zrruziev

What is NUMA (Non-Uniformed Memory Access)

Non-Uniform Memory Access (NUMA) is one of the computer memory design methods used in multiprocessor systems, and the time to access the memory varies depending on the relative position between the memory and the processor. In the NUMA architecture, when a processor accesses its local memory, it is faster than when it accesses the remote memory. Remote memory refers to memory that is connected to another processor, and local memory refers to memory that is connected to its own processor. In other words, it is a technology to increase memory access efficiency while using multiple processors on one motherboard. When a specific processor runs out of memory, it monopolizes the bus by itself, so other processors have to play. , and designate 'access only here', and call it a NUMA node.

1. Check Nodes

lspci | grep -i nvidia
  
01:00.0 VGA compatible controller: NVIDIA Corporation TU106 [GeForce RTX 2060 12GB] (rev a1)

Mlawrence95

import pandas as pd

def confusion_matrix(df: pd.DataFrame, col1: str, col2: str):
    """
    Given a dataframe with at least
    two categorical columns, create a 
    confusion matrix of the count of the columns
    cross-counts
    
    use like:

recamshak

from sklearn.datasets import make_blobs
from sklearn.cluster import dbscan
from sklearn.cluster._dbscan_inner import dbscan_inner
from sklearn.metrics import pairwise_distances_chunked
from scipy.sparse import csr_matrix
import numpy as np

# dataset
n = 50000
ds, _ = make_blobs(n, 100, 50)

miwong

MNIST tutorial: crafting adversarial examples with the Jacobian-based saliency map attack

This tutorial explains how to use CleverHans together with a TensorFlow model to craft adversarial examples, using the Jacobian-based saliency map approach. This attack is described in details by the following paper. We assume basic knowledge of TensorFlow. If you need help getting CleverHans installed before getting started, you may find our MNIST tutorial on the fast gradient sign method to be useful.

miwong

MNIST tutorial: the fast gradient sign method and adversarial training

This tutorial explains how to use CleverHans together with a TensorFlow model to craft adversarial examples, as well as make the model more robust to adversarial examples. We assume basic knowledge of TensorFlow.

Setup

First, make sure that you have TensorFlow

nzec

Thanks to /u/zpoo32 for reporting several issues in this list!

Deemix

• deemix: just the cli and the library
• deemix-pyweb: the app with a GUI
• deemix-server: just the server part of deemix-pyweb

lucko

Run:

• brew install gnupg pinentry-mac
• git config --global user.signingkey <key>
• git config --global commit.gpgsign true
• git config --global gpg.program gpg

Then add the following line to ~/.gnupg/gpg-agent.conf:

pinentry-program /usr/local/bin/pinentry-mac

goffinet

#!/bin/bash
# Centos 7/8 John the Ripper Installation
#release=(j 1.8.0)
release=(k 1.9.0)
# Check Centos version
if [ -f /etc/redhat-release ] ; then
  source /etc/os-release 
  if [ $VERSION_ID == "8" ] ; then
    packager=dnf
  elif [ $VERSION_ID == "7" ] ; then

dasgoll

# Centos 7.1
yum -y install wget
wget http://li.nux.ro/download/nux/dextop/el7/x86_64/nux-dextop-release-0-5.el7.nux.noarch.rpm
rpm -ivh nux-dextop-release-0-5.el7.nux.noarch.rpm

yum -y install deluge-web


systemctl start deluge-web
systemctl stop firewalld

ashwin

# Helps define a constant in Python
# From Section 6.3: Defining Constants
# Python Cookbook (2 Ed) by Alex Martelli et al.
#
# Usage:
# import const
# const.magic = 23 # First binding is fine
# const.magic = 88 # Second binding raises const.ConstError

class _const: