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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datasets.FEMNIST.FEMNIST"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = FEMNIST\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"b = SmallWorld(6, 2, 2, 1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{1, 2, 5, 9, 10},\n",
" {0, 2, 3, 4, 5},\n",
" {0, 1, 3, 8, 10},\n",
" {1, 2, 4, 6, 7, 8, 10},\n",
" {1, 3, 5, 8, 10},\n",
" {0, 1, 4, 6, 9},\n",
" {1, 3, 5, 7, 8, 10},\n",
" {0, 2, 3, 6, 8, 9, 11},\n",
" {1, 2, 3, 4, 6, 7, 11},\n",
" {0, 2, 4, 5, 7, 10, 11},\n",
" {0, 2, 3, 4, 5, 6, 9},\n",
" {0, 4, 7, 8, 9}]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.adj_list"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{1, 2, 5, 9, 10}\n",
"{0, 2, 3, 4, 5}\n",
"{0, 1, 3, 8, 10}\n",
"{1, 2, 4, 6, 7, 8, 10}\n",
"{1, 3, 5, 8, 10}\n",
"{0, 1, 4, 6, 9}\n",
"{1, 3, 5, 7, 8, 10}\n",
"{0, 2, 3, 6, 8, 9, 11}\n",
"{1, 2, 3, 4, 6, 7, 11}\n",
"{0, 2, 4, 5, 7, 10, 11}\n",
"{0, 2, 3, 4, 5, 6, 9}\n",
"{0, 4, 7, 8, 9}\n"
]
}
],
"source": [
"for i in range(12):\n",
" print(b.neighbors(i))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"clients = []"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"num_samples = []\n",
"data = defaultdict(lambda : None)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"files = os.listdir(datadir)[0:1]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"./leaf/data/femnist/data/train/all_data_6_niid_0_keep_0_train_9.json\n",
"Current_Users: 100\n",
"total_users: 200\n",
"total_users: 100\n",
"dict_keys(['f3408_47', 'f3327_11', 'f3417_01', 'f3339_15', 'f3580_22', 'f3414_29', 'f3328_45', 'f3592_19', 'f3516_45', 'f3130_44', 'f3321_36', 'f3284_38', 'f3232_11', 'f3547_04', 'f3265_08', 'f3500_08', 'f3243_44', 'f3349_22', 'f3118_09', 'f3179_39', 'f3381_42', 'f3198_32', 'f3299_12', 'f3237_27', 'f3593_26', 'f3133_33', 'f3591_14', 'f3231_19', 'f3478_49', 'f3447_20', 'f3442_00', 'f3464_12', 'f3293_30', 'f3111_05', 'f3227_14', 'f3146_14', 'f3165_11', 'f3440_33', 'f3379_03', 'f3529_11', 'f3441_24', 'f3253_11', 'f3238_40', 'f3583_09', 'f3256_38', 'f3325_08', 'f3512_31', 'f3214_03', 'f3572_03', 'f3457_40', 'f3419_33', 'f3496_38', 'f3582_25', 'f3205_40', 'f3353_33', 'f3115_25', 'f3517_27', 'f3567_49', 'f3230_21', 'f3336_15', 'f3415_33', 'f3280_34', 'f3294_06', 'f3171_30', 'f3363_42', 'f3105_03', 'f3545_06', 'f3426_23', 'f3102_36', 'f3164_09', 'f3202_01', 'f3365_46', 'f3450_19', 'f3573_02', 'f3290_01', 'f3443_42', 'f3471_02', 'f3136_07', 'f3553_12', 'f3434_00', 'f3537_23', 'f3479_08', 'f3578_27', 'f3286_40', 'f3155_15', 'f3494_34', 'f3460_47', 'f3595_18', 'f3518_46', 'f3433_10', 'f3538_29', 'f3266_12', 'f3375_30', 'f3390_07', 'f3261_00', 'f3221_05', 'f3139_09', 'f3234_23', 'f3341_29', 'f3485_45'])\n",
"(155, 784)\n",
"(155,)\n",
"(164, 784)\n",
"(164,)\n",
"[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 18 19 20 21 22 23 24 25\n",
" 26 27 29 30 31 32 33 34 35 36 37 38 39 40 43 44 45 46 47 48 49 50 51 52\n",
" 53 54 55 56 57 58 60 61]\n"
]
}
],
"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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"import torch\n",
"['import torch.multiprocessing as mp\\n', '\\n', '\\n', 'x = [1, 2]\\n', '\\n', 'def f(id, a):\\n', ' print(id, x)\\n', ' print(id, a)\\n', '\\n', \"if __name__ == '__main__':\\n\", ' x.append(3)\\n', ' mp.spawn(f, nprocs=2, args=(x, ))']\n"
]
}
],
"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": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'a', 'a']\n",
"['a', 'a', 'c']\n"
]
}
],
"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": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b']\n"
]
}
],
"source": [
"l = ['a', 'b', 'c']\n",
"print(l[:-1])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Section: GRAPH\n",
"('package', 'decentralizepy.graphs.SmallWorld')\n",
"('graph_class', 'SmallWorld')\n",
"Section: DATASET\n",
"('dataset_class', 'Femnist')\n",
"('model_class', 'LogisticRegression')\n",
"('n_procs', 1.0)\n",
"('train_dir', '')\n",
"('test_dir', '')\n",
"('sizes', '[0.4, 0.2, 0.3, 0.1]')\n",
"Section: MODEL_PARAMS\n",
"('optimizer_package', 'torch.optim')\n",
"('optimizer_class', 'SGD')\n",
"('lr', 0.1)\n"
"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",
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{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15\n"
]
}
],
"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": 3,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "spawn() got an unexpected keyword argument 'kwargs'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_52405/4231740097.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmultiprocessing\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\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[0mfunc\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;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m6\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;31mTypeError\u001b[0m: spawn() got an unexpected keyword argument 'kwargs'"
]
}
],
"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": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = '[0.4, 0.2, 0.3, 0.1]'\n",
"type(eval(l))"
]
},
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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
}