rahulvigneswaran

# First run the following things
echo "Downloading..."

wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz

echo "Unzipping..."

tar -xf caffe_ilsvrc12.tar.gz && rm -f caffe_ilsvrc12.tar.gz

echo "Done."

rahulvigneswaran

def save_latest(epoch, model_dir, model, optimizer, scheduler=None, wandb_id=None):
        """Saves latest epoch's weights and other necessary things to resume
        """
        model_states = {
            "epoch": epoch,
            "state_dict": model.state_dict(),
            "opt_state_dict": optimizer.state_dict(),
            "sch_state_dict": scheduler.state_dict() if scheduler != None else None,
            "wandb_id_save": wandb_id,        #----> Remove this if you don't use wandb for logging
        }

rahulvigneswaran

Table of contents

• GPU
• CPU
• Storage
• General Training
  • Loss
  • Dataset/Dataloader
  • Misc

rahulvigneswaran

rahulvigneswaran

import os
import numpy as np
from scipy.stats import wasserstein_distance, energy_distance
from matplotlib import pyplot as plt 

epoch_name = "epoch_1_"
epoch = 1

a = os.getcwd()
root, dirs, files = next(os.walk(a))

rahulvigneswaran

import os
import numpy as np
from scipy.stats import wasserstein_distance, energy_distance
from matplotlib import pyplot as plt 

epoch_name = "epoch_1_"
epoch = 1

a = os.getcwd()
root, dirs, files = next(os.walk(a))

rahulvigneswaran

Check this repo :

https://github.com/mseitzer/gpu-monitor

rahulvigneswaran

import os

parser = argparse.ArgumentParser()
parser.add_argument("--gpu", default="0", type=str)



args = parser.parse_args()

rahulvigneswaran

def print_nonzeros(model):
    nonzero = total = 0
    for name, p in model.named_parameters():
        tensor = p.data.cpu().numpy()
        nz_count = np.count_nonzero(tensor)
        total_params = np.prod(tensor.shape)
        nonzero += nz_count
        total += total_params
        print(f'{name:20} | nonzeros = {nz_count:7} / {total_params:7} ({100 * nz_count / total_params:6.2f}%) | total_pruned = {total_params - nz_count :7} | shape = {tensor.shape}')
    print(f'alive: {nonzero}, pruned : {total - nonzero}, total: {total}, Compression rate : {total/nonzero:10.2f}x  ({100 * (total-nonzero) / total:6.2f}% pruned)')