{
"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": 1,
"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"
]
},
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_2255475/3991202644.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;31m#f = Femnist(2, 'leaf/data/femnist/data/train', sizes=[0.6, 0.4])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_graph_from_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"36_nodes.edges\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"edges\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m36\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Gitlab/decentralizepy/src/decentralizepy/graphs/Graph.py\u001b[0m in \u001b[0;36mread_graph_from_file\u001b[0;34m(self, file, type, force_connect)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__insert_edge__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"adjacency\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0mnode_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Gitlab/decentralizepy/src/decentralizepy/graphs/Graph.py\u001b[0m in \u001b[0;36m__insert_edge__\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mdestination\u001b[0m \u001b[0mvertex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \"\"\"\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madj_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madj_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"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": null,
"metadata": {},
"outputs": [],
"source": [
"from decentralizepy.graphs.SmallWorld import SmallWorld\n",
"\n",
"s = SmallWorld(36, 2, .5)"
]
}
],
"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
}