Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Last update: Nov 03, 2022

Related tags

Overview

Differentially private Imagenet training

Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis and Abhradeep Thakurta.

This is not an officially supported Google product.

Repository structure

benchmarks directory contains code which we used to compare performance of various DP-SGD frameworks on CIFAR10 and MNIST
imagenet directory contains Imagenet trainign code.

Installation

If you are going to use NVIDIA GPU then install latest NVIDIA drivers, CUDA and CuDNN. While latest versions are not strictly necessary to run the code, we sometimes observed slower performance with older versions of CUDA and CuDNN.

Set up Python virtual environment with all necessary libraries:

# Create virtualenv
virtualenv -p python3 ~/.venv/dp_imagenet
source ~/.venv/dp_imagenet/bin/activate
# Install Objax with CUDA
pip install --upgrade objax
pip install --upgrade jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_releases.html
# Tensorflow and TFDS (for datasets readers)
pip install tensorflow
pip install tensorflow-datasets

Extra libraries for TF and Opacus benchmarks:

pip install tensorflow-privacy
pip install opacus
pip install torchvision
pip install tensorboard

Follow instructions at https://www.tensorflow.org/datasets/catalog/imagenet2012 to download Imagenet dataset for TFDS.

Before running any code, make sure to enter virtual environment and setup PYTHONPATH:

# Enter virtual env, set up path
source ~/.venv/dp_imagenet/bin/activate
cd ${REPOSITORY_DIRECTORY}
export PYTHONPATH=$PYTHONPATH:.

Training Imagenet models with DP

Here are few examples showing how to run Imagenet training with and without DP:

# Resnet50 without DP
python imagenet/imagenet_train.py --tfds_data_dir="${TFDS_DATA_DIR}" --max_eval_batches=10 --eval_every_n_steps=100 --train_device_batch_size=64 --disable_dp

# Resnet18 without DP
python imagenet/imagenet_train.py --tfds_data_dir="${TFDS_DATA_DIR}" --max_eval_batches=10 --eval_every_n_steps=100 --model=resnet18 --train_device_batch_size=64 --disable_dp

# Resnet18 with DP
python imagenet/imagenet_train.py --tfds_data_dir="${TFDS_DATA_DIR}" --max_eval_batches=10 --eval_every_n_steps=100 --model=resnet18 --train_device_batch_size=64

To pre-train model on Places365 and finetune with differential privacy on Imagenet use the following commands:

# Prepare directory for Places365 checkpoint
PLACES_CHECKPOINT_DIR="${HOME}/experiments/places365"
mkdir -p "${PLACES_CHECKPOINT_DIR}"

# Pre-train model on Places365 without differential privacy
# This will train a model to about 55% accuracy on Places365
# when run on 8 GPUs.
python imagenet/imagenet_train.py \
  --tfds_data_dir="${TFDS_DATA_DIR}" \
  --dataset=places365 \
  --eval_every_n_steps=1024 \
  --model=resnet18 \
  --num_train_epochs=80 \
  --lr_warmup_epochs=4 \
  --base_learning_rate=0.05 \
  --disable_dp \
  --train_device_batch_size=128 \
  --model_dir="${PLACES_CHECKPOINT_DIR}"

# Prepare directory for Imagenet checkpoint
IMAGENET_DP_CHECKPOINT_DIR="${HOME}/experiments/imagenet_dp"
mkdir -p "${IMAGENET_DP_CHECKPOINT_DIR}"

# Finetune model on Imagenet with differential privacy.
# This will train a differentially private Imagenet model
# to approximately 48% accuracy with epsilon ~10, delta ~10^{-6}
# when run on 8 GPUs.
# If number of GPUs is different then adjust --grad_acc_steps argument
# such that number_of_gpus*grad_acc_steps = 512.
python imagenet/imagenet_train.py \
  --tfds_data_dir="${TFDS_DATA_DIR}" \
  --eval_every_n_steps=1024 \
  --model=resnet18 \
  --num_train_epochs=70 \
  --dp_clip_norm=1.0 \
  --dp_sigma=0.058014 \
  --grad_acc_steps=64 \
  --base_learning_rate=0.03 \
  --lr_warmup_epochs=1 \
  --num_layers_to_freeze=6 \
  --finetune_path="${PLACES_CHECKPOINT_DIR}/ckpt/0000141312.npz" \
  --model_dir="${IMAGENET_DP_CHECKPOINT_DIR}"

Running DP-SGD benchmarks

Following commands were used to obtain benchmarks of various frameworks for the tech report. All of them were run on n1-standard-96 Google Cloud machine with 8 v100 GPUs. All numbers were obtains with CUDA 11.4 and CuDNN 8.2.2.26.

Objax benchmarks:

# MNIST benchmark without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_objax.py --disable-dp

# MNIST benchmark with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_objax.py

# CIFAR10 benchmark without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_objax.py --disable-dp

# CIFAR10 benchmark with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_objax.py

# Imagenet benchmark Resnet18 without DP
python imagenet/imagenet_train.py --tfds_data_dir="${TFDS_DATA_DIR}" --disable_dp --base_learning_rate=0.2

# Imagenet benchmark Resnet18 with DP
python imagenet/imagenet_train.py --tfds_data_dir="${TFDS_DATA_DIR}" --base_learning_rate=2.0

Opacus benchmarks:

# MNIST benchmark without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_opacus.py --disable-dp

# MNIST benchmark with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_opacus.py

# CIFAR10 benchmark without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_opacus.py --disable-dp

# CIFAR10 benchmark with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_opacus.py

Tensorflow benchmarks:

# MNIST benchmark without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_tf.py --dpsgd=False

# MNIST benchmark with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/mnist_tf.py

# CIFAR10 example without DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_tf.py --dpsgd=False

# CIFAR10 example with DP
CUDA_VISIBLE_DEVICES=0 python benchmarks/cifar10_tf.py

Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Related tags

Overview

Differentially private Imagenet training

Repository structure

Installation

Training Imagenet models with DP

Running DP-SGD benchmarks

Owner

Google Research

This repository contains small projects related to Neural Networks and Deep Learning in general.

A high-performance distributed deep learning system targeting large-scale and automated distributed training.

This repository contains the reference implementation for our proposed Convolutional CRFs.

Unofficial Alias-Free GAN implementation. Based on rosinality's version with expanded training and inference options.

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

[CVPR 2020] Transform and Tell: Entity-Aware News Image Captioning

Capstone-Project-2 - A game program written in the Python language

YOLOX Win10 Project

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

Unofficial implementation of Pix2SEQ

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

This repository contains code from the paper "TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network"

Prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop

一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM，xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt, FiBiNet,AFN,ONN,DIN, DIEN ... （pytorch, tf2.0）

SatelliteSfM - A library for solving the satellite structure from motion problem

Winners of DrivenData's Overhead Geopose Challenge

[CVPR 2021] A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts

(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator