diff --git a/36_nodes.edges b/eval/36_nodes.edges
similarity index 100%
rename from 36_nodes.edges
rename to eval/36_nodes.edges
diff --git a/eval/80_nodes.edges b/eval/80_nodes.edges
new file mode 100644
index 0000000000000000000000000000000000000000..d047c0bffe2fb882b08318955583c6153178a840
--- /dev/null
+++ b/eval/80_nodes.edges
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diff --git a/eval/96_nodes.edges b/eval/96_nodes.edges
new file mode 100644
index 0000000000000000000000000000000000000000..e7f88a1e350ce7f8db1142d0f85f0b7c1953d3c8
--- /dev/null
+++ b/eval/96_nodes.edges
@@ -0,0 +1,531 @@
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diff --git a/config.ini b/eval/config.ini
similarity index 73%
rename from config.ini
rename to eval/config.ini
index 07a6d9b754c8bb52f654f77aafe7c1053c8912b1..74b79298ed917774a5cff7aab6776858b58c3e9e 100644
--- a/config.ini
+++ b/eval/config.ini
@@ -2,9 +2,9 @@
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
model_class = CNN
-n_procs = 12
-train_dir = leaf/data/femnist/per_user_data/train
-test_dir = leaf/data/femnist/data/test
+n_procs = 16
+train_dir = /home/risharma/Gitlab/decentralizepy/leaf/data/femnist/per_user_data/train
+test_dir = /home/risharma/Gitlab/decentralizepy/leaf/data/femnist/data/test
; python list of fractions below
sizes =
@@ -25,7 +25,7 @@ loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
-addresses_filepath = ip_addr.json
+addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
diff --git a/graph.adj b/eval/graph.adj
similarity index 100%
rename from graph.adj
rename to eval/graph.adj
diff --git a/ip_addr.json b/eval/ip_addr_3Machines.json
similarity index 100%
rename from ip_addr.json
rename to eval/ip_addr_3Machines.json
diff --git a/eval/ip_addr_6Machines.json b/eval/ip_addr_6Machines.json
new file mode 100644
index 0000000000000000000000000000000000000000..12eb2052c1a72995fe228925f39ca2ab547c5054
--- /dev/null
+++ b/eval/ip_addr_6Machines.json
@@ -0,0 +1,8 @@
+{
+ "0": "10.90.41.128",
+ "1": "10.90.41.129",
+ "2": "10.90.41.130",
+ "3": "10.90.41.131",
+ "4": "10.90.41.132",
+ "5": "10.90.41.133"
+}
\ No newline at end of file
diff --git a/eval/main.ipynb b/eval/main.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7e0081309431e91f8c63744c04494f889fcd5c54
--- /dev/null
+++ b/eval/main.ipynb
@@ -0,0 +1,1199 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datasets.Femnist import Femnist\n",
+ "from graphs import SmallWorld\n",
+ "from collections import defaultdict\n",
+ "import os\n",
+ "import json\n",
+ "import numpy as np\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = FEMNIST\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b = SmallWorld(6, 2, 2, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b.adj_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(12):\n",
+ " print(b.neighbors(i))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clients = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_samples = []\n",
+ "data = defaultdict(lambda : None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datadir = \"./leaf/data/femnist/data/train\"\n",
+ "files = os.listdir(datadir)\n",
+ "total_users=0\n",
+ "users = set()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "files = os.listdir(datadir)[0:1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for f in files:\n",
+ " file_path = os.path.join(datadir, f)\n",
+ " print(file_path)\n",
+ " with open(file_path, 'r') as inf:\n",
+ " client_data = json.load(inf)\n",
+ " current_users = len(client_data['users'])\n",
+ " print(\"Current_Users: \", current_users)\n",
+ " total_users += current_users\n",
+ " users.update(client_data['users'])\n",
+ "\n",
+ "print(\"total_users: \", total_users)\n",
+ "print(\"total_users: \", len(users))\n",
+ "print(client_data['user_data'].keys())\n",
+ "print(np.array(client_data['user_data']['f3408_47']['x']).shape)\n",
+ "print(np.array(client_data['user_data']['f3408_47']['y']).shape)\n",
+ "print(np.array(client_data['user_data']['f3327_11']['x']).shape)\n",
+ "print(np.array(client_data['user_data']['f3327_11']['y']).shape)\n",
+ "print(np.unique(np.array(client_data['user_data']['f3327_11']['y'])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "file = 'run.py'\n",
+ "with open(file, 'r') as inf:\n",
+ " print(inf.readline().strip())\n",
+ " print(inf.readlines())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def f(l):\n",
+ " l[2] = 'c'\n",
+ "\n",
+ "a = ['a', 'a', 'a']\n",
+ "print(a)\n",
+ "f(a)\n",
+ "print(a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l = ['a', 'b', 'c']\n",
+ "print(l[:-1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from localconfig import LocalConfig\n",
+ "\n",
+ "def read_ini(file_path):\n",
+ " config = LocalConfig(file_path)\n",
+ " for section in config:\n",
+ " print(\"Section: \", section)\n",
+ " for key, value in config.items(section):\n",
+ " print((key, value))\n",
+ " print(dict(config.items('DATASET')))\n",
+ " return config\n",
+ " \n",
+ "config = read_ini(\"config.ini\")\n",
+ "for section in config:\n",
+ " print(section)\n",
+ "#d = dict(config.sections())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def func(a = 1, b = 2, c = 3):\n",
+ " print(a + b + c)\n",
+ "\n",
+ "l = [3, 5, 7]\n",
+ "\n",
+ "func(*l)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torch import multiprocessing as mp\n",
+ "\n",
+ "mp.spawn(fn = func, nprocs = 2, args = [], kwargs = {'a': 4, 'b': 5, 'c': 6})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "l = '[0.4, 0.2, 0.3, 0.1]'\n",
+ "type(eval(l))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.datasets.Femnist import Femnist\n",
+ "f1 = Femnist(0, 1, 'leaf/data/femnist/data/train')\n",
+ "ts = f1.get_trainset(1)\n",
+ "for data, target in ts:\n",
+ " print(data)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.datasets.Femnist import Femnist\n",
+ "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
+ "from decentralizepy.mappings.Linear import Linear\n",
+ "\n",
+ "f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\n",
+ "g = SmallWorld(4, 1, 0.5)\n",
+ "l = Linear(2, 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.node.Node import Node\n",
+ "from torch import multiprocessing as mp\n",
+ "import logging\n",
+ "n1 = Node(0, l, g, f, \"./results\", logging.DEBUG)\n",
+ "n2 = Node(1, l, g, f, \"./results\", logging.DEBUG)\n",
+ "# mp.spawn(fn = Node, nprocs = 2, args=[l,g,f])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from testing import f"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torch import multiprocessing as mp\n",
+ "import torch\n",
+ "m1 = torch.nn.Linear(1,1)\n",
+ "o1 = torch.optim.SGD(m1.parameters(), 0.6)\n",
+ "print(m1)\n",
+ "\n",
+ "\n",
+ "mp.spawn(fn = f, nprocs = 2, args=[m1, o1])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "o1.param_groups"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with torch.no_grad():\n",
+ " o1.param_groups[0][\"params\"][0].copy_(torch.zeros(1,))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "o1.param_groups"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m1.state_dict()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "loss = getattr(torch.nn.functional, 'nll_loss')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Section: GRAPH\n",
+ "('package', 'decentralizepy.graphs.SmallWorld')\n",
+ "('graph_class', 'SmallWorld')\n",
+ "Section: DATASET\n",
+ "('dataset_package', 'decentralizepy.datasets.Femnist')\n",
+ "('dataset_class', 'Femnist')\n",
+ "('model_class', 'CNN')\n",
+ "('n_procs', 36)\n",
+ "('train_dir', 'leaf/data/femnist/per_user_data/train')\n",
+ "('test_dir', 'leaf/data/femnist/data/test')\n",
+ "('sizes', '')\n",
+ "Section: OPTIMIZER_PARAMS\n",
+ "('optimizer_package', 'torch.optim')\n",
+ "('optimizer_class', 'Adam')\n",
+ "('lr', 0.01)\n",
+ "Section: TRAIN_PARAMS\n",
+ "('training_package', 'decentralizepy.training.Training')\n",
+ "('training_class', 'Training')\n",
+ "('epochs_per_round', 1)\n",
+ "('batch_size', 1024)\n",
+ "('shuffle', True)\n",
+ "('loss_package', 'torch.nn')\n",
+ "('loss_class', 'CrossEntropyLoss')\n",
+ "Section: COMMUNICATION\n",
+ "('comm_package', 'decentralizepy.communication.TCP')\n",
+ "('comm_class', 'TCP')\n",
+ "('addresses_filepath', 'ip_addr.json')\n",
+ "Section: SHARING\n",
+ "('sharing_package', 'decentralizepy.sharing.Sharing')\n",
+ "('sharing_class', 'Sharing')\n",
+ "{'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': ''}\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "from decentralizepy.node.Node import Node\n",
+ "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
+ "from decentralizepy.graphs.Graph import Graph\n",
+ "from decentralizepy.mappings.Linear import Linear\n",
