{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyTorch Differential Privacy Experiment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting torch\n", " Downloading torch-2.4.0-cp311-none-macosx_11_0_arm64.whl.metadata (26 kB)\n", "Collecting torchvision\n", " Downloading torchvision-0.19.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (6.0 kB)\n", "Collecting opacus\n", " Downloading opacus-1.5.2-py3-none-any.whl.metadata (7.9 kB)\n", "Requirement already satisfied: numpy in /Users/nullzero/Library/Caches/pypoetry/virtualenvs/verida-differential-privacy-OB45ac0m-py3.11/lib/python3.11/site-packages (2.1.0)\n", "Requirement already satisfied: pandas in /Users/nullzero/Library/Caches/pypoetry/virtualenvs/verida-differential-privacy-OB45ac0m-py3.11/lib/python3.11/site-packages (2.2.2)\n", "Requirement already satisfied: filelock in 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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 }