hideandseek

Highly modularized deep learning training library.

Why use hideandseek?

Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inference
Run multiple deep learning experiments in parallel on multiples GPUs (powered by hydra, and python multiprocessing)
Design and analyze experiments scientifically by modifying variables (powered by 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 prettifying codes. (30.10.2022.)

import torch
import torch.nn as nn

# Generate data
x = torch.rand(200,1)
y = 5*x+2

network = nn.Linear(1,1)
dataset = torch.utils.data.TensorDataset(x, y)
criterion = nn.MSELoss()
cfg = {
'lr': 1e-2,
'batch_size': 32,
'epoch': 10 # optional
}

# Training configuration. All you need to train a neural network
kwargs = {
'network':network,
'dataset':dataset,
'cfg_train':cfg,
'criterion':criterion,
'name': 'Test' # optional
}
trainer = hs.N.Node(**kwargs)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trainer.network.to(device)

# Train for predefined number of epochs
trainer.train() # Train for predefined number of epochs
trainer.train(5) # Train for specified number of epochs
trainer.train(epoch=5) # Same thing with trainer.train(5)
trainer.train(step=500) # Train for specified number of updates

trainer.network.cpu()

and simply run multiple batch of experiments with a single line command such as:

python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \
hydra/launcher=joblib hydra.launcher.n_jobs=8
# Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden.

To do

Draw figures to explain hideandseek
GUI for generating experiment scripts when conducting variable sweeps

jsyoo61/hideandseek

hideandseek