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Test connection to Redis with netcat

echo -e '*1\r\n$4\r\nPING\r\n' | nc redis.host.com 6379

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#!/bin/bash
sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 2930ADAE8CAF5059EE73BB4B58712A2291FA4AD5
echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu xenial/mongodb-org/3.6 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-3.6.list
sudo apt-get update -y
sudo apt-get install -y mongodb-org
sudo service mongod start
wget http://worldcup.sfg.io/matches 
mongoimport -d mongoaldb -c matches --type json --file matches --jsonArray
wget https://github.com/azat-co/mongoui/archive/master.zip
unzip master.zip

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connect to nvidia container from AWS

start the instance

• Go to ec2 instances console (in our case the region is Ohio)
• Actions -> Instance State -> Start
• Wait for the instance to start
• copy the Public DNS (IPv4) from the instance description

Connect to the instance

to connect with ssh run the following command: ssh -i '/.../tensorrt_test.pem' ubuntu@INSTANCE_PUBLIC_DNS

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def is_contained(sub,lst):
    last_element_index = -1 # dummy value
    if set(sub) > set(lst): # check if sub is subset of lst
        return False
    for i in range(len(sub)): # iterate over sub with for-loop
        if sub[i] in lst: # check if element i in sub exists in lst
            lst_indexes = [j for j, y in enumerate(lst) if y == sub[i]] # list of all occurrences of sub[i] in lst
            if max(lst_indexes) > last_element_index + 1: # check if relevant elements are in ascending order
                last_element_index = min(lst_indexes) # update last index
            else:

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Ontology Learning

• Layer Cake model
• 4 steps content auto classification
• pyLDAvis
• basic text-classification
• text-classification on stack overflow
• free book about text mining
• database of knowledge
• nice presentation

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Largest Files Windows

The following command show the 20 largest files on C:\ drive:

 dir c:\ -recurse -erroraction silentlycontinue | sort length -descending | select -first 20

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PromQL

Prometheus is a time-series db, it's query language called PromQL.

Here are some analogies from SQL which may help to understand the basics of PromQL better:

• metric ~ sql table
• label ~ sql column

example:

• Count how many containers running on each node:

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Odometry - estimate errors

• N - Encoder resolution
• r - Wheel radius
• b - distance between wheels

The following equations are applied:

$$teta = 2*u/b u = 2*pi*r/n$$

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configure templating for docker service

• create new variable with type Query and name it as you wish
• under Query Options:
  • Query: container_tasks_state{state="running",container_label_com_docker_swarm_service_name=~".*_logstash"}
  • Regex: /.container_label_com_docker_swarm_service_name="(.)",container_label_com_docker_swarm_task_id=/

it will create variables for any service which ends with "logstash"

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#!/usr/bin/env python

import numpy as np
from pylab import *
import scipy.integrate as spi

#Parameter Values
PopIn= {'g1s_young':1/12,'g1i_young':1/12,'g1r_young':1/12,'g1s_mid':1/12,'g1i_mid':1/12,'g1r_mid':1/12,'g1s_old':1/12,'g1i_old':1/12,'g1r_old':1/12,'g1s_vold':1/12,'g1i_vold':1/12,'g1r_vold':1/12}
arr = [1/12]*12
beta= 0.50