
     
                            [[-0.1648, -0.0270, -0.0489],
                             [-0.1122, -0.0288, -0.0073],
                             [-0.1443, -0.1712,  0.0100]],
     
                            [[-0.1142, -0.1552,  0.1568],
                             [ 0.0743, -0.1108, -0.0643],
                             [-0.0394, -0.1345,  0.0992]]],
     
     
                           [[[-0.1591,  0.0942, -0.1035],
                             [-0.0781,  0.0725, -0.0888],
                             [ 0.0959,  0.0213,  0.1222]],
     
                            [[ 0.1202, -0.0217, -0.0955],
                             [-0.1748, -0.1133, -0.0704],
                             [-0.0670, -0.1401,  0.1553]],
     
                            [[ 0.0053, -0.0871, -0.0239],
                             [ 0.0961, -0.0547,  0.1741],
                             [-0.0570,  0.0477,  0.1853]]],
     
     
                           [[[-0.1115, -0.0183, -0.1302],
                             [ 0.1435, -0.0238, -0.0048],
                             [ 0.1862, -0.1837,  0.1711]],
     
                            [[ 0.1375, -0.1798,  0.0818],
                             [-0.0792,  0.0820,  0.1373],
                             [ 0.1849,  0.0672, -0.1822]],
     
                            [[ 0.1868, -0.0356,  0.0726],
                             [-0.1523, -0.1130,  0.1506],
                             [-0.1046,  0.0178,  0.0990]]],
     
     
                           [[[ 0.1321, -0.1641,  0.0411],
                             [ 0.0526,  0.0393,  0.0918],
                             [-0.1345,  0.0750,  0.0859]],
     
                            [[-0.0985,  0.1466,  0.1349],
                             [-0.1461, -0.1742,  0.0941],
                             [-0.1502, -0.1813,  0.0864]],
     
                            [[-0.1039,  0.1179,  0.1499],
                             [-0.0366, -0.0120,  0.0951],
                             [ 0.0087,  0.1212, -0.0183]]],
     
     
                           [[[-0.1375,  0.0765, -0.0072],
                             [-0.0041,  0.0379, -0.0243],
                             [-0.1495,  0.1601,  0.1575]],
     
                            [[-0.0454,  0.1642,  0.0720],
                             [-0.0533,  0.0150,  0.0039],
                             [ 0.0194,  0.0113, -0.1194]],
     
                            [[ 0.0527, -0.0886,  0.0359],
                             [ 0.1595,  0.0526, -0.0048],
                             [-0.1790, -0.0458, -0.0324]]],
     
     
                           [[[-0.1638,  0.0942,  0.0686],
                             [-0.1082, -0.0675,  0.1892],
                             [-0.1347, -0.1247,  0.0739]],
     
                            [[ 0.0595,  0.1504, -0.1657],
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                             [-9.2320e-03, -3.3036e-02,  3.3196e-02],
                             [ 2.3940e-02,  2.0442e-02,  1.4183e-02]],
     
                            [[-2.7139e-02, -3.4129e-03, -1.0090e-02],
                             [ 1.3073e-02, -1.6998e-02, -4.7540e-02],
                             [-2.5758e-02, -1.9363e-02,  2.1905e-02]],
     
                            [[ 5.7593e-02,  1.5013e-02, -5.7894e-02],
                             [ 5.7964e-02, -2.3412e-02,  2.6955e-02],
                             [-3.9814e-02, -4.6015e-02, -5.3240e-02]]],
     
     
                           [[[ 5.8211e-02, -4.1118e-02,  2.7704e-02],
                             [ 5.7198e-02,  8.4165e-03, -5.1708e-02],
                             [ 3.1423e-02,  1.5026e-02,  3.5922e-02]],
     
                            [[ 8.8858e-03,  3.2818e-02,  5.4486e-02],
                             [-2.6636e-02,  2.2604e-02,  2.9531e-02],
                             [-1.0327e-03,  2.2348e-03,  2.4103e-02]],
     
                            [[ 3.8683e-02, -5.0057e-03,  5.0224e-02],
                             [ 3.5756e-02, -2.7295e-02, -2.2854e-02],
                             [-3.2043e-02, -3.2415e-02,  4.1034e-02]],
     
                            ...,
     
                            [[-1.9791e-02,  4.3243e-02, -3.5177e-02],
                             [ 2.4554e-02,  4.2845e-03,  4.8009e-02],
                             [ 2.4897e-03,  3.9550e-02, -3.0833e-02]],
     
                            [[ 4.5807e-02,  6.5845e-03,  9.3362e-05],
                             [-1.9411e-02, -2.9161e-02,  5.0828e-02],
                             [ 1.2028e-03,  2.1260e-02, -4.3710e-03]],
     
                            [[-4.8702e-02, -2.0571e-02, -3.5162e-02],
                             [-2.5856e-02, -2.6619e-02,  8.1867e-03],
                             [-2.7671e-02, -9.6651e-03, -5.3279e-02]]],
     
     
                           [[[-5.6432e-02,  2.3722e-02, -2.1750e-02],
                             [-4.8247e-03, -2.1226e-02, -1.0829e-02],
                             [-1.9523e-02, -1.8187e-02,  2.2772e-03]],
     
                            [[-1.0907e-02,  3.3984e-02, -1.2088e-02],
                             [-1.8657e-02, -4.8297e-02,  2.3614e-02],
                             [-3.9670e-02,  6.1733e-03, -2.9168e-02]],
     
                            [[-4.2112e-02,  2.8203e-02, -1.7385e-03],
                             [-2.5282e-02, -9.4592e-05,  6.5093e-03],
                             [-4.1745e-02,  4.3988e-03, -1.1622e-02]],
     
                            ...,
     
                            [[ 4.2991e-02,  1.6912e-02, -4.3689e-02],
                             [ 5.1871e-02,  4.8566e-02,  3.6205e-02],
                             [-3.2016e-02, -1.3596e-02, -2.7950e-02]],
     
                            [[-2.8307e-02, -4.0278e-02,  1.5087e-02],
                             [ 4.0443e-02, -3.5727e-02,  3.7196e-02],
                             [-1.4194e-02, -2.7319e-02, -5.1305e-02]],
     
                            [[-2.9962e-02,  2.4693e-02, -4.4912e-02],
                             [ 5.5890e-03,  4.6671e-02,  3.3599e-02],
                             [-3.9949e-02, -4.4716e-02,  2.2345e-02]]],
     
     
                           ...,
     
     
                           [[[ 3.1920e-02, -4.9932e-02, -1.0871e-02],
                             [-3.7500e-02,  4.1638e-02, -1.3246e-02],
                             [ 1.6447e-02, -5.6741e-02, -3.7524e-02]],
     
                            [[ 3.3903e-02, -3.1321e-02, -4.4877e-02],
                             [-2.2473e-02, -2.4225e-02,  4.5838e-02],
                             [-2.0069e-02,  3.8338e-02,  5.8010e-02]],
     
                            [[ 1.7602e-02, -5.2530e-02,  4.9331e-02],
                             [ 2.4509e-02,  2.3943e-02, -2.1774e-02],
                             [-5.7154e-02,  5.7090e-02,  3.7531e-02]],
     
                            ...,
     
                            [[ 2.8630e-02, -4.8644e-04, -5.3822e-02],
                             [-1.1102e-02,  4.8524e-02, -2.7142e-02],
                             [-5.3463e-02,  3.5607e-02, -1.1110e-02]],
     
                            [[ 4.7891e-02, -3.4098e-02,  3.6984e-02],
                             [ 3.9062e-02, -5.1119e-03, -3.3252e-02],
                             [ 5.5029e-02,  3.1092e-03, -5.9391e-03]],
     
                            [[ 9.6775e-03,  2.2903e-02, -1.5971e-02],
                             [-2.6969e-02,  8.5069e-04, -2.5744e-02],
                             [ 2.7311e-03, -2.2119e-02,  2.4367e-03]]],
     
     
                           [[[ 1.5206e-02, -5.3504e-02, -1.8013e-02],
                             [-3.0064e-02,  3.2887e-02,  1.7612e-02],
                             [-4.2775e-02, -2.7335e-02,  5.1532e-02]],
     
                            [[ 4.3325e-02,  1.2909e-03,  5.6831e-02],
                             [ 4.8283e-03, -4.4274e-02,  1.7624e-02],
                             [-7.2574e-03,  1.3743e-02, -5.1502e-02]],
     
                            [[-1.4299e-02, -3.0024e-02,  4.9578e-02],
                             [-4.6143e-02, -3.0686e-02, -5.0727e-02],
                             [ 5.4766e-02, -1.4012e-02,  3.3267e-02]],
     
                            ...,
     
                            [[ 5.2773e-02,  4.0294e-02, -2.6113e-02],
                             [-2.5069e-02,  4.3956e-02, -4.7841e-02],
                             [ 3.0924e-02,  1.5174e-02, -4.8323e-02]],
     
                            [[-2.0325e-02,  3.2666e-02,  2.5174e-02],
                             [ 5.3775e-03, -3.2712e-02, -5.2251e-02],
                             [-2.7426e-02, -4.5502e-04, -3.2174e-02]],
     
                            [[ 8.5780e-03, -5.2099e-02,  5.7285e-02],
                             [-5.0897e-02, -5.3995e-03,  4.2719e-02],
                             [-5.2257e-02, -7.9682e-03,  2.1848e-02]]],
     
     
                           [[[-2.8660e-02,  1.2392e-02,  2.9940e-02],
                             [-1.1170e-02, -1.7499e-02, -5.3951e-02],
                             [ 4.9738e-02,  2.7240e-02,  2.9588e-03]],
     
                            [[ 3.7728e-02, -3.1084e-02,  4.6459e-02],
                             [-1.4961e-02,  1.6951e-02, -1.3976e-02],
                             [ 2.5768e-02, -3.8991e-02, -2.8738e-02]],
     
                            [[ 1.6084e-02, -7.3174e-03, -4.5839e-02],
                             [-3.6029e-02,  1.5303e-02, -5.4380e-02],
                             [ 2.1913e-02, -4.4792e-02,  4.7973e-02]],
     
                            ...,
     
                            [[ 5.4945e-02, -4.5541e-02, -1.5806e-02],
                             [-4.5216e-02, -2.0338e-02,  3.3373e-02],
                             [-1.8431e-02,  4.3953e-02, -4.8196e-02]],
     
                            [[-3.6129e-02,  5.7705e-02, -1.2229e-02],
                             [-5.2801e-02,  7.0930e-03, -2.6721e-02],
                             [ 3.6720e-02,  7.5540e-04,  5.5401e-02]],
     