+ "from torch import multiprocessing as mp\n",
+ "import torch\n",
+ "import logging\n",
+ "\n",
+ "from localconfig import LocalConfig\n",
+ "\n",
+ "def read_ini(file_path):\n",
+ " config = LocalConfig(file_path)\n",
+ " for section in config:\n",
+ " print(\"Section: \", section)\n",
+ " for key, value in config.items(section):\n",
+ " print((key, value))\n",
+ " print(dict(config.items('DATASET')))\n",
+ " return config\n",
+ " \n",
+ "config = read_ini(\"config.ini\")\n",
+ "my_config = dict()\n",
+ "for section in config:\n",
+ " my_config[section] = dict(config.items(section))\n",
+ "\n",
+ "#f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\n",
+ "g = Graph()\n",
+ "g.read_graph_from_file(\"36_nodes.edges\", \"edges\")\n",
+ "l = Linear(1, 36)\n",
+ "\n",
+ "#Node(0, 0, l, g, my_config, 20, \"results\", logging.DEBUG)\n",
+ "\n",
+ "mp.spawn(fn = Node, nprocs = g.n_procs, args=[0,l,g,my_config,20,\"results\",logging.INFO])\n",
+ "\n",
+ "# mp.spawn(fn = Node, args = [l, g, config, 10, \"results\", logging.DEBUG], nprocs=2)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.mappings.Linear import Linear\n",
+ "from testing import f\n",
+ "from torch import multiprocessing as mp\n",
+ "\n",
+ "l = Linear(1, 2)\n",
+ "mp.spawn(fn = f, nprocs = 2, args = [0, 2, \"ip_addr.json\", l])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.datasets.Femnist import Femnist\n",
+ "\n",
+ "f = Femnist()\n",
+ "\n",
+ "f.file_per_user('../leaf/data/femnist/data/train','../leaf/data/femnist/per_user_data/train')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = set()\n",
+ "a.update([2, 3, 4, 5])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{2, 3, 4, 5}"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2 3 4 5\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(*a)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
+ "\n",
+ "s = SmallWorld(96, 2, .5)\n",
+ "s.write_graph_to_file('96_nodes.edges')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['/home/risharma/miniconda3/envs/decpy/lib/python3.9/site-packages/ipykernel_launcher.py',\n",
+ " '--ip=127.0.0.1',\n",
+ " '--stdin=9008',\n",
+ " '--control=9006',\n",
+ " '--hb=9005',\n",
+ " '--Session.signature_scheme=\"hmac-sha256\"',\n",
+ " '--Session.key=b\"eac5d2f8-c460-45f1-a268-1e4b46a6efd6\"',\n",
+ " '--shell=9007',\n",
+ " '--transport=\"tcp\"',\n",
+ " '--iopub=9009',\n",
+ " '--f=/tmp/tmp-21212479paJaUBJBN84.json']"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "sys.argv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m1_state_dict: OrderedDict([('conv1.weight', tensor([[[[-0.0229, -0.1412, -0.1879, -0.1106, 0.1383],\n",
+ " [-0.0951, -0.0891, -0.0665, 0.0580, 0.1503],\n",
+ " [ 0.0548, -0.1465, -0.1330, 0.0034, 0.0442],\n",
+ " [ 0.0647, 0.1041, -0.0606, 0.0304, 0.1865],\n",
+ " [ 0.1933, -0.1473, -0.0146, 0.1158, -0.1871]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1394, -0.1527, -0.0536, 0.0266, 0.0947],\n",
+ " [ 0.1923, 0.1522, 0.0670, 0.0957, 0.1105],\n",
+ " [ 0.1480, 0.0392, -0.1256, -0.0925, -0.1490],\n",
+ " [-0.0283, 0.1573, -0.1840, 0.1257, -0.0401],\n",
+ " [ 0.1835, 0.0560, -0.1895, -0.1500, 0.0887]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0774, -0.0870, -0.0947, -0.1334, 0.1894],\n",
+ " [ 0.0948, -0.0605, 0.0826, 0.1156, 0.0111],\n",
+ " [-0.1094, -0.1074, 0.0999, -0.1194, 0.1004],\n",
+ " [-0.0173, 0.1559, 0.0715, -0.0290, 0.0097],\n",
+ " [-0.0547, 0.0237, -0.0429, 0.1555, -0.0716]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1742, 0.1413, -0.1431, 0.0339, -0.1187],\n",
+ " [-0.1739, 0.1583, 0.0853, 0.0963, -0.1279],\n",
+ " [ 0.0941, -0.1236, -0.1247, -0.0534, 0.0822],\n",
+ " [-0.0420, 0.1833, 0.0420, -0.0185, 0.0723],\n",
+ " [-0.1710, -0.1717, -0.1689, -0.1454, -0.1634]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1793, 0.0073, 0.1140, -0.1528, -0.1188],\n",
+ " [-0.1980, 0.0458, -0.0361, 0.1428, 0.1635],\n",
+ " [ 0.0007, -0.0679, -0.1566, -0.0512, 0.1546],\n",
+ " [ 0.0979, 0.1354, 0.1986, -0.1026, 0.0206],\n",
+ " [ 0.1713, -0.0090, -0.0990, -0.1577, -0.0561]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0531, -0.1490, 0.0013, 0.1090, 0.0666],\n",
+ " [ 0.0604, 0.0964, 0.1117, -0.0058, 0.1389],\n",
+ " [-0.0228, 0.0183, 0.0797, 0.1482, -0.1976],\n",
+ " [-0.0633, -0.1305, -0.1355, 0.1928, 0.0251],\n",
