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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PyTorch Differential Privacy Experiment"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Installing collected packages: mpmath, sympy, pillow, numpy, networkx, torch, scipy, opt-einsum, torchvision, opacus\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 2.1.0\n",
" Uninstalling numpy-2.1.0:\n",
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"Successfully installed mpmath-1.3.0 networkx-3.3 numpy-1.26.4 opacus-1.5.2 opt-einsum-3.3.0 pillow-10.4.0 scipy-1.14.1 sympy-1.13.2 torch-2.4.0 torchvision-0.19.0\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -qqq torch torchvision opacus numpy pandas\n",
"%pip install -qqq wandb datasets tqdm\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch \n",
"from dotenv import load_dotenv\n",
"import wandb \n",
"import logging\n",
"import shutil\n",
"import sys\n",
"from datetime import datetime, timedelta\n",
"\n",
"import argparse\n",
"from collections import Counter\n",
"from pathlib import Path\n",
"from statistics import mean\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from opacus import PrivacyEngine\n",
"from opacus.layers import DPGRU, DPLSTM, DPRNN\n",
"from torch.nn.utils.rnn import pad_sequence\n",
"from torch.utils.data import DataLoader, Dataset\n",
"from tqdm import tqdm, tqdm_notebook"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"device = \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
"if os.path.exists('.env'):\n",
" load_dotenv('.env')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logging.basicConfig(\n",
" format=\"%(asctime)s:%(levelname)s:%(message)s\",\n",
" datefmt=\"%m/%d/%Y %H:%M:%S\",\n",
" stream=sys.stdout,\n",
")\n",
"logger = logging.getLogger(\"ddp\")\n",
"logger.setLevel(level=logging.INFO)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"wandb.login(key=os.getenv('WANDB_API_KEY'))\n",
"wandb.init(project=\"verida-pii\", name=\"verida_data_raw\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_name = 'Ezi/medical_and_legislators_synthetic'"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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