                            [[-4.0386e-02,  1.6714e-02,  2.9246e-02],
                             [ 4.7033e-02, -8.7923e-03,  5.1267e-02],
                             [-3.1211e-02, -2.7036e-02,  4.0346e-02]]]])),
                  ('conv4.bias',
                   tensor([-0.0368, -0.0535, -0.0568, -0.0364,  0.0167,  0.0255,  0.0031,  0.0562,
                           -0.0575,  0.0470, -0.0128,  0.0159, -0.0387,  0.0517, -0.0508, -0.0104,
                            0.0133, -0.0265,  0.0290,  0.0147, -0.0405,  0.0074,  0.0434, -0.0436,
                           -0.0295,  0.0417,  0.0324,  0.0316,  0.0143, -0.0312,  0.0463,  0.0441])),
                  ('fc1.weight',
                   tensor([[-0.0206, -0.0161, -0.0053,  ..., -0.0257, -0.0314, -0.0105],
                           [-0.0229,  0.0030,  0.0239,  ...,  0.0199, -0.0279,  0.0155]])),
                  ('fc1.bias', tensor([-0.0333, -0.0126]))])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 m2 = m1.state_dict()
 m2['fc1.bias'] = torch.Tensor([-0.5, -1.2])
 m1.load_state_dict(m2)
 m1.state_dict()
 ```
 
 %% Output
 
     OrderedDict([('conv1.weight',
                   tensor([[[[-0.1261,  0.1833, -0.1406],
                             [ 0.1324, -0.0685,  0.0938],
                             [ 0.0432,  0.1814, -0.0541]],
     
                            [[-0.1776, -0.1839, -0.0111],
                             [ 0.0888,  0.0888, -0.1344],
                             [-0.1838,  0.1737,  0.1584]],
     
                            [[ 0.0417,  0.1064, -0.0156],
                             [ 0.0667,  0.0856, -0.1746],
                             [ 0.0412,  0.1620,  0.0125]]],
     
     
                           [[[-0.0530, -0.1273, -0.0797],
                             [ 0.0422,  0.1135,  0.0475],
                             [-0.0244,  0.1691, -0.1383]],
     
                            [[ 0.0822, -0.1317, -0.1692],
                             [ 0.1373,  0.1388,  0.0103],
                             [-0.0481,  0.1105,  0.0631]],
     
                            [[-0.0352,  0.1259, -0.0530],
                             [-0.1394, -0.0281,  0.1844],
                             [ 0.0082,  0.1187,  0.0211]]],
     
     
                           [[[ 0.0987,  0.0788, -0.1126],
                             [ 0.1769,  0.0763, -0.1767],
                             [-0.0570,  0.1156,  0.1770]],
     
                            [[ 0.0643, -0.0024, -0.0625],
                             [ 0.0819,  0.0140, -0.1882],
                             [ 0.1325, -0.0632, -0.0202]],
     
                            [[ 0.0053,  0.1042, -0.0058],
                             [-0.1082, -0.1753,  0.1762],
                             [-0.0501,  0.1166,  0.0561]]],
     
     
                           [[[ 0.0358, -0.0685, -0.1278],
                             [ 0.0029, -0.1107,  0.1169],
                             [-0.1408,  0.1293,  0.1142]],
     
                            [[-0.0814,  0.0470,  0.0188],
                             [ 0.1538,  0.0137,  0.1128],
                             [-0.1597,  0.1432,  0.1370]],
     
                            [[ 0.1425,  0.1769, -0.0037],
                             [-0.1080, -0.0805, -0.0195],
                             [-0.1335, -0.1666,  0.1399]]],
     
     
                           [[[ 0.1117,  0.1918, -0.1666],
                             [-0.1392,  0.0086,  0.0172],
                             [-0.0721, -0.1711,  0.0344]],
     
                            [[ 0.1820, -0.0537, -0.0974],
                             [ 0.0366, -0.0710,  0.1273],
                             [ 0.1132, -0.1594,  0.0878]],
     
                            [[-0.0874, -0.0401,  0.1827],
                             [-0.0301,  0.1205, -0.0396],
                             [-0.1143, -0.1007,  0.1561]]],
     
     
                           [[[ 0.1522, -0.0012, -0.1785],
                             [-0.1833, -0.1828, -0.1643],
                             [-0.1765, -0.1757, -0.0608]],
     
                            [[-0.0684,  0.0521,  0.1137],
                             [-0.0028,  0.0616,  0.0758],
                             [-0.1736,  0.0667,  0.1229]],
     
                            [[ 0.1298, -0.1848, -0.1570],
                             [-0.1052, -0.1172, -0.1223],
                             [-0.1389, -0.0095, -0.0410]]],
     
     
                           [[[ 0.0213, -0.0975,  0.0964],
                             [ 0.0535, -0.0775,  0.0790],
                             [-0.1796, -0.1468,  0.1036]],
     
                            [[-0.0403,  0.0646, -0.0932],
                             [ 0.1779, -0.1616,  0.0644],
                             [-0.0508, -0.1158, -0.0592]],
     
                            [[-0.1644, -0.1327,  0.0817],
                             [ 0.0320, -0.0213, -0.0946],
                             [-0.1106,  0.1463, -0.1642]]],
     
     
                           [[[-0.0985, -0.1160, -0.0915],
                             [ 0.1857,  0.0806,  0.1761],
                             [-0.0817,  0.1095,  0.0896]],
     
                            [[-0.0660, -0.1680,  0.1833],
                             [ 0.0611,  0.0077, -0.0848],
                             [-0.1516,  0.1737,  0.0484]],
     
                            [[ 0.1434, -0.0732, -0.0904],
                             [ 0.0962,  0.1783,  0.0192],
                             [ 0.0915,  0.0006,  0.0334]]],
     
     
                           [[[-0.0047,  0.1807, -0.1798],
                             [-0.0164,  0.1119, -0.0805],
                             [ 0.1855, -0.0681, -0.0187]],
     
                            [[-0.0069,  0.0491, -0.1868],
                             [-0.1609, -0.0316,  0.0150],
                             [-0.1605,  0.1506, -0.0074]],
     
                            [[ 0.0851, -0.1732, -0.1777],
                             [ 0.0539, -0.0500, -0.1231],
                             [ 0.1654,  0.0342, -0.1904]]],
     
     
                           [[[ 0.0476,  0.0284,  0.1212],
                             [-0.1603, -0.1924,  0.0144],
                             [ 0.0076, -0.0928, -0.1645]],
     
                            [[ 0.0215,  0.1845, -0.1034],
                             [ 0.1574, -0.1577, -0.0438],
                             [-0.1360, -0.0601, -0.1693]],
     
                            [[-0.0720,  0.0619,  0.1405],
                             [ 0.0699, -0.1288,  0.0041],
                             [-0.0381, -0.1697, -0.1568]]],
     
     
                           [[[-0.1599,  0.1231, -0.1034],
                             [-0.0314,  0.0105, -0.1449],
                             [-0.0172, -0.0781,  0.0839]],
     
                            [[-0.0676,  0.1185, -0.1559],
                             [-0.1053, -0.1306,  0.1820],
                             [ 0.1584, -0.1370,  0.1828]],
     
                            [[ 0.0658,  0.1412, -0.0537],
                             [-0.1230, -0.1411, -0.0011],
                             [-0.1318, -0.0458,  0.1838]]],
     
     
                           [[[-0.0268,  0.1747, -0.1037],
                             [ 0.0515, -0.0228, -0.1024],
                             [-0.1543, -0.0643, -0.0100]],
     
                            [[-0.1572, -0.1530,  0.0026],
                             [ 0.1463, -0.1233,  0.0470],
                             [-0.1595, -0.1108, -0.0654]],
     
                            [[-0.0521, -0.0094,  0.1544],
                             [-0.0505, -0.0332,  0.0048],
                             [ 0.0735,  0.1350,  0.0690]]],
     
     
                           [[[ 0.0025,  0.0724,  0.0930],
                             [-0.1885,  0.0475,  0.1100],
                             [-0.1622,  0.0087, -0.0030]],
     
                            [[ 0.1032, -0.1425, -0.0620],
                             [ 0.1515, -0.0736, -0.1888],
                             [-0.1246,  0.1424, -0.0491]],
     
                            [[ 0.1759, -0.1616,  0.1198],
                             [-0.1103,  0.1032,  0.1727],
                             [-0.0601,  0.1635,  0.0034]]],
     
     
                           [[[ 0.0301,  0.1517,  0.0657],
                             [-0.1368, -0.1165,  0.1193],
                             [-0.0962,  0.1451,  0.1099]],
     
                            [[ 0.1646, -0.1860, -0.1187],
                             [-0.1367, -0.0911,  0.1337],
                             [-0.0926, -0.0524, -0.0672]],
     
                            [[-0.1509, -0.1231, -0.0855],
                             [ 0.1808, -0.0713,  0.0410],
                             [-0.0621, -0.0506,  0.1871]]],
     
     
                           [[[ 0.0888, -0.0874, -0.0826],
                             [ 0.0416, -0.0961,  0.0603],
                             [ 0.1455,  0.0050,  0.0318]],
     
                            [[-0.1633,  0.0070, -0.1537],
                             [-0.0109,  0.1602, -0.0463],
                             [-0.0423, -0.0147, -0.1045]],
     
                            [[ 0.1640, -0.0997, -0.1662],
                             [-0.1074,  0.1549, -0.1905],
                             [-0.1708,  0.1624,  0.0219]]],
     
     
                           [[[ 0.0824, -0.1376,  0.1086],
                             [ 0.0836,  0.0135,  0.0351],
                             [-0.1518,  0.0784, -0.1708]],
     
                            [[-0.1636, -0.1571,  0.1032],
                             [-0.1152,  0.0274, -0.1022],
                             [-0.0956, -0.1606, -0.1615]],
     
                            [[ 0.1307,  0.0419,  0.1924],
                             [-0.0599, -0.1296, -0.0448],
                             [ 0.0363,  0.0377, -0.0460]]],
     
     
                           [[[-0.1685, -0.1277, -0.0465],
                             [ 0.0922, -0.1011,  0.0742],
                             [ 0.0053, -0.1456,  0.0135]],
     
                            [[ 0.1341,  0.0131,  0.1281],
                             [-0.1020,  0.1069, -0.0631],
                             [-0.0439, -0.1189, -0.1822]],
     
                            [[ 0.1624, -0.1253,  0.0302],
                             [ 0.0709,  0.0767,  0.1453],
                             [ 0.0203,  0.1603, -0.1720]]],
     
     
                           [[[-0.1550,  0.1513, -0.1003],
                             [ 0.0370,  0.0367, -0.0233],
                             [ 0.0916, -0.0871,  0.1579]],
     
                            [[-0.1900,  0.0314,  0.0865],
                             [-0.0197,  0.0296, -0.0048],
                             [ 0.0846,  0.1543, -0.0770]],
     
                            [[-0.0016, -0.0978,  0.1826],
                             [-0.0477,  0.0689,  0.1079],
                             [ 0.0400,  0.0880,  0.1674]]],
     
     
                           [[[ 0.0145, -0.0447, -0.1742],
                             [ 0.0394,  0.0127, -0.1172],
                             [ 0.1330, -0.1207,  0.0326]],
     
                            [[-0.0155, -0.1602,  0.0023],
                             [ 0.0789,  0.1648,  0.1781],
                             [-0.1468, -0.0481, -0.1260]],
     
                            [[-0.0139,  0.0848, -0.0536],
                             [-0.1581,  0.1130,  0.0717],
                             [ 0.0275, -0.0006, -0.0049]]],
     
     
                           [[[-0.0199,  0.0032, -0.1246],
                             [ 0.0479,  0.1418, -0.1295],
                             [-0.1646, -0.1139, -0.1018]],
     
                            [[ 0.1475,  0.1413, -0.0354],
                             [ 0.0612, -0.1652,  0.0801],
                             [-0.1306, -0.0165,  0.1733]],
     
                            [[ 0.1527,  0.0911, -0.1906],
                             [-0.1152,  0.1737,  0.0436],
                             [-0.0213, -0.0314, -0.0319]]],
     
     
                           [[[-0.0003, -0.0546, -0.1255],
                             [ 0.0914, -0.1414,  0.0542],
                             [ 0.1139,  0.0132,  0.0815]],
     
                            [[-0.0042,  0.0541,  0.1456],
                             [ 0.0509, -0.0790,  0.0272],
                             [ 0.1419,  0.0992, -0.1448]],
     
                            [[ 0.0496,  0.0013,  0.0838],
                             [-0.0662,  0.0315, -0.1168],
                             [-0.0069, -0.1503,  0.0729]]],
     
     
                           [[[ 0.1866,  0.1329, -0.0560],
                             [ 0.0026,  0.1533,  0.0326],
                             [-0.1161, -0.0323,  0.0053]],
     
                            [[-0.0243, -0.1823, -0.1657],
                             [-0.0107, -0.0832,  0.0029],
                             [ 0.0981,  0.1241, -0.1788]],
     
                            [[-0.0400, -0.0577, -0.0757],
                             [-0.0584,  0.0176, -0.1019],
                             [-0.1828,  0.1589, -0.0312]]],
     
     
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                             [ 0.0483,  0.0121, -0.0149],
                             [ 0.0328,  0.0201,  0.0402]],
     
                            [[ 0.0463,  0.0157, -0.0332],
                             [ 0.0150,  0.0479,  0.0461],
                             [ 0.0275,  0.0506, -0.0466]]],
     
     
                           [[[-0.0478,  0.0274,  0.0500],
                             [-0.0394,  0.0032, -0.0496],
                             [ 0.0381,  0.0391,  0.0330]],
     
                            [[ 0.0184, -0.0560, -0.0345],
                             [-0.0459, -0.0215,  0.0452],
                             [ 0.0049,  0.0537,  0.0544]],
     
                            [[-0.0413, -0.0084,  0.0585],
                             [ 0.0338, -0.0067, -0.0113],
                             [-0.0187, -0.0234, -0.0525]],
     
                            ...,
     
                            [[-0.0389,  0.0325, -0.0538],
                             [ 0.0118,  0.0509,  0.0352],
                             [-0.0351, -0.0341, -0.0506]],
     
                            [[ 0.0136, -0.0349,  0.0082],
                             [ 0.0358, -0.0211,  0.0537],
                             [-0.0183,  0.0390, -0.0267]],
     
                            [[-0.0219, -0.0145, -0.0351],
                             [ 0.0556,  0.0033, -0.0030],
                             [ 0.0075, -0.0425, -0.0365]]]])),
                  ('conv3.bias',
                   tensor([ 0.0304,  0.0099, -0.0004,  0.0334,  0.0301,  0.0491,  0.0530, -0.0432,
                           -0.0127, -0.0549, -0.0419,  0.0159, -0.0284,  0.0295, -0.0148,  0.0275,
                            0.0554, -0.0056,  0.0389, -0.0264, -0.0383,  0.0126,  0.0320,  0.0312,
                            0.0018,  0.0560, -0.0329, -0.0155, -0.0391, -0.0539, -0.0571, -0.0254])),
                  ('conv4.weight',
                   tensor([[[[-3.8911e-02, -3.4220e-02,  4.2567e-03],
                             [-4.5321e-02, -5.2531e-02, -8.1722e-03],
                             [-2.2638e-02,  4.4213e-02,  5.6989e-02]],
     
                            [[ 1.8417e-03, -1.4453e-02,  4.9892e-02],
                             [ 5.7762e-02,  9.6610e-03, -3.9509e-02],
                             [ 3.3795e-02,  5.0409e-02, -5.8834e-02]],
     
                            [[ 4.6645e-03, -1.6286e-02,  4.3410e-02],
                             [-3.4043e-02, -2.2207e-02,  4.0967e-02],
                             [ 5.3004e-02, -2.2756e-02, -6.7993e-03]],
     
                            ...,
     
                            [[ 1.1741e-02, -5.5062e-02, -3.3625e-02],
                             [-9.2320e-03, -3.3036e-02,  3.3196e-02],
                             [ 2.3940e-02,  2.0442e-02,  1.4183e-02]],
     
                            [[-2.7139e-02, -3.4129e-03, -1.0090e-02],
                             [ 1.3073e-02, -1.6998e-02, -4.7540e-02],
                             [-2.5758e-02, -1.9363e-02,  2.1905e-02]],
     
                            [[ 5.7593e-02,  1.5013e-02, -5.7894e-02],
                             [ 5.7964e-02, -2.3412e-02,  2.6955e-02],
                             [-3.9814e-02, -4.6015e-02, -5.3240e-02]]],
     
     
                           [[[ 5.8211e-02, -4.1118e-02,  2.7704e-02],
                             [ 5.7198e-02,  8.4165e-03, -5.1708e-02],
                             [ 3.1423e-02,  1.5026e-02,  3.5922e-02]],
     
                            [[ 8.8858e-03,  3.2818e-02,  5.4486e-02],
                             [-2.6636e-02,  2.2604e-02,  2.9531e-02],
                             [-1.0327e-03,  2.2348e-03,  2.4103e-02]],
     
                            [[ 3.8683e-02, -5.0057e-03,  5.0224e-02],
                             [ 3.5756e-02, -2.7295e-02, -2.2854e-02],
                             [-3.2043e-02, -3.2415e-02,  4.1034e-02]],
     
                            ...,
     
                            [[-1.9791e-02,  4.3243e-02, -3.5177e-02],
                             [ 2.4554e-02,  4.2845e-03,  4.8009e-02],
                             [ 2.4897e-03,  3.9550e-02, -3.0833e-02]],
     
                            [[ 4.5807e-02,  6.5845e-03,  9.3362e-05],
                             [-1.9411e-02, -2.9161e-02,  5.0828e-02],
                             [ 1.2028e-03,  2.1260e-02, -4.3710e-03]],
     
                            [[-4.8702e-02, -2.0571e-02, -3.5162e-02],
                             [-2.5856e-02, -2.6619e-02,  8.1867e-03],
                             [-2.7671e-02, -9.6651e-03, -5.3279e-02]]],
     
     
                           [[[-5.6432e-02,  2.3722e-02, -2.1750e-02],
                             [-4.8247e-03, -2.1226e-02, -1.0829e-02],
                             [-1.9523e-02, -1.8187e-02,  2.2772e-03]],
     
                            [[-1.0907e-02,  3.3984e-02, -1.2088e-02],
                             [-1.8657e-02, -4.8297e-02,  2.3614e-02],
                             [-3.9670e-02,  6.1733e-03, -2.9168e-02]],
     
                            [[-4.2112e-02,  2.8203e-02, -1.7385e-03],
                             [-2.5282e-02, -9.4592e-05,  6.5093e-03],
                             [-4.1745e-02,  4.3988e-03, -1.1622e-02]],
     
                            ...,
     
                            [[ 4.2991e-02,  1.6912e-02, -4.3689e-02],
                             [ 5.1871e-02,  4.8566e-02,  3.6205e-02],
                             [-3.2016e-02, -1.3596e-02, -2.7950e-02]],
     
                            [[-2.8307e-02, -4.0278e-02,  1.5087e-02],
                             [ 4.0443e-02, -3.5727e-02,  3.7196e-02],
                             [-1.4194e-02, -2.7319e-02, -5.1305e-02]],
     
                            [[-2.9962e-02,  2.4693e-02, -4.4912e-02],
                             [ 5.5890e-03,  4.6671e-02,  3.3599e-02],
                             [-3.9949e-02, -4.4716e-02,  2.2345e-02]]],
     
     
                           ...,
     
     
                           [[[ 3.1920e-02, -4.9932e-02, -1.0871e-02],
                             [-3.7500e-02,  4.1638e-02, -1.3246e-02],
                             [ 1.6447e-02, -5.6741e-02, -3.7524e-02]],
     
                            [[ 3.3903e-02, -3.1321e-02, -4.4877e-02],
                             [-2.2473e-02, -2.4225e-02,  4.5838e-02],
                             [-2.0069e-02,  3.8338e-02,  5.8010e-02]],
     
                            [[ 1.7602e-02, -5.2530e-02,  4.9331e-02],
                             [ 2.4509e-02,  2.3943e-02, -2.1774e-02],
                             [-5.7154e-02,  5.7090e-02,  3.7531e-02]],
     
                            ...,
     
                            [[ 2.8630e-02, -4.8644e-04, -5.3822e-02],
                             [-1.1102e-02,  4.8524e-02, -2.7142e-02],
                             [-5.3463e-02,  3.5607e-02, -1.1110e-02]],
     
                            [[ 4.7891e-02, -3.4098e-02,  3.6984e-02],
                             [ 3.9062e-02, -5.1119e-03, -3.3252e-02],
                             [ 5.5029e-02,  3.1092e-03, -5.9391e-03]],
     
                            [[ 9.6775e-03,  2.2903e-02, -1.5971e-02],
                             [-2.6969e-02,  8.5069e-04, -2.5744e-02],
                             [ 2.7311e-03, -2.2119e-02,  2.4367e-03]]],
     
     
                           [[[ 1.5206e-02, -5.3504e-02, -1.8013e-02],
                             [-3.0064e-02,  3.2887e-02,  1.7612e-02],
                             [-4.2775e-02, -2.7335e-02,  5.1532e-02]],
     
                            [[ 4.3325e-02,  1.2909e-03,  5.6831e-02],
                             [ 4.8283e-03, -4.4274e-02,  1.7624e-02],
                             [-7.2574e-03,  1.3743e-02, -5.1502e-02]],
     
                            [[-1.4299e-02, -3.0024e-02,  4.9578e-02],
                             [-4.6143e-02, -3.0686e-02, -5.0727e-02],
                             [ 5.4766e-02, -1.4012e-02,  3.3267e-02]],
     
                            ...,
     
                            [[ 5.2773e-02,  4.0294e-02, -2.6113e-02],
                             [-2.5069e-02,  4.3956e-02, -4.7841e-02],
                             [ 3.0924e-02,  1.5174e-02, -4.8323e-02]],
     
                            [[-2.0325e-02,  3.2666e-02,  2.5174e-02],
                             [ 5.3775e-03, -3.2712e-02, -5.2251e-02],
                             [-2.7426e-02, -4.5502e-04, -3.2174e-02]],
     
                            [[ 8.5780e-03, -5.2099e-02,  5.7285e-02],
                             [-5.0897e-02, -5.3995e-03,  4.2719e-02],
                             [-5.2257e-02, -7.9682e-03,  2.1848e-02]]],
     
     
                           [[[-2.8660e-02,  1.2392e-02,  2.9940e-02],
                             [-1.1170e-02, -1.7499e-02, -5.3951e-02],
                             [ 4.9738e-02,  2.7240e-02,  2.9588e-03]],
     
                            [[ 3.7728e-02, -3.1084e-02,  4.6459e-02],
                             [-1.4961e-02,  1.6951e-02, -1.3976e-02],
                             [ 2.5768e-02, -3.8991e-02, -2.8738e-02]],
     
                            [[ 1.6084e-02, -7.3174e-03, -4.5839e-02],
                             [-3.6029e-02,  1.5303e-02, -5.4380e-02],
                             [ 2.1913e-02, -4.4792e-02,  4.7973e-02]],
     
                            ...,
     
                            [[ 5.4945e-02, -4.5541e-02, -1.5806e-02],
                             [-4.5216e-02, -2.0338e-02,  3.3373e-02],
                             [-1.8431e-02,  4.3953e-02, -4.8196e-02]],
     
                            [[-3.6129e-02,  5.7705e-02, -1.2229e-02],
                             [-5.2801e-02,  7.0930e-03, -2.6721e-02],
                             [ 3.6720e-02,  7.5540e-04,  5.5401e-02]],
     
                            [[-4.0386e-02,  1.6714e-02,  2.9246e-02],
                             [ 4.7033e-02, -8.7923e-03,  5.1267e-02],
                             [-3.1211e-02, -2.7036e-02,  4.0346e-02]]]])),
                  ('conv4.bias',
                   tensor([-0.0368, -0.0535, -0.0568, -0.0364,  0.0167,  0.0255,  0.0031,  0.0562,
                           -0.0575,  0.0470, -0.0128,  0.0159, -0.0387,  0.0517, -0.0508, -0.0104,
                            0.0133, -0.0265,  0.0290,  0.0147, -0.0405,  0.0074,  0.0434, -0.0436,
                           -0.0295,  0.0417,  0.0324,  0.0316,  0.0143, -0.0312,  0.0463,  0.0441])),
                  ('fc1.weight',
                   tensor([[-0.0206, -0.0161, -0.0053,  ..., -0.0257, -0.0314, -0.0105],
                           [-0.0229,  0.0030,  0.0239,  ...,  0.0199, -0.0279,  0.0155]])),
                  ('fc1.bias', tensor([-0.5000, -1.2000]))])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 o1.state_dict()
 ```
 
 %% Output
 
     {'state': {},
      'param_groups': [{'lr': 0.6,
        'momentum': 0,
        'dampening': 0,
        'weight_decay': 0,
        'nesterov': False,
        'params': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}]}
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 with torch.no_grad():
     o1.param_groups[0]["params"][0].copy_(torch.zeros(1,))
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 o1.param_groups
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 m1.state_dict()
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 import torch
 loss = getattr(torch.nn.functional, 'nll_loss')
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 loss
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 %matplotlib inline
 
 from decentralizepy.node.Node import Node
 from decentralizepy.graphs.SmallWorld import SmallWorld
 from decentralizepy.graphs.Graph import Graph
 from decentralizepy.mappings.Linear import Linear
 from torch import multiprocessing as mp
 import torch
 import logging
 
 from localconfig import LocalConfig
 
 def read_ini(file_path):
     config = LocalConfig(file_path)
     for section in config:
         print("Section: ", section)
         for key, value in config.items(section):
             print((key, value))
     print(dict(config.items('DATASET')))
     return config
 
 config = read_ini("config.ini")
 my_config = dict()
 for section in config:
     my_config[section] = dict(config.items(section))
 
 #f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])
 g = Graph()
 g.read_graph_from_file("36_nodes.edges", "edges")
 l = Linear(1, 36)
 
 #Node(0, 0, l, g, my_config, 20, "results", logging.DEBUG)
 
 mp.spawn(fn = Node, nprocs = g.n_procs, args=[0,l,g,my_config,20,"results",logging.INFO])
 