+ " [ 0.1038, 0.1539, -0.1245, -0.1238, -0.1278]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1392, 0.1037, -0.1825, -0.1580, -0.0792],\n",
+ " [ 0.0841, 0.0298, -0.0681, -0.0392, 0.1775],\n",
+ " [ 0.1716, -0.1017, -0.0676, 0.0086, 0.1156],\n",
+ " [ 0.1659, -0.1998, 0.1957, -0.1146, -0.1235],\n",
+ " [ 0.1529, -0.1974, 0.0128, -0.0563, 0.1234]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0504, 0.0855, 0.1083, 0.1809, 0.0631],\n",
+ " [-0.1540, -0.1761, -0.1962, 0.1521, 0.1945],\n",
+ " [ 0.0082, -0.1470, 0.1477, 0.0105, 0.0649],\n",
+ " [-0.0417, -0.0348, 0.1127, 0.1458, -0.0528],\n",
+ " [ 0.0503, -0.0565, -0.1026, -0.1498, -0.1100]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0269, -0.0011, 0.1104, 0.0586, 0.1700],\n",
+ " [ 0.0510, 0.1185, -0.0363, 0.1386, 0.0394],\n",
+ " [-0.1133, -0.0371, 0.0734, 0.0324, 0.0490],\n",
+ " [ 0.1369, -0.1887, 0.0128, -0.1641, -0.1880],\n",
+ " [-0.1801, -0.1711, 0.0953, 0.0019, -0.0155]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0658, 0.1422, -0.0990, 0.0745, -0.0602],\n",
+ " [ 0.1194, 0.1782, 0.1857, -0.1421, 0.1797],\n",
+ " [ 0.1318, 0.1498, 0.0598, -0.0296, 0.1046],\n",
+ " [ 0.1479, 0.0303, 0.0832, 0.0793, -0.1085],\n",
+ " [-0.0348, -0.0243, 0.1281, 0.1019, -0.1861]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0127, 0.0643, -0.0695, -0.1715, 0.1027],\n",
+ " [ 0.0106, 0.0895, -0.0900, 0.1267, -0.1861],\n",
+ " [ 0.0575, -0.0293, -0.0502, 0.0449, 0.0972],\n",
+ " [ 0.1639, 0.0922, 0.0754, 0.0992, 0.1333],\n",
+ " [-0.1010, -0.1987, -0.1987, 0.0979, 0.0844]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0595, 0.1274, 0.0035, -0.1782, 0.0618],\n",
+ " [-0.1258, 0.1920, -0.1004, -0.1887, 0.1253],\n",
+ " [ 0.0305, -0.0963, 0.1010, -0.1703, -0.0220],\n",
+ " [-0.0885, 0.1817, 0.1438, -0.0833, -0.0235],\n",
+ " [-0.0957, -0.1052, -0.0988, 0.1519, 0.1797]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0704, -0.1125, 0.1092, 0.0502, -0.1922],\n",
+ " [ 0.1264, -0.1142, -0.0028, 0.1783, 0.1064],\n",
+ " [ 0.0479, 0.0609, 0.0481, -0.1642, 0.1164],\n",
+ " [-0.0078, 0.1299, -0.0604, -0.1085, -0.1817],\n",
+ " [-0.1847, 0.0150, 0.0267, -0.0448, 0.1085]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.1162, -0.1187, -0.0052, -0.1902, 0.1876],\n",
+ " [-0.0629, -0.1870, 0.1811, 0.1169, 0.0902],\n",
+ " [ 0.1260, 0.1220, -0.0269, 0.1645, -0.0277],\n",
+ " [ 0.0844, -0.1716, -0.1790, -0.1351, -0.1734],\n",
+ " [-0.1917, -0.0923, 0.1367, 0.0649, -0.1088]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0812, 0.0060, 0.1412, -0.1781, -0.1559],\n",
+ " [ 0.1968, 0.0077, -0.1668, -0.0187, 0.0243],\n",
+ " [ 0.0039, -0.1839, 0.0851, 0.0685, 0.0555],\n",
+ " [-0.1985, 0.0410, 0.1378, 0.0668, 0.1061],\n",
+ " [-0.1742, -0.1719, -0.0100, -0.1634, 0.1688]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1012, -0.1973, -0.0757, 0.0054, -0.1555],\n",
+ " [-0.1071, -0.1538, -0.0328, 0.0623, -0.1842],\n",
+ " [ 0.1411, -0.1391, 0.0815, -0.0975, 0.0353],\n",
+ " [ 0.0047, 0.0009, 0.0916, 0.0259, -0.0072],\n",
+ " [-0.0594, -0.0094, -0.0396, -0.1560, -0.1354]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0084, -0.1336, -0.0328, -0.1315, 0.0229],\n",
+ " [-0.1683, -0.0804, -0.1505, 0.1407, 0.0267],\n",
+ " [-0.1622, -0.1326, 0.1872, -0.0500, -0.0283],\n",
+ " [ 0.0816, -0.1468, -0.1670, 0.0517, 0.0871],\n",
+ " [ 0.0952, 0.1328, 0.1915, 0.0444, -0.0996]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1080, -0.0201, 0.1742, -0.1997, -0.0529],\n",
+ " [ 0.0494, 0.0338, -0.0606, 0.1981, 0.1089],\n",
+ " [ 0.1071, 0.0330, 0.0973, 0.1393, 0.1783],\n",
+ " [-0.0267, 0.0777, 0.0048, -0.1587, -0.0907],\n",
+ " [ 0.1901, 0.0947, 0.0467, -0.0415, -0.1970]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.0452, -0.1996, -0.0107, -0.1986, 0.0665],\n",
+ " [-0.0549, -0.0272, -0.0993, -0.0788, -0.1093],\n",
+ " [-0.0612, 0.1818, 0.1108, -0.1628, 0.0810],\n",
+ " [-0.0778, -0.0030, 0.1858, -0.0210, -0.0162],\n",
+ " [-0.1393, 0.1149, 0.0684, -0.1721, 0.0747]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1577, -0.0780, 0.1825, -0.1546, 0.0453],\n",
+ " [ 0.1300, 0.1881, 0.1711, 0.0382, -0.1831],\n",
+ " [-0.1404, 0.1899, -0.0056, -0.0250, 0.1982],\n",
+ " [ 0.0568, 0.0955, -0.1767, -0.1165, -0.1990],\n",
+ " [ 0.0861, -0.0023, 0.1161, 0.0518, 0.0527]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.1428, 0.1669, -0.1795, -0.1235, 0.1853],\n",
