ruslangrimov

import os
import tarfile
from tqdm.auto import tqdm

tar_file = "/data/images.tar.gz"
extract_to = "/data/"

files = {}

with tarfile.open(tar_file, "r:gz") as file:

ruslangrimov

kill $(ps aux | grep '[p]ython csp_build.py' | awk '{print $2}')

ruslangrimov

from multiprocessing import Pool

with Pool(processes=10) as pool:
    for _ in tqdm(pool.imap_unordered(parse_row, df.itertuples(name=None)), total=len(df)):
        pass

ruslangrimov

ruslangrimov

# Prepare
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install build-essential cmake unzip pkg-config
sudo apt-get install libjpeg-dev libpng-dev libtiff-dev
sudo apt-get install libjasper-dev

# If previous line doesn't work
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update

ruslangrimov

class SaveOutput:
    def __init__(self):
        self.outputs = []
        
    def __call__(self, module, module_in, module_out):
        self.outputs.append(module_out)
        
    def clear(self):
        self.outputs = []
        

ruslangrimov

{
    "window.zoomLevel": 0,
    "editor.tabSize": 4,
    "editor.insertSpaces": true,
    "editor.detectIndentation": false,
    "editor.rulers": [ 80 ],
    "terminal.integrated.inheritEnv": false,
    "python.dataScience.sendSelectionToInteractiveWindow": true,
    "files.trimTrailingWhitespace": true,
    "python.linting.flake8Enabled": true,

ruslangrimov

#!/bin/sh

tmux new-session -d -s watch 'exec watch -n 1 nvidia-smi'
tmux rename-window 'Watch'
tmux select-window -t watch:0
tmux split-window -v 'exec htop'
tmux resize-pane -y 8

ruslangrimov

// This script makes it possible to resume cells output receiving after a notebook reloads.
// Works even when the notebook is open in other browser (output will be shown in all opened windows).
// Works with keras and tqdm (not with tqdm_notebook).
// 
// Add this code into your ~/.jupyter/custom/custom.js

define([
        'base/js/namespace',
        'base/js/promises'
     ], function(Jupyter, promises) {

ruslangrimov

np.random.seed(42)
random.seed(42)
cudnn.benchmark = False
cudnn.deterministic = True
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
torch.set_printoptions(precision=10)