 # mp.spawn(fn = Node, args = [l, g, config, 10, "results", logging.DEBUG], nprocs=2)
 ```
 
 %% Output
 
     Section:  GRAPH
     ('package', 'decentralizepy.graphs.SmallWorld')
     ('graph_class', 'SmallWorld')
     Section:  DATASET
     ('dataset_package', 'decentralizepy.datasets.Femnist')
     ('dataset_class', 'Femnist')
     ('model_class', 'CNN')
     ('n_procs', 36)
     ('train_dir', 'leaf/data/femnist/per_user_data/train')
     ('test_dir', 'leaf/data/femnist/data/test')
     ('sizes', '')
     Section:  OPTIMIZER_PARAMS
     ('optimizer_package', 'torch.optim')
     ('optimizer_class', 'Adam')
     ('lr', 0.01)
     Section:  TRAIN_PARAMS
     ('training_package', 'decentralizepy.training.Training')
     ('training_class', 'Training')
     ('epochs_per_round', 1)
     ('batch_size', 1024)
     ('shuffle', True)
     ('loss_package', 'torch.nn')
     ('loss_class', 'CrossEntropyLoss')
     Section:  COMMUNICATION
     ('comm_package', 'decentralizepy.communication.TCP')
     ('comm_class', 'TCP')
     ('addresses_filepath', 'ip_addr.json')
     Section:  SHARING
     ('sharing_package', 'decentralizepy.sharing.Sharing')
     ('sharing_class', 'Sharing')
     {'dataset_package': 'decentralizepy.datasets.Femnist', 'dataset_class': 'Femnist', 'model_class': 'CNN', 'n_procs': 36, 'train_dir': 'leaf/data/femnist/per_user_data/train', 'test_dir': 'leaf/data/femnist/data/test', 'sizes': ''}
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.mappings.Linear import Linear
 from testing import f
 from torch import multiprocessing as mp
 
 l = Linear(1, 2)
 mp.spawn(fn = f, nprocs = 2, args = [0, 2, "ip_addr.json", l])
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.datasets.Celeba import Celeba
 
 f = Celeba(0,0,None)
 
 f.file_per_user('/home/risharma/leaf/data/celeba/data/train','/home/risharma/leaf/data/celeba/per_user_data/train')
 ```
 
 %% Output
 
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 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.datasets.Femnist import Femnist
 
 f = Femnist()
 
 f.file_per_user('../leaf/data/femnist/data/train','../leaf/data/femnist/per_user_data/train')
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 a = set()
 a.update([2, 3, 4, 5])
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 a
 ```
 
 %% Output
 
     {2, 3, 4, 5}
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 print(*a)
 ```
 
 %% Output
 
     2 3 4 5
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.graphs.FullyConnected import FullyConnected
 
 s = FullyConnected(96)
 s.write_graph_to_file('96_node_fullyConnected.edges')
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.graphs.SmallWorld import SmallWorld
 
 s1 = SmallWorld(96, 2, 1.0)
 s1.write_graph_to_file('96_nodes_random1.edges')
 s2 = SmallWorld(96, 2, 1.0)
 s2.write_graph_to_file('96_nodes_random2.edges')
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 import sys
 sys.argv
 ```
 
 %% Output
 
     ['/home/risharma/miniconda3/envs/decpy/lib/python3.9/site-packages/ipykernel_launcher.py',
      '--ip=127.0.0.1',
      '--stdin=9008',
      '--control=9006',
      '--hb=9005',
      '--Session.signature_scheme="hmac-sha256"',
      '--Session.key=b"eac5d2f8-c460-45f1-a268-1e4b46a6efd6"',
      '--shell=9007',
      '--transport="tcp"',
      '--iopub=9009',
      '--f=/tmp/tmp-21212479paJaUBJBN84.json']
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 import torch
 from decentralizepy.datasets.Femnist import CNN
 m1 = CNN()
 o1 = torch.optim.SGD(m1.parameters(), 0.6)
 #print("m1_parameters: ", {k:v.data for k, v in zip(m1.state_dict(), m1.parameters())})
 #print("m1_state_dict: ", m1.state_dict())
 #print("o1_state_dict: ", o1.state_dict())
 tensors_to_cat = [v.data for _, v in m1.state_dict().items()]
 print("tensors to cat: ", tensors_to_cat)
 ```
 
 %% Output
 
     tensors to cat:  [tensor([[[[-0.0754, -0.1052,  0.0336,  0.0976, -0.1882],
               [ 0.1723,  0.0860, -0.0557, -0.1182,  0.1845],
               [-0.1112,  0.0554, -0.0190,  0.1264, -0.0851],
               [-0.0788,  0.1281, -0.0477,  0.1910,  0.0089],
               [-0.1205, -0.0230, -0.0574,  0.1633, -0.1101]]],
     
     
             [[[ 0.0271, -0.1426, -0.0381,  0.1128,  0.0396],
               [-0.0023,  0.1021, -0.0483, -0.0167,  0.0090],
               [-0.0645, -0.1863, -0.1494, -0.0473,  0.1572],
               [-0.0027, -0.0923,  0.0186,  0.0787,  0.1404],
               [ 0.0714,  0.0434,  0.1853,  0.1262,  0.0502]]],
     
     
             [[[-0.1708,  0.0628, -0.1877, -0.0879, -0.1190],
               [-0.0111,  0.0684, -0.1659, -0.1297,  0.0684],
               [ 0.1629, -0.1196, -0.0096,  0.1854, -0.0721],
               [ 0.1122, -0.0325,  0.1887, -0.0685,  0.1441],
               [ 0.1734,  0.0720, -0.0941, -0.0536,  0.0355]]],
     
     
             [[[-0.1465, -0.0899,  0.1122,  0.0323, -0.1287],
               [-0.1237,  0.0852, -0.1984, -0.0995,  0.0734],
               [ 0.0521, -0.1664, -0.0875,  0.0643, -0.1166],
               [ 0.1862,  0.1754, -0.0282,  0.1074, -0.1337],
               [ 0.1779,  0.1883,  0.1376, -0.1518, -0.1619]]],
     
     
             [[[ 0.0158, -0.1902,  0.0607,  0.1062,  0.0373],
               [-0.1737, -0.1112,  0.0597, -0.1030, -0.0897],
               [ 0.0892,  0.1166, -0.0127,  0.1859, -0.1859],
               [ 0.1935, -0.1200,  0.0472, -0.0144, -0.0219],
               [-0.0596, -0.0255, -0.1733,  0.1746,  0.1136]]],
     
     
             [[[-0.1769,  0.0885,  0.0706,  0.1742,  0.1178],
               [-0.1831,  0.0380, -0.0769, -0.1068,  0.1958],
               [-0.0166, -0.0059,  0.0663,  0.0610,  0.0321],
               [-0.1393, -0.1884, -0.0678, -0.1400,  0.1042],
               [ 0.0653, -0.1733,  0.0744,  0.1278,  0.0482]]],
     
     
             [[[ 0.0935, -0.0115, -0.1991,  0.1451, -0.1708],
               [ 0.1979, -0.0709,  0.1771,  0.0486,  0.1292],
               [-0.1624,  0.0661, -0.0004, -0.0079,  0.1195],
               [ 0.0655,  0.1849,  0.1179,  0.1520, -0.0723],
               [ 0.1374,  0.1547,  0.1871,  0.1924,  0.0539]]],
     
     
             [[[-0.1602,  0.1380,  0.1321, -0.0722, -0.0291],
               [ 0.0820, -0.0490, -0.1345,  0.1719,  0.1306],
               [-0.0183,  0.0615,  0.1189, -0.0707, -0.0696],
               [ 0.0017, -0.1185, -0.0766,  0.1017, -0.0518],
               [-0.1316, -0.1323, -0.0135,  0.0103, -0.1509]]],
     
     
             [[[-0.1189, -0.1300, -0.1908, -0.0222,  0.0017],
               [-0.0644,  0.0996,  0.0454,  0.0198, -0.1608],
               [-0.0614, -0.0721, -0.0223, -0.0746, -0.0081],
               [-0.0133,  0.1326,  0.1275, -0.0954,  0.0281],
               [ 0.1004, -0.0435, -0.1027,  0.1867,  0.0034]]],
     
     
             [[[-0.0022, -0.0830, -0.0399,  0.1039, -0.1230],
               [-0.0067,  0.1032,  0.0895,  0.0679,  0.0133],
               [ 0.0148, -0.1275,  0.0391, -0.0173,  0.1621],
               [ 0.0532, -0.1873, -0.1499,  0.0605, -0.0016],
               [ 0.0386, -0.0360,  0.0255, -0.0580, -0.1954]]],
     
     
             [[[-0.0186, -0.1922, -0.1356,  0.1696,  0.0111],
               [ 0.1121, -0.0990,  0.0789, -0.0053,  0.0729],
               [-0.0565, -0.0405,  0.0559,  0.1065, -0.1723],
               [ 0.0569, -0.0432, -0.0484,  0.1533,  0.0421],
               [-0.0369,  0.1050,  0.1623, -0.1503,  0.0680]]],
     
     
             [[[-0.0028, -0.0167,  0.0598,  0.0854, -0.1996],
               [ 0.0548, -0.0586,  0.1987, -0.0415, -0.0477],
               [ 0.1172, -0.0924,  0.1351,  0.1319,  0.0983],
               [ 0.0756,  0.1345,  0.1360, -0.0779, -0.1169],
               [ 0.1524,  0.0208,  0.0672,  0.1320,  0.1958]]],
     
     
             [[[-0.0794, -0.1947,  0.0410,  0.1480, -0.0168],
               [ 0.0836, -0.0750, -0.0903, -0.1946,  0.0676],
               [-0.1202,  0.0276,  0.0058, -0.1465,  0.1545],
               [-0.1914,  0.0926, -0.0799, -0.0715, -0.0607],
               [ 0.1624,  0.1929, -0.1532, -0.0007, -0.1312]]],
     
     
             [[[ 0.0281, -0.0407, -0.0482,  0.0492,  0.0039],
               [-0.0752, -0.0298,  0.0764, -0.0738, -0.0705],
               [ 0.1404, -0.1254,  0.1613, -0.0501, -0.1432],
               [ 0.0132, -0.0170, -0.0971, -0.0117,  0.1406],
               [ 0.0655, -0.1970,  0.1782,  0.0665,  0.1174]]],
     
     
             [[[ 0.1879,  0.0858,  0.0340,  0.1138, -0.0841],
               [ 0.1503, -0.1680,  0.0618,  0.1971, -0.0156],
               [-0.1211, -0.0485,  0.0675, -0.0536,  0.0046],
               [ 0.1134,  0.1310, -0.0966, -0.1924, -0.0812],
               [-0.0800, -0.0553,  0.1818, -0.0217,  0.0107]]],
     
     
             [[[ 0.1118,  0.1368,  0.0269, -0.0690,  0.0168],
               [-0.1371, -0.1983,  0.0016, -0.0094,  0.0502],
               [-0.0219,  0.1939, -0.1821,  0.0988, -0.1016],
               [-0.0063,  0.0789,  0.1037, -0.0646, -0.1694],
               [-0.0464,  0.0624,  0.0681, -0.1855,  0.1219]]],
     
     
             [[[ 0.0640,  0.1420, -0.0144, -0.0606,  0.0963],
               [ 0.1349,  0.0906,  0.0826, -0.0385, -0.1635],
               [ 0.0278,  0.1875,  0.1559,  0.1424,  0.0878],
               [-0.0593, -0.0847, -0.0458,  0.1402,  0.0160],
               [ 0.1887, -0.1220, -0.0316, -0.0892, -0.0401]]],
     
     
             [[[-0.1103, -0.0884,  0.0775, -0.0338, -0.1245],
               [-0.1848,  0.0657,  0.0945,  0.0677, -0.1089],
               [ 0.0585, -0.0791, -0.0535,  0.0527, -0.0741],
               [ 0.1146,  0.1908, -0.0167, -0.0033, -0.1418],
               [-0.1011,  0.0829, -0.1472, -0.1310, -0.0991]]],
     
     
             [[[ 0.0865,  0.1608,  0.0168, -0.1126, -0.0889],
               [-0.1322,  0.1052,  0.1285,  0.0574,  0.1983],
               [ 0.0788,  0.0125, -0.0781, -0.0303, -0.1089],
               [-0.1860, -0.1878,  0.0608,  0.0402, -0.0390],
               [-0.1797, -0.1828,  0.0475, -0.1754,  0.0657]]],
     
     
             [[[-0.0632,  0.1015, -0.1493, -0.0855,  0.1822],
               [-0.1492,  0.0236, -0.1418, -0.1826, -0.1978],
               [ 0.0854, -0.1503,  0.1953, -0.0668, -0.1042],
               [ 0.1537, -0.0573,  0.0178,  0.1350,  0.1823],
               [-0.1131, -0.0838, -0.1445, -0.1476, -0.0288]]],
     
     
             [[[ 0.1445,  0.1748,  0.0146, -0.0807, -0.1905],
               [-0.1474, -0.0938,  0.0965,  0.0233,  0.0757],
               [-0.1257, -0.1316, -0.0105,  0.1447, -0.1200],
               [ 0.0921, -0.0332, -0.1153, -0.0260,  0.0640],
               [-0.1528,  0.1237,  0.1956,  0.0167,  0.0205]]],
     
     
             [[[-0.0236, -0.1264, -0.0342, -0.1199, -0.1381],
               [-0.0895, -0.0116, -0.1108,  0.0994,  0.0744],
               [-0.1190, -0.1275, -0.1097,  0.0678, -0.1547],
               [-0.1405,  0.0500, -0.1195,  0.0917, -0.0952],
               [-0.1799, -0.1516,  0.0956, -0.0402,  0.0426]]],
     