+ " [ 0.0413, 0.1310, -0.1435, 0.1138, 0.1319],\n",
+ " [ 0.0111, 0.1122, 0.0705, -0.1176, 0.0823],\n",
+ " [ 0.1546, -0.1581, -0.0103, 0.1821, -0.1248],\n",
+ " [-0.0897, 0.1802, -0.1877, -0.0283, 0.0269]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.0465, 0.0382, -0.1681, 0.1968, 0.1778],\n",
+ " [-0.1720, -0.1210, 0.0393, -0.1669, 0.0734],\n",
+ " [-0.0806, 0.1679, -0.1045, -0.0793, -0.1499],\n",
+ " [ 0.1467, 0.0118, 0.1104, -0.0798, -0.0674],\n",
+ " [-0.0927, -0.1603, 0.0296, 0.1935, 0.1357]]],\n",
+ "\n",
+ "\n",
+ " [[[ 0.1372, -0.1376, -0.0391, -0.0653, 0.0170],\n",
+ " [-0.0567, -0.1158, -0.1480, -0.1212, 0.1692],\n",
+ " [ 0.0937, -0.1809, -0.0567, 0.0506, 0.0525],\n",
+ " [ 0.0562, -0.0614, -0.0638, 0.0013, -0.1509],\n",
+ " [ 0.0072, -0.1710, 0.0440, -0.0052, 0.0619]]],\n",
+ "\n",
+ "\n",
+ " [[[-0.1553, 0.1425, -0.0342, 0.1451, 0.1473],\n",
+ " [ 0.0821, 0.0459, 0.1424, -0.0007, 0.1109],\n",
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+ " [ 0.1662, 0.1725, 0.1976, -0.0250, -0.0974]]]])), ('conv1.bias', tensor([ 0.1412, -0.1699, -0.0253, -0.1142, 0.0345, -0.1778, 0.0337, -0.1811,\n",
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+ " [-2.0459e-02, 3.2729e-02, -3.4816e-02, 2.6955e-02, 1.1083e-02]],\n",
+ "\n",
+ " ...,\n",
+ "\n",
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+ "\n",
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+ " [-1.4182e-03, 2.7721e-02, -1.1709e-02, -1.0164e-02, 1.3831e-02]],\n",
+ "\n",
+ " ...,\n",
+ "\n",
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+ "\n",
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+ "\n",
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+ "\n",
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+ "\n",
+ " ...,\n",
+ "\n",
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+ "\n",
+ "\n",
+ " ...,\n",
+ "\n",
+ "\n",
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+ "\n",
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+ "\n",
+ " ...,\n",
+ "\n",
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+ "\n",
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+ " [ 4.0170e-03, -3.3140e-02, -2.9632e-02, 1.4946e-03, -1.6940e-02]],\n",
+ "\n",
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+ " [-4.7325e-03, 2.8462e-02, -5.8148e-03, -2.6908e-02, -2.0238e-02]],\n",
+ "\n",
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+ " [ 1.9193e-02, 9.7368e-03, 2.6554e-02, -2.7229e-02, 1.5611e-02]],\n",
+ "\n",
+ " ...,\n",
+ "\n",
+ " [[ 2.5990e-02, 1.8204e-03, 6.3907e-04, 1.3599e-02, -2.4928e-02],\n",
+ " [-4.0177e-03, -9.5758e-03, -3.1354e-02, 3.4710e-02, 1.5811e-02],\n",
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+ "\n",
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+ " [ 2.8540e-02, -1.9069e-02, -8.6731e-03, 6.6561e-03, -7.9887e-03]],\n",
+ "\n",
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+ "\n",
+ "\n",
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+ "\n",
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+ "\n",
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+ " [-2.4607e-03, 1.4686e-03, -2.0640e-02, -3.1963e-02, 3.5608e-03]],\n",
+ "\n",
+ " ...,\n",
+ "\n",
+ " [[ 3.3729e-02, 1.5060e-02, -1.0339e-02, -3.3869e-02, 9.5173e-03],\n",
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+ "\n",
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+ "\n",
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+ " -6.6246e-03, -4.3380e-03, 4.9442e-03, -6.8804e-03, 7.1063e-03,\n",
+ " -4.3328e-03, -5.7675e-03, -7.0120e-03, -1.2138e-02, -1.4318e-02,\n",
+ " -1.2090e-02, 1.6474e-02, 1.5264e-02, -1.3311e-02, 1.5019e-02,\n",
+ " -1.0184e-02, 1.3452e-02, -4.7364e-03, -1.2686e-02, -1.0105e-03,\n",
+ " -7.7642e-03, 1.0269e-02, 1.4772e-02, 2.0888e-03, 1.0636e-02,\n",
+ " 7.9315e-03, 6.5338e-03, -6.3965e-03, 9.0090e-03, -3.8823e-03,\n",
+ " 1.7116e-02, -1.3396e-02, -1.6212e-02, 5.8403e-03, -1.7172e-02,\n",
+ " 1.3479e-02, -4.3699e-03, -7.0589e-03, 1.1175e-02, 1.7116e-02,\n",
+ " 1.2828e-02, 7.4169e-03, 8.4769e-03, -1.2264e-02, 1.6118e-02,\n",
+ " 7.7984e-03, -2.0431e-03, 2.3805e-03, -1.7833e-02, -9.6818e-03,\n",
+ " -7.6913e-03, -1.3305e-02])), ('fc2.weight', tensor([[-0.0175, 0.0319, 0.0240, ..., -0.0283, -0.0260, 0.0342],\n",
+ " [ 0.0135, -0.0069, -0.0351, ..., 0.0021, -0.0435, -0.0082],\n",
+ " [-0.0023, 0.0039, 0.0404, ..., 0.0216, 0.0299, -0.0086],\n",
