sailfish009

Linux Security Guide

Table of Contents

Sysctl
Bootloader
Mandatory Access Control
Sandboxing
Root Tweaks
Extra Tweaks

sailfish009

import base64
import os
import json
import pickle
import uuid
import re

import streamlit as st
import pandas as pd

sailfish009

function perc2color(perc) {
	var r, g, b = 0;
	if(perc < 50) {
		r = 255;
		g = Math.round(5.1 * perc);
	}
	else {
		g = 255;
		r = Math.round(510 - 5.10 * perc);
	}

sailfish009

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software

• NVIDIA graphics card driver (v450.57)
• CUDA (v11.0.2)
• cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

sailfish009

def make_weights_for_balanced_classes(images, nclasses):                        
    count = [0] * nclasses                                                      
    for item in images:                                                         
        count[item[1]] += 1                                                     
    weight_per_class = [0.] * nclasses                                      
    N = float(sum(count))                                                   
    for i in range(nclasses):                                                   
        weight_per_class[i] = N/float(count[i])                                 
    weight = [0] * len(images)                                              
    for idx, val in enumerate(images):                                          

sailfish009

sailfish009

class Ralamb(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for ind in range(10)]
        super(Ralamb, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Ralamb, self).__setstate__(state)

sailfish009

from typing import Union

import torch
import numpy as np


def percentile(t: torch.tensor, q: float) -> Union[int, float]:
    """
    Return the ``q``-th percentile of the flattened input tensor's data.
    

sailfish009

import xmltodict
import pandas as pd

with open("full_database.xml") as db:
    doc = xmltodict.parse(db.read())

values = []
for item in doc['drugbank']['drug']:
    logp = None
    try:

sailfish009

import os, time, sys, subprocess

if len(sys.argv) == 2:
    time.sleep(5)
    print 'track end'
    if sys.platform == 'darwin':
	    subprocess.Popen(['say', 'hello'])
else:
    print 'main begin'
    subprocess.Popen(['python', os.path.realpath(__file__), '0'], close_fds=True)