     
             [[[ 0.0556,  0.1145,  0.0388,  0.0917, -0.0881],
               [-0.0933,  0.0580,  0.1433, -0.1127, -0.0142],
               [-0.0014,  0.0792, -0.0451, -0.0696,  0.0486],
               [ 0.0006,  0.0264, -0.1252,  0.0796,  0.0561],
               [-0.0203, -0.0664, -0.0443,  0.1173, -0.0193]]],
     
     
             [[[-0.0796, -0.1144, -0.0728,  0.0867, -0.1342],
               [-0.0150, -0.0840, -0.0134,  0.0634,  0.1743],
               [-0.0773, -0.0827, -0.0470, -0.0879,  0.0346],
               [ 0.0642,  0.1078,  0.0373,  0.1683,  0.0433],
               [-0.0024,  0.1508, -0.1980,  0.0808, -0.1028]]],
     
     
             [[[-0.1358, -0.1896, -0.1495,  0.1018,  0.0048],
               [-0.1030, -0.1340,  0.0090, -0.0781,  0.0415],
               [-0.1069,  0.0658, -0.0144,  0.1455,  0.1006],
               [-0.0209,  0.0757, -0.1481, -0.0273, -0.0880],
               [ 0.0859, -0.1996, -0.1787, -0.1869, -0.0310]]],
     
     
             [[[-0.1005,  0.1260, -0.1949,  0.1433,  0.0845],
               [ 0.0336,  0.1110, -0.0389,  0.0584, -0.0470],
               [ 0.0333,  0.0861, -0.0371,  0.1471, -0.0385],
               [ 0.1889,  0.1999,  0.1643,  0.0767, -0.1738],
               [-0.1423, -0.0945, -0.0881,  0.0847, -0.0170]]],
     
     
             [[[-0.1417,  0.0120, -0.1466, -0.1825, -0.1018],
               [ 0.1989, -0.1077, -0.1263, -0.0141,  0.0296],
               [ 0.0414,  0.1355, -0.1630, -0.1348,  0.1382],
               [ 0.0589,  0.1278,  0.0218,  0.1217,  0.1369],
               [ 0.0659,  0.1200,  0.0031,  0.0240, -0.1200]]],
     
     
             [[[ 0.1293, -0.1227, -0.0351, -0.1777,  0.0515],
               [-0.1087,  0.0541,  0.0684,  0.0929, -0.1989],
               [ 0.0596, -0.0794, -0.0461,  0.0260, -0.1812],
               [-0.0437,  0.0989, -0.1171, -0.0531, -0.1493],
               [-0.1677, -0.1009,  0.0778, -0.1462,  0.0935]]],
     
     
             [[[-0.0794,  0.1744, -0.0915, -0.1289, -0.0317],
               [ 0.1679,  0.1361,  0.0542,  0.1967,  0.1620],
               [-0.0898,  0.0305, -0.1617, -0.1643, -0.0253],
               [ 0.1339, -0.1151,  0.0954, -0.0359, -0.1790],
               [-0.0408, -0.1074,  0.0081, -0.0884,  0.0163]]],
     
     
             [[[ 0.0022, -0.1097,  0.0842, -0.1927,  0.0277],
               [ 0.0127, -0.0666,  0.1235, -0.1696,  0.1377],
               [ 0.0741, -0.1627, -0.0953,  0.1622,  0.0921],
               [ 0.1111,  0.1422, -0.1884, -0.0130, -0.0533],
               [ 0.0838,  0.1769,  0.0109,  0.0086, -0.0795]]],
     
     
             [[[-0.0502, -0.1545, -0.1304,  0.1680, -0.0439],
               [ 0.1891,  0.1681, -0.1068,  0.1589, -0.1011],
               [-0.0923, -0.1569,  0.1337,  0.0743,  0.1794],
               [-0.1630,  0.0071, -0.0524, -0.1211, -0.0362],
               [-0.1111,  0.1168,  0.0010, -0.0352,  0.1756]]],
     
     
             [[[-0.0282, -0.0647, -0.0309,  0.0586,  0.1442],
               [ 0.1881,  0.1471,  0.0371, -0.0573,  0.1329],
               [ 0.0760, -0.0789, -0.0935,  0.1464,  0.1953],
               [-0.0965, -0.0576,  0.1569, -0.0305,  0.1716],
               [ 0.1946,  0.1921,  0.1460,  0.1270,  0.0192]]]]), tensor([ 0.1092, -0.1012,  0.1255, -0.0428,  0.1610,  0.0467,  0.1861,  0.1967,
             -0.0119, -0.0437, -0.0364, -0.1432, -0.1487,  0.1987, -0.0183,  0.0156,
              0.0776,  0.1051,  0.1560,  0.1141, -0.1168, -0.0773,  0.1036,  0.0045,
              0.0811,  0.0564,  0.1103,  0.1955,  0.1101,  0.1653,  0.1274,  0.0707]), tensor([[[[ 3.4215e-02,  3.1851e-02, -3.1990e-02, -2.4633e-02, -1.4061e-02],
               [ 1.2874e-02,  1.9820e-02, -2.9326e-02, -3.5093e-02, -1.6611e-02],
               [-1.8654e-02, -2.6634e-02, -1.0565e-03, -3.2393e-02,  2.5394e-02],
               [ 2.8582e-02,  2.3981e-02, -3.6160e-03, -3.3239e-02, -3.3089e-02],
               [-1.4720e-02, -1.4778e-02, -1.3619e-02, -8.4345e-03,  3.2735e-02]],
     
              [[-8.9284e-03, -1.1837e-02, -2.9223e-02,  2.7357e-02,  3.1966e-02],
               [-3.2790e-02, -2.8377e-02,  6.0738e-03, -1.0012e-02,  2.0444e-03],
               [-2.6949e-02,  4.2564e-03,  2.1914e-02, -1.0865e-02,  6.7992e-03],
               [-3.0702e-02,  3.4266e-02, -1.4731e-02,  5.6710e-03, -1.2800e-02],
               [-2.6684e-02, -1.1684e-02,  2.0707e-02,  2.5899e-02, -2.3985e-02]],
     
              [[ 1.5901e-02,  2.6151e-02,  3.1089e-02, -1.1213e-02,  3.3065e-02],
               [ 3.1703e-03,  2.5620e-02,  9.4295e-03,  2.3775e-02, -1.4995e-02],
               [ 2.9061e-02, -2.2070e-02,  1.8273e-02, -1.7573e-02, -8.4471e-03],
               [-2.3864e-02, -1.0588e-02, -1.6631e-02,  5.6322e-04, -3.7847e-03],
               [-3.4452e-02, -4.9367e-03, -8.4581e-03,  9.4274e-03,  1.8410e-02]],
     
              ...,
     
              [[ 1.8886e-02, -1.4010e-02,  3.1874e-02,  3.2005e-02, -2.3095e-02],
               [-3.0070e-03, -7.3744e-04,  7.5279e-03,  3.4897e-03,  2.9886e-02],
               [-7.0828e-03, -1.5942e-03, -2.5189e-02,  3.1235e-02,  1.0274e-02],
               [-6.7937e-03, -6.4489e-03, -1.9903e-03, -2.9061e-03, -2.7987e-02],
               [ 1.3823e-02, -2.3655e-02, -1.3454e-02, -1.0748e-02, -2.5365e-02]],
     
              [[-1.7251e-02,  2.2574e-02, -3.1775e-02,  1.1056e-02,  3.0264e-02],
               [-2.4015e-03,  3.6797e-04,  3.1375e-02, -2.4610e-02, -1.7509e-02],
               [ 1.0531e-02,  1.4460e-02,  2.5519e-02, -2.7404e-02, -3.1486e-02],
               [-7.5893e-03,  7.7746e-03, -7.8301e-03,  1.8347e-02, -1.2583e-02],
               [-9.9493e-03,  1.7974e-03, -8.8425e-03, -1.0114e-02, -8.3833e-03]],
     
              [[-2.7485e-02, -1.0523e-02, -2.7561e-02, -3.0887e-02,  1.6148e-02],
               [-2.1342e-02, -3.1566e-02, -2.8830e-02,  2.3036e-02,  2.0477e-02],
               [ 2.5157e-02,  1.3644e-02,  2.7742e-02,  2.7517e-02,  1.9181e-02],
               [ 2.8086e-02, -3.4166e-02, -2.4337e-02,  2.3341e-02, -1.8879e-02],
               [ 1.1548e-02,  3.8810e-03,  2.1140e-02, -1.9494e-02,  1.9646e-02]]],
     
     
             [[[ 1.4197e-02,  2.2693e-02,  2.4212e-02,  2.3371e-03,  3.2226e-02],
               [-1.2037e-03,  1.7354e-03,  3.5187e-02,  1.1291e-02, -4.9615e-04],
               [ 9.9413e-03, -3.5207e-04,  9.8214e-03,  1.9830e-02,  3.4511e-02],
               [-5.0710e-03,  2.5797e-02, -2.4624e-02, -2.3307e-02, -1.6377e-02],
               [-2.6477e-02, -5.8760e-04, -4.7716e-03, -2.0889e-02,  1.0995e-02]],
     
              [[-2.7908e-02,  3.3644e-02,  5.9671e-03, -1.6862e-02, -3.5059e-02],
               [ 1.6676e-02,  1.2755e-02,  1.5726e-02, -3.4257e-03,  2.4924e-02],
               [-2.3684e-02,  2.5474e-02, -1.5754e-02, -2.3486e-02, -2.9507e-03],
               [ 1.2314e-02,  2.9314e-02,  3.3555e-02,  1.8871e-02,  1.8055e-02],
               [-1.4242e-02,  2.5115e-02, -2.2893e-02, -2.3078e-02,  1.8173e-02]],
     
              [[ 5.5846e-03,  2.1291e-03,  5.2495e-03,  1.2620e-03, -2.2444e-02],
               [-1.6137e-02,  2.2707e-03, -2.3414e-02, -2.6367e-02,  1.8329e-02],
               [-2.9248e-02, -4.8049e-03, -1.7246e-02, -1.4117e-02, -2.3186e-02],
               [ 2.5687e-02,  3.1482e-02, -3.5057e-02, -2.6718e-02, -2.1352e-02],
               [ 6.3401e-03,  2.7100e-02, -8.5221e-04, -3.0839e-02,  3.2441e-02]],
     
              ...,
     
              [[-1.4497e-02, -1.4756e-02,  2.8516e-02,  6.1750e-03, -2.4559e-02],
               [-3.5086e-02, -2.1854e-02, -1.5571e-02, -1.5009e-02,  9.1230e-03],
               [ 1.7028e-02,  8.5996e-03,  1.8000e-02,  9.9304e-03,  1.4644e-02],
               [ 2.5854e-02, -9.1312e-03, -1.5623e-02,  2.3103e-02,  1.4050e-02],
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              ...,
     
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             ...,
     
     
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              ...,
     
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              ...,
     
              [[ 1.0640e-02, -3.4773e-03,  1.7848e-02,  2.3339e-02,  6.5579e-03],
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              [[ 1.2845e-02,  1.6605e-03, -1.9191e-02,  1.9487e-02, -1.7243e-02],
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              [[ 8.5709e-03, -2.3691e-02,  1.7627e-02,  3.5223e-02,  2.6195e-03],
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               [ 4.3871e-03, -1.1070e-02, -4.8881e-03,  1.8845e-02, -1.4769e-03]],
     