+ " ...,\n",
+ " [ 0.0134, -0.0336, -0.0370, ..., -0.0342, 0.0213, 0.0314],\n",
+ " [ 0.0109, 0.0286, -0.0269, ..., -0.0081, 0.0009, -0.0038],\n",
+ " [-0.0080, 0.0278, 0.0083, ..., 0.0007, 0.0084, -0.0251]])), ('fc2.bias', tensor([ 2.2357e-02, 1.9226e-02, 1.1123e-02, -2.4498e-03, -1.6226e-02,\n",
+ " 3.9878e-02, -3.7970e-02, 2.9134e-02, 1.5137e-02, 3.2274e-02,\n",
+ " -3.5957e-02, -2.9998e-02, 7.1591e-03, -2.9987e-02, -2.4313e-02,\n",
+ " 4.2241e-02, 9.3610e-03, 1.5294e-02, -1.6146e-02, 3.7757e-02,\n",
+ " 2.1120e-02, -3.7359e-03, -2.3047e-02, 6.2452e-04, -2.0957e-02,\n",
+ " 3.2647e-02, 3.9253e-02, 2.0014e-02, -3.0535e-02, -1.6816e-02,\n",
+ " -3.9067e-02, -1.2906e-02, -2.3393e-02, 4.3545e-02, -3.0294e-02,\n",
+ " -1.3963e-02, 4.1285e-02, 3.9417e-02, 3.4044e-02, 2.5627e-02,\n",
+ " -3.4948e-04, -1.9061e-05, 3.3105e-02, -3.3628e-02, 2.1815e-02,\n",
+ " 2.9446e-02, 2.1422e-02, -1.4810e-03, 2.7396e-02, -1.2598e-02,\n",
+ " 2.5633e-03, -3.4380e-02, -2.7923e-02, -2.9328e-03, 5.5454e-03,\n",
+ " 1.7421e-02, -2.5213e-02, 6.5188e-03, 1.3223e-02, -2.5570e-02,\n",
+ " -2.6113e-02, -8.3992e-03]))])\n",
+ "o1_state_dict: {'state': {}, 'param_groups': [{'lr': 0.6, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'params': [0, 1, 2, 3, 4, 5, 6, 7]}]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from decentralizepy.datasets.Femnist import CNN\n",
+ "m1 = CNN()\n",
+ "o1 = torch.optim.SGD(m1.parameters(), 0.6)\n",
+ "print(\"m1_state_dict: \", m1.state_dict())\n",
+ "print(\"o1_state_dict: \", o1.state_dict())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "996934296aa9d79be6c3d800a38d8fdb7dfa8fe7bb07df178f1397cde2cb8742"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.9.7 64-bit ('tff': conda)",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/testing.py b/eval/testing.py
similarity index 100%
rename from testing.py
rename to eval/testing.py
diff --git a/install_nMachines.sh b/install_nMachines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e9b23753d64258a1fac79a7302b39a966ea48d85
--- /dev/null
+++ b/install_nMachines.sh
@@ -0,0 +1,14 @@
+#!\bin\bash
+
+cd
+mkdir -p Gitlab
+cd Gitlab
+git clone git@gitlab.epfl.ch:risharma/decentralizepy.git
+cd decentralizepy
+mkdir -p leaf/data/femnist/data/train
+mkdir -p leaf/data/femnist/data/test
+mkdir -p leaf/data/femnist/per_user_data/train
+~/miniconda3/bin/conda remove --name decpy --all
+~/miniconda3/bin/conda create -n decpy python=3.9
+~/miniconda3/envs/decpy/bin/pip install --upgrade pip --quiet
+~/miniconda3/envs/decpy/bin/pip install --editable .\[dev\]
diff --git a/main.ipynb b/main.ipynb
deleted file mode 100644
index f3907f05939f9ec1a9b0c3e703d4120df738999c..0000000000000000000000000000000000000000
--- a/main.ipynb
+++ /dev/null
@@ -1,585 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from datasets.Femnist import Femnist\n",
- "from graphs import SmallWorld\n",
- "from collections import defaultdict\n",
- "import os\n",
- "import json\n",
- "import numpy as np\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = FEMNIST\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "b = SmallWorld(6, 2, 2, 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "b.adj_list"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i in range(12):\n",
- " print(b.neighbors(i))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "clients = []"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "num_samples = []\n",
- "data = defaultdict(lambda : None)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "datadir = \"./leaf/data/femnist/data/train\"\n",
- "files = os.listdir(datadir)\n",
- "total_users=0\n",
- "users = set()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "files = os.listdir(datadir)[0:1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for f in files:\n",
- " file_path = os.path.join(datadir, f)\n",
- " print(file_path)\n",
- " with open(file_path, 'r') as inf:\n",
- " client_data = json.load(inf)\n",
- " current_users = len(client_data['users'])\n",
- " print(\"Current_Users: \", current_users)\n",
- " total_users += current_users\n",
- " users.update(client_data['users'])\n",
- "\n",
- "print(\"total_users: \", total_users)\n",
- "print(\"total_users: \", len(users))\n",
- "print(client_data['user_data'].keys())\n",
- "print(np.array(client_data['user_data']['f3408_47']['x']).shape)\n",
