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"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",
"source": [
"a = FEMNIST\n",
"a"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"b = SmallWorld(6, 2, 2, 1)"
]
},
{
"cell_type": "code",
"source": [
"b.adj_list"
]
},
{
"cell_type": "code",
"source": [
"for i in range(12):\n",
" print(b.neighbors(i))"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"clients = []"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"num_samples = []\n",
"data = defaultdict(lambda : None)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"datadir = \"./leaf/data/femnist/data/train\"\n",
"files = os.listdir(datadir)\n",
"total_users=0\n",
"users = set()"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"files = os.listdir(datadir)[0:1]"
]
},
{
"cell_type": "code",
"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",
"source": [
"file = 'run.py'\n",
"with open(file, 'r') as inf:\n",
" print(inf.readline().strip())\n",
" print(inf.readlines())"
]
},
{
"cell_type": "code",
"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",
"source": [
"l = ['a', 'b', 'c']\n",
"print(l[:-1])"
]
},
{
"cell_type": "code",
"from localconfig import LocalConfig\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",
"config = read_ini(\"config.ini\")\n",
"for section in config:\n",
" print(section)\n",
"#d = dict(config.sections())"
{
"cell_type": "code",
"metadata": {},
"source": [
"def func(a = 1, b = 2, c = 3):\n",
" print(a + b + c)\n",
"\n",
"l = [3, 5, 7]\n",
"\n",
"func(*l)"
]
},
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{
"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"
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]
},
{
"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",
"metadata": {},
"outputs": [],
"source": [
"from testing import f"
]
},
{
"cell_type": "code",
"metadata": {},
"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",
"mp.spawn(fn = f, nprocs = 2, args=[m1, o1])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
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"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()"
]
},
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{
"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": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from decentralizepy.node.Node import Node\n",
"from decentralizepy.graphs.SmallWorld import SmallWorld\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 = SmallWorld(4, 1, 0.5)\n",
"print(g)\n",
"l = Linear(2, 2)\n",
"\n",
"#Node(0, 0, l, g, my_config, 20, \"results\", logging.DEBUG)\n",
"\n",
"#mp.spawn(fn = Node, nprocs = 1, args=[0,l,g,my_config,20,\"results\",logging.DEBUG])\n",
"\n",
"# mp.spawn(fn = Node, args = [l, g, config, 10, \"results\", logging.DEBUG], nprocs=2)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'mp' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_1457289/353106489.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\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;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnprocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ip_addr.json\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\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[0m",
"\u001b[0;31mNameError\u001b[0m: name 'mp' is not defined"
]
}
],
"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"
]
},
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{
"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
}