              ...,
     
              [[ 1.2379e-02,  7.2388e-03, -1.4122e-02,  1.8673e-02,  1.8472e-02],
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               [-2.0098e-02,  9.6565e-03,  1.4130e-02, -2.1930e-02, -1.9950e-03]]]]), tensor([ 0.0258, -0.0098,  0.0090, -0.0266, -0.0003,  0.0192, -0.0012, -0.0049,
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             -0.0023, -0.0143,  0.0052, -0.0041, -0.0139, -0.0123,  0.0161, -0.0053,
             -0.0059,  0.0012, -0.0077, -0.0133, -0.0012, -0.0127,  0.0005, -0.0130,
             -0.0176, -0.0114,  0.0139, -0.0153,  0.0124,  0.0151, -0.0092, -0.0168,
             -0.0070,  0.0114, -0.0125,  0.0129, -0.0066,  0.0150,  0.0046,  0.0043,
              0.0111,  0.0087, -0.0170, -0.0174, -0.0116,  0.0087,  0.0146,  0.0078,
             -0.0055, -0.0119, -0.0041, -0.0017, -0.0093,  0.0170,  0.0093, -0.0026,
             -0.0117,  0.0008, -0.0084, -0.0079, -0.0071, -0.0148, -0.0170,  0.0022,
             -0.0049,  0.0149,  0.0078, -0.0042,  0.0118, -0.0100,  0.0136,  0.0112,
             -0.0024,  0.0149, -0.0113, -0.0128,  0.0030,  0.0005, -0.0037,  0.0042,
              0.0034, -0.0113,  0.0158,  0.0071,  0.0074, -0.0136, -0.0155,  0.0028,
              0.0136,  0.0177, -0.0068,  0.0063,  0.0021, -0.0085,  0.0044,  0.0164,
             -0.0075,  0.0109,  0.0103, -0.0105, -0.0167,  0.0114,  0.0093, -0.0098,
              0.0115, -0.0011,  0.0097,  0.0087,  0.0075, -0.0120,  0.0011, -0.0067,
              0.0058, -0.0050, -0.0077,  0.0039, -0.0112, -0.0015, -0.0152,  0.0074,
             -0.0085,  0.0085,  0.0060, -0.0059, -0.0125, -0.0065, -0.0160,  0.0137,
              0.0145, -0.0097, -0.0032,  0.0059, -0.0137, -0.0119,  0.0141, -0.0012,
              0.0105, -0.0023,  0.0089, -0.0011,  0.0122,  0.0144, -0.0047,  0.0127,
             -0.0046,  0.0057, -0.0112,  0.0062,  0.0176,  0.0162,  0.0004, -0.0061,
             -0.0010, -0.0111, -0.0037, -0.0083,  0.0007, -0.0071,  0.0121, -0.0104,
              0.0169, -0.0023, -0.0051, -0.0170, -0.0123,  0.0005, -0.0006, -0.0099,
              0.0049, -0.0009,  0.0176,  0.0136, -0.0070,  0.0170, -0.0169,  0.0073,
             -0.0058, -0.0111, -0.0014, -0.0076, -0.0080,  0.0006,  0.0075,  0.0064,
             -0.0090, -0.0023,  0.0173,  0.0155, -0.0130,  0.0142,  0.0103, -0.0153,
             -0.0011,  0.0139,  0.0086, -0.0130,  0.0029,  0.0074, -0.0003,  0.0030,
             -0.0004,  0.0141, -0.0008, -0.0158,  0.0103, -0.0035,  0.0115, -0.0030,
             -0.0049, -0.0023, -0.0098,  0.0058, -0.0002,  0.0170,  0.0124,  0.0086,
             -0.0128, -0.0120,  0.0043,  0.0066,  0.0069, -0.0064,  0.0058, -0.0016,
              0.0105,  0.0117,  0.0121, -0.0135,  0.0078, -0.0032, -0.0041,  0.0038,
              0.0156,  0.0073,  0.0012,  0.0168, -0.0071, -0.0160,  0.0059,  0.0089,
             -0.0043,  0.0019, -0.0178,  0.0018,  0.0017, -0.0090,  0.0136, -0.0167,
              0.0018,  0.0094, -0.0153, -0.0061, -0.0172, -0.0072, -0.0088, -0.0058,
              0.0103, -0.0004,  0.0163,  0.0048,  0.0093,  0.0106, -0.0093,  0.0023,
              0.0003, -0.0150, -0.0174, -0.0041, -0.0008, -0.0068,  0.0174,  0.0111,
              0.0168,  0.0034, -0.0021,  0.0051,  0.0053,  0.0017, -0.0137,  0.0046,
             -0.0005, -0.0071, -0.0112,  0.0125,  0.0107, -0.0157, -0.0031, -0.0079,
             -0.0030,  0.0099, -0.0033,  0.0061,  0.0009,  0.0099, -0.0055, -0.0099,
              0.0057,  0.0126, -0.0092, -0.0071, -0.0156, -0.0092, -0.0086,  0.0043,
              0.0026,  0.0127, -0.0045,  0.0163, -0.0036,  0.0123,  0.0060,  0.0123,
             -0.0040,  0.0074, -0.0113, -0.0036, -0.0157, -0.0103, -0.0140,  0.0143,
              0.0035, -0.0015, -0.0158, -0.0157, -0.0079,  0.0111,  0.0134,  0.0094,
             -0.0120, -0.0178, -0.0065, -0.0007,  0.0016, -0.0102, -0.0133, -0.0095,
             -0.0143, -0.0050, -0.0029, -0.0060, -0.0126,  0.0008,  0.0079,  0.0073,
             -0.0082, -0.0120,  0.0162, -0.0058,  0.0126,  0.0147, -0.0131, -0.0136,
             -0.0013, -0.0102, -0.0065, -0.0032, -0.0055,  0.0150, -0.0159, -0.0142]), tensor([[-0.0060, -0.0238, -0.0167,  ..., -0.0295, -0.0283,  0.0013],
             [ 0.0272, -0.0164,  0.0261,  ...,  0.0342,  0.0118, -0.0295],
             [-0.0431, -0.0081, -0.0005,  ...,  0.0110,  0.0334, -0.0054],
             ...,
             [-0.0257, -0.0054,  0.0206,  ..., -0.0191,  0.0412, -0.0227],
             [ 0.0334, -0.0221,  0.0043,  ...,  0.0383, -0.0305,  0.0005],
             [-0.0314,  0.0262,  0.0313,  ..., -0.0048,  0.0006,  0.0426]]), tensor([-0.0431, -0.0134, -0.0107,  0.0229,  0.0287, -0.0393, -0.0060, -0.0009,
              0.0412, -0.0344, -0.0020,  0.0278, -0.0317, -0.0283,  0.0094,  0.0142,
             -0.0097, -0.0215, -0.0283, -0.0332, -0.0333,  0.0352, -0.0130, -0.0117,
             -0.0346, -0.0030, -0.0087, -0.0178,  0.0225,  0.0430,  0.0153, -0.0233,
              0.0231, -0.0374, -0.0200,  0.0007,  0.0341,  0.0077, -0.0098, -0.0249,
              0.0400, -0.0381, -0.0155,  0.0188, -0.0362,  0.0095, -0.0313,  0.0109,
              0.0298, -0.0086,  0.0114, -0.0137, -0.0284,  0.0243,  0.0336,  0.0356,
              0.0076,  0.0008, -0.0358, -0.0435,  0.0303,  0.0354])]
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 a = [(3, 2), (2, 5), (2, 6)]
 #a.sort(reverse = True)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 b = [(13, 12), (12, 15), (12, 16)]
 
 import torch
 a_t = torch.tensor(a)
 b_t = torch.tensor(b)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 print(a_t)
 print(b_t)
 ```
 
 %% Output
 
     tensor([[3, 2],
             [2, 5],
             [2, 6]])
     tensor([[13, 12],
             [12, 15],
             [12, 16]])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 indices = torch.tensor([2, 3, 5])
 torch.flatten(a_t)[indices] = torch.flatten(b_t)[:3]
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 c = torch.stack((torch.flatten(a_t), torch.flatten(b_t)), dim=-1)
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 print(a_t)
 print(b_t)
 ```
 
 %% Output
 
     tensor([[ 3,  2],
             [13, 12],
             [ 2, 12]])
     tensor([[13, 12],
             [12, 15],
             [12, 16]])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 c
 ```
 
 %% Output
 
     tensor([[ 3, 13],
             [ 2, 12],
             [ 2, 12],
             [ 5, 15],
             [ 2, 12],
             [ 6, 16]])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 c[-1] = 26
 ```
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 c
 ```
 
 %% Output
 
     tensor([ 3,  2,  2,  5,  2,  6, 13, 12, 12, 15, 12, 26])
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 b
 ```
 
 %% Output
 
     [(13, 12), (12, 15), (12, 16)]
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 torch.tensor([1,2,3]).shape[0]
 ```
 
 %% Output
 
     3
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 from decentralizepy.datasets.Partitioner import DataPartitioner
 l = [1, 2, 3, 4, 5, 6, 7]
 e = len(l) // 3
 frac = e / len(l)
 sizes = [frac] * 3
 sizes[-1] += 1.0 - frac * 3
 print("sizes: ", sizes)
 
 for i in range(3):
     myPar = DataPartitioner(l, sizes).use(i)
     for j in range(len(myPar)):
         print(myPar.__getitem__(j), end=' ')
     print()
 
 ```
 
 %% Output
 
     sizes:  [0.2857142857142857, 0.2857142857142857, 0.4285714285714286]
     2 3
     6 5
     1 4 7
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
 w = 1
 p = 2
 i = w/p
 print(i)
 ```
 
 %% Output
 
     0.5
 
 %% Cell type:code id: tags:
 
-``` 
+``` python
+from torch import multiprocessing as mp
+from decentralizepy.node.PeerSampler import PeerSampler
+from decentralizepy.node.Node import Node
+from decentralizepy.mappings.Linear import Linear
+from decentralizepy.graphs.Regular import Regular
+
+l = Linear(1, 6)
+g = Regular(6, 2)
+processes = [mp.Process(target = PeerSampler, args=[-1, 0, l, g, None]),
+             mp.Process(target = Node, args=[1, 0, l, g, None]),
+             mp.Process(target = Node, args=[2, 0, l, g, None]),
+             mp.Process(target = Node, args=[3, 0, l, g, None]),
+             mp.Process(target = Node, args=[4, 0, l, g, None]),
+             mp.Process(target = Node, args=[5, 0, l, g, None]),
+             mp.Process(target = Node, args=[6, 0, l, g, None]),
+            ]
+
+for p in processes:
+    p.start()
+
+for p in processes:
+    p.join()
+
+
+
+
+
+```
+
+%% Cell type:code id: tags:
+
+``` python
 ```

eval/plot.py

 import json
 import os
 import sys
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 
 
 def get_stats(l):
@@ -25,18 +26,32 @@ def get_stats(l):
     return mean_dict, stdev_dict, min_dict, max_dict
 
 
-def plot(means, stdevs, mins, maxs, title, label, loc):
+def plot(means, stdevs, mins, maxs, title, label, loc, xlabel="communication rounds"):
     plt.title(title)
-    plt.xlabel("communication rounds")
-    x_axis = list(means.keys())
-    y_axis = list(means.values())
-    err = list(stdevs.values())
-    plt.errorbar(x_axis, y_axis, yerr=err, label=label)
+    plt.xlabel(xlabel)
+    x_axis = np.array(list(means.keys()))
+    y_axis = np.array(list(means.values()))
+    err = np.array(list(stdevs.values()))
+    plt.plot(x_axis, y_axis, label=label)
+    plt.fill_between(x_axis, y_axis - err, y_axis + err, alpha=0.4)
     plt.legend(loc=loc)
 