- "print(np.array(client_data['user_data']['f3408_47']['y']).shape)\n",
- "print(np.array(client_data['user_data']['f3327_11']['x']).shape)\n",
- "print(np.array(client_data['user_data']['f3327_11']['y']).shape)\n",
- "print(np.unique(np.array(client_data['user_data']['f3327_11']['y'])))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "file = 'run.py'\n",
- "with open(file, 'r') as inf:\n",
- " print(inf.readline().strip())\n",
- " print(inf.readlines())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def f(l):\n",
- " l[2] = 'c'\n",
- "\n",
- "a = ['a', 'a', 'a']\n",
- "print(a)\n",
- "f(a)\n",
- "print(a)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "l = ['a', 'b', 'c']\n",
- "print(l[:-1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from localconfig import LocalConfig\n",
- "\n",
- "def read_ini(file_path):\n",
- " config = LocalConfig(file_path)\n",
- " for section in config:\n",
- " print(\"Section: \", section)\n",
- " for key, value in config.items(section):\n",
- " print((key, value))\n",
- " print(dict(config.items('DATASET')))\n",
- " return config\n",
- " \n",
- "config = read_ini(\"config.ini\")\n",
- "for section in config:\n",
- " print(section)\n",
- "#d = dict(config.sections())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def func(a = 1, b = 2, c = 3):\n",
- " print(a + b + c)\n",
- "\n",
- "l = [3, 5, 7]\n",
- "\n",
- "func(*l)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import multiprocessing as mp\n",
- "\n",
- "mp.spawn(fn = func, nprocs = 2, args = [], kwargs = {'a': 4, 'b': 5, 'c': 6})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "l = '[0.4, 0.2, 0.3, 0.1]'\n",
- "type(eval(l))\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.datasets.Femnist import Femnist\n",
- "f1 = Femnist(0, 1, 'leaf/data/femnist/data/train')\n",
- "ts = f1.get_trainset(1)\n",
- "for data, target in ts:\n",
- " print(data)\n",
- " break"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.datasets.Femnist import Femnist\n",
- "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
- "from decentralizepy.mappings.Linear import Linear\n",
- "\n",
- "f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\n",
- "g = SmallWorld(4, 1, 0.5)\n",
- "l = Linear(2, 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.node.Node import Node\n",
- "from torch import multiprocessing as mp\n",
- "import logging\n",
- "n1 = Node(0, l, g, f, \"./results\", logging.DEBUG)\n",
- "n2 = Node(1, l, g, f, \"./results\", logging.DEBUG)\n",
- "# mp.spawn(fn = Node, nprocs = 2, args=[l,g,f])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from testing import f"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from torch import multiprocessing as mp\n",
- "import torch\n",
- "m1 = torch.nn.Linear(1,1)\n",
- "o1 = torch.optim.SGD(m1.parameters(), 0.6)\n",
- "print(m1)\n",
- "\n",
- "\n",
- "mp.spawn(fn = f, nprocs = 2, args=[m1, o1])\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "o1.param_groups"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with torch.no_grad():\n",
- " o1.param_groups[0][\"params\"][0].copy_(torch.zeros(1,))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "o1.param_groups"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "m1.state_dict()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import torch\n",
- "loss = getattr(torch.nn.functional, 'nll_loss')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "loss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Section: GRAPH\n",
- "('package', 'decentralizepy.graphs.SmallWorld')\n",
- "('graph_class', 'SmallWorld')\n",
- "Section: DATASET\n",
- "('dataset_package', 'decentralizepy.datasets.Femnist')\n",
- "('dataset_class', 'Femnist')\n",
- "('model_class', 'CNN')\n",
- "('n_procs', 36)\n",
- "('train_dir', 'leaf/data/femnist/per_user_data/train')\n",
- "('test_dir', 'leaf/data/femnist/data/test')\n",
- "('sizes', '')\n",
- "Section: OPTIMIZER_PARAMS\n",
- "('optimizer_package', 'torch.optim')\n",
- "('optimizer_class', 'Adam')\n",
- "('lr', 0.01)\n",
- "Section: TRAIN_PARAMS\n",
- "('training_package', 'decentralizepy.training.Training')\n",
- "('training_class', 'Training')\n",
- "('epochs_per_round', 1)\n",
- "('batch_size', 1024)\n",
- "('shuffle', True)\n",
- "('loss_package', 'torch.nn')\n",
- "('loss_class', 'CrossEntropyLoss')\n",
- "Section: COMMUNICATION\n",
- "('comm_package', 'decentralizepy.communication.TCP')\n",
