{
"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",
" \n",
"read_ini(\"config.ini\")"
]
},
{
"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": [
"f1 = Femnist(1, 'leaf/data/femnist/data/train')\n",
"f1.instantiate_dataset()\n",
"f1.train_x.shape"
]
},
{
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"from testing import f"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear(in_features=1, out_features=1, bias=True)\n",
"1 OrderedDict([('weight', tensor([[0.9654]])), ('bias', tensor([-0.2141]))])\n",
"1 [{'params': [Parameter containing:\n",
"tensor([[0.9654]], requires_grad=True), Parameter containing:\n",
"tensor([-0.2141], requires_grad=True)], 'lr': 0.6, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False}]\n",
"1 OrderedDict([('weight', tensor([[0.]])), ('bias', tensor([-0.2141]))])\n",
"1 [{'params': [Parameter containing:\n",
"tensor([[0.]], requires_grad=True), Parameter containing:\n",
"tensor([-0.2141], requires_grad=True)], 'lr': 0.6, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False}]\n",
"0 OrderedDict([('weight', tensor([[0.]])), ('bias', tensor([-0.2141]))])\n",
"0 [{'params': [Parameter containing:\n",
"tensor([[0.]], requires_grad=True), Parameter containing:\n",
"tensor([-0.2141], requires_grad=True)], 'lr': 0.6, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False}]\n",
"0 OrderedDict([('weight', tensor([[0.]])), ('bias', tensor([-0.2141]))])\n",
"0 [{'params': [Parameter containing:\n",
"tensor([[0.]], requires_grad=True), Parameter containing:\n",
"tensor([-0.2141], requires_grad=True)], 'lr': 0.6, 'momentum': 0, 'dampening': 0, 'weight_decay': 0, 'nesterov': False}]\n"
]
}
],
"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": []
}
],
"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
}