 
-def plot_results(path):
+def replace_dict_key(d_org: dict, d_other: dict):
+    result = {}
+    for x, y in d_org.items():
+        result[d_other[x]] = y
+    return result
+
+
+def plot_results(path, centralized, data_machine="machine0", data_node=0):
     folders = os.listdir(path)
+    if centralized.lower() in ["true", "1", "t", "y", "yes"]:
+        centralized = True
+        print("Centralized")
+    else:
+        centralized = False
+
     folders.sort()
     print("Reading folders from: ", path)
     print("Folders: ", folders)
@@ -44,8 +59,8 @@ def plot_results(path):
     meta_means, meta_stdevs = {}, {}
     data_means, data_stdevs = {}, {}
     for folder in folders:
-        folder_path = os.path.join(path, folder)
-        if not os.path.isdir(folder_path):
+        folder_path = Path(os.path.join(path, folder))
+        if not folder_path.is_dir() or "weights" == folder_path.name:
             continue
         results = []
         machine_folders = os.listdir(folder_path)
@@ -59,71 +74,130 @@ def plot_results(path):
                 filepath = os.path.join(mf_path, f)
                 with open(filepath, "r") as inf:
                     results.append(json.load(inf))
+        if folder.startswith("FL") or folder.startswith("Parameter Server"):
+            data_node = -1
+        else:
+            data_node = 0
+        with open(folder_path / data_machine / f"{data_node}_results.json", "r") as f:
+            main_data = json.load(f)
+        main_data = [main_data]
+
+        # Plotting bytes over time
+        plt.figure(10)
+        b_means, stdevs, mins, maxs = get_stats([x["total_bytes"] for x in results])
+        plot(b_means, stdevs, mins, maxs, "Total Bytes", folder, "lower right")
+        df = pd.DataFrame(
+            {
+                "mean": list(b_means.values()),
+                "std": list(stdevs.values()),
+                "nr_nodes": [len(results)] * len(b_means),
+            },
+            list(b_means.keys()),
+            columns=["mean", "std", "nr_nodes"],
+        )
+        df.to_csv(
+            os.path.join(path, "total_bytes_" + folder + ".csv"), index_label="rounds"
+        )
+
         # Plot Training loss
         plt.figure(1)
         means, stdevs, mins, maxs = get_stats([x["train_loss"] for x in results])
         plot(means, stdevs, mins, maxs, "Training Loss", folder, "upper right")
+
+        correct_bytes = [b_means[x] for x in means]
+
         df = pd.DataFrame(
             {
                 "mean": list(means.values()),
                 "std": list(stdevs.values()),
                 "nr_nodes": [len(results)] * len(means),
+                "total_bytes": correct_bytes,
             },
             list(means.keys()),
-            columns=["mean", "std", "nr_nodes"],
+            columns=["mean", "std", "nr_nodes", "total_bytes"],
+        )
+        plt.figure(11)
+        means = replace_dict_key(means, b_means)
+        plot(
+            means,
+            stdevs,
+            mins,
+            maxs,
+            "Training Loss",
+            folder,
+            "upper right",
+            "Total Bytes per node",
         )
+
         df.to_csv(
             os.path.join(path, "train_loss_" + folder + ".csv"), index_label="rounds"
         )
         # Plot Testing loss
         plt.figure(2)
-        means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in results])
+        if centralized:
+            means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in main_data])
+        else:
+            means, stdevs, mins, maxs = get_stats([x["test_loss"] for x in results])
         plot(means, stdevs, mins, maxs, "Testing Loss", folder, "upper right")
         df = pd.DataFrame(
             {
                 "mean": list(means.values()),
                 "std": list(stdevs.values()),
                 "nr_nodes": [len(results)] * len(means),
+                "total_bytes": correct_bytes,
             },
             list(means.keys()),
-            columns=["mean", "std", "nr_nodes"],
+            columns=["mean", "std", "nr_nodes", "total_bytes"],
+        )
+        plt.figure(12)
+        means = replace_dict_key(means, b_means)
+        plot(
+            means,
+            stdevs,
+            mins,
+            maxs,
+            "Testing Loss",
+            folder,
+            "upper right",
+            "Total Bytes per node",
         )
+
         df.to_csv(
             os.path.join(path, "test_loss_" + folder + ".csv"), index_label="rounds"
         )
         # Plot Testing Accuracy
         plt.figure(3)
-        means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in results])
+        if centralized:
+            means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in main_data])
+        else:
+            means, stdevs, mins, maxs = get_stats([x["test_acc"] for x in results])
         plot(means, stdevs, mins, maxs, "Testing Accuracy", folder, "lower right")
         df = pd.DataFrame(
             {
                 "mean": list(means.values()),
                 "std": list(stdevs.values()),
                 "nr_nodes": [len(results)] * len(means),
+                "total_bytes": correct_bytes,
             },
             list(means.keys()),
-            columns=["mean", "std", "nr_nodes"],
-        )
-        df.to_csv(
-            os.path.join(path, "test_acc_" + folder + ".csv"), index_label="rounds"
+            columns=["mean", "std", "nr_nodes", "total_bytes"],
         )
-        plt.figure(6)
-        means, stdevs, mins, maxs = get_stats([x["grad_std"] for x in results])
+        plt.figure(13)
+        means = replace_dict_key(means, b_means)
         plot(
             means,
             stdevs,
             mins,
             maxs,
-            "Gradient Variation over Nodes",
+            "Testing Accuracy",
             folder,
-            "upper right",
+            "lower right",
+            "Total Bytes per node",
         )
-        # Plot Testing loss
-        plt.figure(7)
-        means, stdevs, mins, maxs = get_stats([x["grad_mean"] for x in results])
-        plot(
-            means, stdevs, mins, maxs, "Gradient Magnitude Mean", folder, "upper right"
+        df.to_csv(
+            os.path.join(path, "test_acc_" + folder + ".csv"), index_label="rounds"
         )
+
         # Collect total_bytes shared
         bytes_list = []
         for x in results:
@@ -152,16 +226,21 @@ def plot_results(path):
         data_means[folder] = list(means.values())[0]
         data_stdevs[folder] = list(stdevs.values())[0]
 
+    plt.figure(10)
+    plt.savefig(os.path.join(path, "total_bytes.png"), dpi=300)
+    plt.figure(11)
+    plt.savefig(os.path.join(path, "bytes_train_loss.png"), dpi=300)
+    plt.figure(12)
+    plt.savefig(os.path.join(path, "bytes_test_loss.png"), dpi=300)
+    plt.figure(13)
+    plt.savefig(os.path.join(path, "bytes_test_acc.png"), dpi=300)
+
     plt.figure(1)
     plt.savefig(os.path.join(path, "train_loss.png"), dpi=300)
     plt.figure(2)
     plt.savefig(os.path.join(path, "test_loss.png"), dpi=300)
     plt.figure(3)
     plt.savefig(os.path.join(path, "test_acc.png"), dpi=300)
-    plt.figure(6)
-    plt.savefig(os.path.join(path, "grad_std.png"), dpi=300)
-    plt.figure(7)
-    plt.savefig(os.path.join(path, "grad_mean.png"), dpi=300)
     # Plot total_bytes
     plt.figure(4)
     plt.title("Data Shared")
@@ -236,6 +315,10 @@ def plot_parameters(path):
 
 
 if __name__ == "__main__":
-    assert len(sys.argv) == 2
-    plot_results(sys.argv[1])
-    # plot_parameters(sys.argv[1])
\ No newline at end of file
+    assert len(sys.argv) == 3
+    # The args are:
+    # 1: the folder with the data
+    # 2: True/False: If True then the evaluation on the test set was centralized
+    # for federated learning folder name must start with "FL"!
+    plot_results(sys.argv[1], sys.argv[2])
+    # plot_parameters(sys.argv[1])

eval/plot_percentile.py

@@ -4,8 +4,8 @@ import sys
 
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 import torch
+from matplotlib import pyplot as plt
 
 
 def get_stats(l):
@@ -70,7 +70,7 @@ def plot_results(path):
             for sp in shared_params:
                 filepath = os.path.join(mf_path, sp)
                 with open(filepath, "r") as spf:
-                    all_shared_params.append(np.array(json.load(spf), dtype = np.int32))
+                    all_shared_params.append(np.array(json.load(spf), dtype=np.int32))
 
         # Plot Training loss
         plt.figure(1)
@@ -78,19 +78,19 @@ def plot_results(path):
         mean = np.mean(all_shared_params, axis=0)
         std = np.std(all_shared_params, axis=0)
         with open(
-                os.path.join(path, "shared_params_avg_"+folder+".json"), "w"
+            os.path.join(path, "shared_params_avg_" + folder + ".json"), "w"
         ) as mf:
             json.dump(mean.tolist(), mf)
 
         with open(
-                os.path.join(path, "shared_params_std_"+folder+".json"), "w"
+            os.path.join(path, "shared_params_std_" + folder + ".json"), "w"
         ) as sf:
             json.dump(std.tolist(), sf)
 
         # copy jupyter notebook code
         percentile = np.percentile(mean, np.arange(0, 100, 1))
         plt.plot(np.arange(0, 100, 1), percentile, label=folder)
-        plt.title('Shared parameters Percentiles')
+        plt.title("Shared parameters Percentiles")
         # plt.ylabel("Absolute frequency value")
         plt.xlabel("Percentiles")
         plt.xticks(np.arange(0, 110, 10))
@@ -101,14 +101,15 @@ def plot_results(path):
         print(sort)
         length = sort.shape[0]
         length = int(length / 20)
-        bins = [torch.sum(sort[length * i: length * (i + 1)]).item() for i in range(20)]
+        bins = [
+            torch.sum(sort[length * i : length * (i + 1)]).item() for i in range(20)
+        ]
         total = np.sum(bins)
-        perc = bins / total #np.divide(bins, total)
+        perc = bins / total  # np.divide(bins, total)
         print(perc)
-        plt.bar(np.arange(0, 97.5, 5), perc, width=5, align='edge',
-                label=folder)
+        plt.bar(np.arange(0, 97.5, 5), perc, width=5, align="edge", label=folder)
 
-        plt.title('Shared parameters Percentiles')
+        plt.title("Shared parameters Percentiles")
         # plt.ylabel("Absolute frequency value")
         plt.xlabel("Percentiles")
         plt.legend(loc="lower right")
@@ -122,4 +123,4 @@ def plot_results(path):
 
 if __name__ == "__main__":
     assert len(sys.argv) == 2
-    plot_results(sys.argv[1])
\ No newline at end of file
+    plot_results(sys.argv[1])

eval/plotting_from_csv.py

+import distutils
+import json
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+
+def plot(x_axis, means, stdevs, pos, nb_plots, title, label, loc, xlabel):
+    cmap = plt.get_cmap("gist_rainbow")
+    plt.title(title)
+    plt.xlabel(xlabel)
+    y_axis = list(means)
+    err = list(stdevs)
+    print("label:", label)
+    print("color: ", cmap(1 / nb_plots * pos))
+    plt.errorbar(
+        list(x_axis), y_axis, yerr=err, label=label, color=cmap(1 / nb_plots * pos)
+    )
+    plt.legend(loc=loc)
+
+
+def plot_results(path, epochs, global_epochs="True"):
+    if global_epochs.lower() in ["true", "1", "t", "y", "yes"]:
+        global_epochs = True
+    else:
+        global_epochs = False
+    epochs = int(epochs)
+    # rounds = int(rounds)
+    folders = os.listdir(path)
+    folders.sort()
+    print("Reading folders from: ", path)
+    print("Folders: ", folders)
+    bytes_means, bytes_stdevs = {}, {}
+    meta_means, meta_stdevs = {}, {}
+    data_means, data_stdevs = {}, {}
+
+    files = os.listdir(path)
+    files = [f for f in files if f.endswith(".csv")]
+    train_loss = sorted([f for f in files if f.startswith("train_loss")])
+    test_acc = sorted([f for f in files if f.startswith("test_acc")])
+    test_loss = sorted([f for f in files if f.startswith("test_loss")])
+    min_losses = []
+    for i, f in enumerate(train_loss):
+        filepath = os.path.join(path, f)
+        with open(filepath, "r") as inf:
+            results_csv = pd.read_csv(inf)
+        # Plot Training loss
+        plt.figure(1)
+        if global_epochs:
+            rounds = results_csv["rounds"].iloc[0]
+            print("Rounds: ", rounds)
+            results_cr = results_csv[results_csv.rounds <= epochs * rounds]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = (
+                results_cr["rounds"].to_numpy() / rounds
+            )  # list(np.arange(0, len(means), 1))
+            x_label = "global epochs"
+        else:
+            results_cr = results_csv[results_csv.rounds <= epochs]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = results_cr["rounds"].to_numpy()
+            x_label = "communication rounds"
+        min_losses.append(np.min(means))
+
+        plot(
+            x_axis,
+            means,
+            stdevs,
+            i,
+            len(train_loss),
+            "Training Loss",
+            f[len("train_loss") + 1 : -len(":2022-03-24T17:54.csv")],
+            "upper right",
+            x_label,
+        )
+
+    min_tlosses = []
+    for i, f in enumerate(test_loss):
+        filepath = os.path.join(path, f)
+        with open(filepath, "r") as inf:
+            results_csv = pd.read_csv(inf)
+        if global_epochs:
+            rounds = results_csv["rounds"].iloc[0]
+            print("Rounds: ", rounds)
+            results_cr = results_csv[results_csv.rounds <= epochs * rounds]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = (
+                results_cr["rounds"].to_numpy() / rounds
+            )  # list(np.arange(0, len(means), 1))
+            x_label = "global epochs"
+        else:
+            results_cr = results_csv[results_csv.rounds <= epochs]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = results_cr["rounds"].to_numpy()
+            x_label = "communication rounds"
+        print("x axis:", x_axis)
+        min_tlosses.append(np.min(means))
+        # Plot Testing loss
+        plt.figure(2)
+        plot(
+            x_axis,
+            means,
+            stdevs,
+            i,
+            len(test_loss),
+            "Testing Loss",
+            f[len("test_loss") + 1 : -len(":2022-03-24T17:54.csv")],
+            "upper right",
+            x_label,
+        )
+
+    max_taccs = []
+    for i, f in enumerate(test_acc):
+        filepath = os.path.join(path, f)
+        with open(filepath, "r") as inf:
+            results_csv = pd.read_csv(inf)
+        if global_epochs:
+            rounds = results_csv["rounds"].iloc[0]
+            print("Rounds: ", rounds)
+            results_cr = results_csv[results_csv.rounds <= epochs * rounds]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = (
+                results_cr["rounds"].to_numpy() / rounds
+            )  # list(np.arange(0, len(means), 1))
+            x_label = "global epochs"
+        else:
+            results_cr = results_csv[results_csv.rounds <= epochs]
+            means = results_cr["mean"].to_numpy()
+            stdevs = results_cr["std"].to_numpy()
+            x_axis = results_cr["rounds"].to_numpy()
+            x_label = "communication rounds"
+        max_taccs.append(np.max(means))
+        # Plot Testing Accuracy
+        plt.figure(3)
+        plot(
+            x_axis,
+            means,
+            stdevs,
+            i,
+            len(test_acc),
+            "Testing Accuracy",
+            f[len("test_acc") + 1 : -len(":2022-03-24T17:54.csv")],
+            "lower right",
+            x_label,
+        )
+
+    names_loss = [
+        f[len("train_loss") + 1 : -len(":2022-03-24T17:54.csv")] for f in train_loss
+    ]
+    names_acc = [
+        f[len("test_acc") + 1 : -len(":2022-03-24T17:54.csv")] for f in test_acc
+    ]
+    print(names_loss)
+    print(names_acc)
+    pf = pd.DataFrame(
+        {
+            "test_accuracy": max_taccs,
+            "test_losses": min_tlosses,
+            "train_losses": min_losses,
+        },
+        names_loss,
+    )
+    pf = pf.sort_values(["test_accuracy"], 0, ascending=False)
+    pf.to_csv(os.path.join(path, "best_results.csv"))
+
+    plt.figure(1)
+    plt.savefig(os.path.join(path, "ge_train_loss.png"), dpi=300)
+    plt.figure(2)
+    plt.savefig(os.path.join(path, "ge_test_loss.png"), dpi=300)
+    plt.figure(3)
+    plt.savefig(os.path.join(path, "ge_test_acc.png"), dpi=300)
+
+
+if __name__ == "__main__":
+    assert len(sys.argv) == 4
+    # The args are:
+    # 1: the folder with the csv files,
+    # 2: the number of epochs / comm rounds to plot for,
+    # 3: True/False with True meaning plot global epochs and False plot communication rounds
+    print(sys.argv[1], sys.argv[2], sys.argv[3])
+    plot_results(sys.argv[1], sys.argv[2], sys.argv[3])

eval/run.sh

 #!/bin/bash
+script_path=$(realpath $(dirname $0))
 
-decpy_path=~/Gitlab/decentralizepy/eval
+# Working directory, where config files are read from and logs are written.
+decpy_path=/mnt/nfs/$(whoami)/decpy_workingdir
 cd $decpy_path
 
-env_python=~/miniconda3/envs/decpy/bin/python3
-graph=96_regular.edges
-original_config=epoch_configs/config_celeba.ini
-config_file=/tmp/config.ini
-procs_per_machine=16
-machines=6
-iterations=200
-test_after=10
-eval_file=testing.py
+# Python interpreter
+env_python=python3
+
+# File regular_16.txt is available in /tutorial
+graph=$decpy_path/regular_16.txt
+
+# File config_celeba_sharing.ini is available in /tutorial
+# In this config file, change addresses_filepath to correspond to your list of machines (example in /tutorial/ip.json)
+original_config=$decpy_path/config_celeba_sharing.ini
+
+# Local config file
+config_file=/tmp/$(basename $original_config)
+
+# Python script to be executed
+eval_file=$script_path/testingPeerSampler.py
+
+# General parameters
+procs_per_machine=8
+machines=2
+iterations=5
+test_after=2
 log_level=INFO
 
 m=`cat $(grep addresses_filepath $original_config | awk '{print $3}') | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+echo M is $m
 log_dir=$(date '+%Y-%m-%dT%H:%M')/machine$m
 mkdir -p $log_dir
 
+# Copy and manipulate the local config file
 cp $original_config $config_file
 # echo "alpha = 0.10" >> $config_file
-$env_python $eval_file -ro 0 -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
\ No newline at end of file
+
+$env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level -wsd $log_dir

eval/run_celeba_synchronous.sh

+#!/bin/bash
+# Documentation
+# Note: documentation was not written for this run file, so actual behaviour may differ
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use Adam.
+#   For SGD these need to be changed manually
+# - The script will set '--test_after' and '--train_evaluate_after' to comm_rounds_per_global_epoch, i.e., the eavaluation
+#   on the train set and on the test set is carried out every global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=6_star.edges
+config_file=~/tmp/config.ini
+procs_per_machine=6
+machines=1
+global_epochs=20
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_celeba_synchronous.ini")
+# Learning rates
+lr="0.001"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="2000"
+# testing every x communication rounds
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+# Celeba has 63741 samples
+# Reddit has 70642
+# Femnist 734463
+# Shakespeares 3678451
+dataset_size=63741
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_grid.sh

@@ -18,10 +18,8 @@
 # Each node needs a folder called 'tmp' in the user's home directory
 #
 # Note:
-# - The script does not change the optimizer. All configs are writen to use Adam.
-#   For SGD these need to be changed manually
-# - The script will set '--test_after' and '--train_evaluate_after' to comm_rounds_per_global_epoch, i.e., the eavaluation
-#   on the train set and on the test set is carried out every global epoch.
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
 # - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
 #   relevant for Adams and other optimizers with internal state)
 #
@@ -39,7 +37,7 @@ decpy_path=$nfs_home/decentralizepy/eval
 cd $decpy_path
 
 env_python=$python_bin/python3
-graph=192_regular.edges
+graph=96_regular.edges
 config_file=~/tmp/config.ini
 procs_per_machine=16
 machines=6
@@ -62,7 +60,6 @@ batchsize=("8" "16")
 comm_rounds_per_global_epoch=("1" "5" "10")
 procs=`expr $procs_per_machine \* $machines`
 echo procs: $procs
-# Celeba has 63741 samples
 dataset_size=63741
 # Calculating the number of samples that each user/proc will have on average
 samples_per_user=`expr $dataset_size / $procs`
@@ -86,6 +83,8 @@ do
     new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $r); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $r)/x)*$iterations); print($iterations if y<$iterations else y)")
     echo batches per communication round: $batches_per_comm_round
     echo corrected iterations: $new_iterations
+    test_after=$(($new_iterations / $global_epochs))
+    echo test after: $test_after
     for lr in "${lrs[@]}"
     do
       for i in "${tests[@]}"
@@ -102,7 +101,7 @@ do
         $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
         $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
         $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $b
-        $env_python $eval_file -ro 0 -tea $r -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $r -cf $config_file -ll $log_level
+        $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
         echo $i is done
         sleep 1
         echo end of sleep

eval/run_xtimes.sh

+#!/bin/bash
+# Documentation
+# Note: documentation was not written for this run file, so actual behaviour may differ
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use Adam.
+#   For SGD these need to be changed manually
+# - The script will set '--test_after' and '--train_evaluate_after' to comm_rounds_per_global_epoch, i.e., the eavaluation
+#   on the train set and on the test set is carried out every global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=150
+eval_file=testingPeerSampler.py
+log_level=INFO
+ip_machines=$nfs_home/$logs_subfolder/ip_addr_6Machines.json
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+# Base configs for which the gird search is done
+tests="$nfs_home/$logs_subfolder/config.ini"
+#tests=("$nfs_home/$logs_subfolder/config_cifar_sharing.ini" "$nfs_home/$logs_subfolder/config_cifar_partialmodel.ini" "$nfs_home/$logs_subfolder/config_cifar_topkacc.ini" "$nfs_home/$logs_subfolder/config_cifar_topkaccRandomAlpha.ini" "$nfs_home/$logs_subfolder/config_cifar_subsampling.ini" "$nfs_home/$logs_subfolder/config_cifar_wavelet.ini" "$nfs_home/$logs_subfolder/config_cifar_waveletRandomAlpha.ini")
+#tests=("$nfs_home/$logs_subfolder/config_cifar_partialmodel.ini" "$nfs_home/$logs_subfolder/config_cifar_topkacc.ini" "$nfs_home/$logs_subfolder/config_cifar_topkaccRandomAlpha.ini" "$nfs_home/$logs_subfolder/config_cifar_subsampling.ini" "$nfs_home/$logs_subfolder/config_cifar_wavelet.ini" "$nfs_home/$logs_subfolder/config_cifar_waveletRandomAlpha.ini")
+#tests=("$nfs_home/$logs_subfolder/config_cifar_subsampling.ini" "$nfs_home/$logs_subfolder/config_cifar_sharing.ini" "$nfs_home/$logs_subfolder/config_cifar_waveletRandomAlpha.ini")
+#tests=("$nfs_home/$logs_subfolder/config_cifar_waveletRandomAlpha.ini")
+# Learning rates
+lr="0.01"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="20"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+# Celeba has 63741 samples
+# Reddit has 70642
+# Femnist 734463
+# Shakespeares 3678451
+dataset_size=50000
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+# random_seeds for which to rerun the experiments
+# random_seeds=("90" "91" "92" "93" "94")
+random_seeds=("94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    #log_dir_base=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    log_dir_base=$nfs_home/$logs_subfolder/lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    graph=$nfs_home/decentralizepy/eval/96_regular.edges
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file COMMUNICATION offset 10720
+    # $env_python $eval_file -cte 0 -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#

eval/run_xtimes_celeba.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config_celeba_sharing.ini
+procs_per_machine=16
+machines=6
+global_epochs=150
+eval_file=testingFederated.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+#tests=("step_configs/config_celeba_sharing.ini" "step_configs/config_celeba_partialmodel.ini" "step_configs/config_celeba_topkacc.ini" "step_configs/config_celeba_subsampling.ini" "step_configs/config_celeba_wavelet.ini")
+tests=("step_configs/config_celeba_sharing.ini")
+# Learning rates
+lr="0.001"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=63741
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+#random_seeds=("90" "91" "92" "93" "94")
+random_seeds=("90")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir_base=$nfs_home/$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_cifar.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=100
+eval_file=testingFederated.py
+log_level=INFO
+working_rate=0.1
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_cifar_sharing.ini")
+# Learning rates
+lr="0.01"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="20"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=50000
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir_base=$nfs_home/$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level -wr $working_rate
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#

eval/run_xtimes_femnist.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config_femnist_sharing.ini
+procs_per_machine=16
+machines=6
+global_epochs=80
+eval_file=testingFederated.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+#tests=("step_configs/config_femnist_sharing.ini" "step_configs/config_femnist_partialmodel.ini" "step_configs/config_femnist_topkacc.ini" "step_configs/config_femnist_subsampling.ini" "step_configs/config_femnist_wavelet.ini")
+tests=("step_configs/config_femnist_sharing.ini")
+# Learning rates
+lr="0.01"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=734463
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+#random_seeds=("90" "91" "92" "93" "94")
+random_seeds=("90")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir_base=$nfs_home/$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_reddit.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config_reddit_sharing.ini
+procs_per_machine=16
+machines=6
+global_epochs=50
+eval_file=testingFederated.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+#tests=("step_configs/config_reddit_sharing.ini" "step_configs/config_reddit_partialmodel.ini" "step_configs/config_reddit_topkacc.ini" "step_configs/config_reddit_subsampling.ini" "step_configs/config_reddit_wavelet.ini")
+tests=("step_configs/config_reddit_sharing.ini")
+# Learning rates
+lr="1"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=70642
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+#random_seeds=("90" "91" "92" "93" "94")
+random_seeds=("90")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir_base=$nfs_home/$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_shakespeare.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config_shakespeare_sharing.ini
+procs_per_machine=16
+machines=6
+global_epochs=100
+eval_file=testingFederated.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+#tests=("step_configs/config_shakespeare_sharing.ini" "step_configs/config_shakespeare_partialmodel.ini" "step_configs/config_shakespeare_topkacc.ini" "step_configs/config_shakespeare_subsampling.ini" "step_configs/config_shakespeare_wavelet.ini")
+tests=("step_configs/config_shakespeare_sharing.ini")
+# Learning rates
+lr="0.5"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="25"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=97545 # sub96, for sub: 678696
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+#random_seeds=("90" "91" "92" "93" "94")
+random_seeds=("90")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir_base=$nfs_home/$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')
+    echo results are stored in: $log_dir_base
+    log_dir=$log_dir_base/machine$m
+    mkdir -p $log_dir
+    weight_store_dir=$log_dir_base/weights
+    mkdir -p $weight_store_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -wsd $weight_store_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/step_configs/config_celeba_fft.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_grow.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_manualadapt.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_partialmodel.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]
@@ -31,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json
 [SHARING]
 sharing_package = decentralizepy.sharing.PartialModel
 sharing_class = PartialModel
+alpha = 0.1
\ No newline at end of file