- "('comm_class', 'TCP')\n",
- "('addresses_filepath', 'ip_addr.json')\n",
- "Section: SHARING\n",
- "('sharing_package', 'decentralizepy.sharing.Sharing')\n",
- "('sharing_class', 'Sharing')\n",
- "{'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': ''}\n"
- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "from decentralizepy.node.Node import Node\n",
- "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
- "from decentralizepy.graphs.Graph import Graph\n",
- "from decentralizepy.mappings.Linear import Linear\n",
- "from torch import multiprocessing as mp\n",
- "import torch\n",
- "import logging\n",
- "\n",
- "from localconfig import LocalConfig\n",
- "\n",
- "def read_ini(file_path):\n",
- " config = LocalConfig(file_path)\n",
- " for section in config:\n",
- " print(\"Section: \", section)\n",
- " for key, value in config.items(section):\n",
- " print((key, value))\n",
- " print(dict(config.items('DATASET')))\n",
- " return config\n",
- " \n",
- "config = read_ini(\"config.ini\")\n",
- "my_config = dict()\n",
- "for section in config:\n",
- " my_config[section] = dict(config.items(section))\n",
- "\n",
- "#f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\n",
- "g = Graph()\n",
- "g.read_graph_from_file(\"36_nodes.edges\", \"edges\")\n",
- "l = Linear(1, 36)\n",
- "\n",
- "#Node(0, 0, l, g, my_config, 20, \"results\", logging.DEBUG)\n",
- "\n",
- "mp.spawn(fn = Node, nprocs = g.n_procs, args=[0,l,g,my_config,20,\"results\",logging.INFO])\n",
- "\n",
- "# mp.spawn(fn = Node, args = [l, g, config, 10, \"results\", logging.DEBUG], nprocs=2)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.mappings.Linear import Linear\n",
- "from testing import f\n",
- "from torch import multiprocessing as mp\n",
- "\n",
- "l = Linear(1, 2)\n",
- "mp.spawn(fn = f, nprocs = 2, args = [0, 2, \"ip_addr.json\", l])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.datasets.Femnist import Femnist\n",
- "\n",
- "f = Femnist()\n",
- "\n",
- "f.file_per_user('leaf/data/femnist/data/train','leaf/data/femnist/per_user_data/train')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = set()\n",
- "a.update([2, 3, 4, 5])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{2, 3, 4, 5}"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2 3 4 5\n"
- ]
- }
- ],
- "source": [
- "print(*a)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from decentralizepy.graphs.SmallWorld import SmallWorld\n",
- "\n",
- "s = SmallWorld(36, 2, .5)\n",
- "s.write_graph_to_file('36_nodes.edges')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['/home/risharma/miniconda3/envs/decpy/lib/python3.9/site-packages/ipykernel_launcher.py',\n",
- " '--ip=127.0.0.1',\n",
- " '--stdin=9008',\n",
- " '--control=9006',\n",
- " '--hb=9005',\n",
- " '--Session.signature_scheme=\"hmac-sha256\"',\n",
- " '--Session.key=b\"eac5d2f8-c460-45f1-a268-1e4b46a6efd6\"',\n",
- " '--shell=9007',\n",
- " '--transport=\"tcp\"',\n",
- " '--iopub=9009',\n",
- " '--f=/tmp/tmp-21212479paJaUBJBN84.json']"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import sys\n",
- "sys.argv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "996934296aa9d79be6c3d800a38d8fdb7dfa8fe7bb07df178f1397cde2cb8742"
- },
- "kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('tff': conda)",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- },
- "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/run.py b/run.py
deleted file mode 100644
index ff82fc22662b23e38c049f8eac9025073e9ea1bc..0000000000000000000000000000000000000000
--- a/run.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import torch
-import torch.multiprocessing as mp
-
-x = [1, 2]
-
-
-def f(id, a):
- print(id, x)
- print(id, a)
-
-
-if __name__ == "__main__":
- x.append(3)
- mp.spawn(f, nprocs=2, args=(x,))
diff --git a/src/decentralizepy/sharing/PartialModel.py b/src/decentralizepy/sharing/PartialModel.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..7702d2ebad2a8e6cd7565c43e284c1fa043492b9 100644
--- a/src/decentralizepy/sharing/PartialModel.py
+++ b/src/decentralizepy/sharing/PartialModel.py
@@ -0,0 +1,6 @@
+from decentralizepy.sharing.Sharing import Sharing
+
+class PartialModel(Sharing):
+ def __init__(self, rank, machine_id, communication, mapping, graph, model, dataset):
+ super().__init__(rank, machine_id, communication, mapping, graph, model, dataset)
+