hideandseek 0.1.1.3

library for deep learning and privacy preserving deep learning

hideandseek

deep learning and privacy preserving deep learning library.

Why use hideandseek?

• Run multiple deep learning experiements in parallel on multiples GPUs
• Design and analyze experiments scientifically by modifying variables (hydra)
• Modularized machine learning pipeline allows using the same script for all types of experiments
• The same training code can be run in privacy preserving setting by minimal modifications

Currently integrating from experiment codes. (30.10.2021.)

import torch
from omegaconf import OmegaConf
import hideandseek as hs

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cfg = OmegaConf.load('config.yaml') # omegaconf.OmegaConf.DictConfig object
model = DNN() # torch.nn.Module object
train_dataset = dataset # torch.utils.data.Dataset object
kwargs = {
  'model': model,
  'dataset': train_dataset,
  'cfg_train': cfg,
  'criterion': criterion,
}
node = hs.Node(**kwargs)

model.to(device)
node.step(local_T=20, horizon='epoch') # trains for 20 epochs
# node.step(local_T=1000, horizon='step') # trains for 1000 steps
model.cpu()

node.save()

test_results = hs.eval.test(node)
scores = hs.eval.scores(test_results)

To do

• Migrate modules from experiment codes
• GUI for generating experiment scripts when conducting variable sweeps