Prune CIFAR10 with PyTorch Environtment # create and prepare a virtual environment conda create -n modelsmith python=3.9 conda activate modelsmith pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 Pruning # Pruning at initialization with random pruning # init_pruning_random.ipynb # Pruning at initialization with magnitude-based pruning # init_pruning_magnitude.ipynb # Pruning at initialization with Grasp # init_pruning_grasp.ipynb # Iterative pruning + retraining # iterative_magnitude_pruning.ipynb # oneshot pruning + retraining # oneshot_magnitude_pruning.ipynb Training Predefined models to train: ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, VGG11, VGG13, VGG16, VGG19 # Start training with: python3 train.py --arch=ResNet18 --epochs=100 # You can manually resume the training with: python3 train.py --resume --lr=0.01