| #!/bin/bash | |
| PARSED=$(getopt -a -n setup-node -o :n:i: --long name:,ip: -- "$@") | |
| VALID_ARGS=$? | |
| GATEWAY=10.210.14.1 | |
| DNS=10.210.14.2 | |
| HOSTSUFFIX=home.kagesenshi.org | |
| usage() { | |
| echo "Usage: $0 -n [NAME] -i [IP]" |
| import cmd | |
| import argparse | |
| from sqlalchemy import create_engine, text | |
| from sqlalchemy.exc import ProgrammingError | |
| from tabulate import tabulate | |
| class SQLShell(cmd.Cmd): | |
| intro = ("Welcome to SQLAlchemy REPL Shell. Type 'exit' to quit. \n" | |
| "This shell does not execute queries immediately. Press CTRL+D or type 'EOF' to execute. \n") | |
| _default_prompt = "SQL> " |
| FROM centos:stream8 | |
| RUN dnf install epel-release -y && \ | |
| dnf groupinstall "Development Tools" -y --enablerepo powertools && \ | |
| dnf install --enablerepo powertools \ | |
| R-core-devel pandoc \ | |
| python3-devel git \ | |
| openblas \ | |
| java-1.8.0-openjdk-devel gcc gcc-c++ -y && \ | |
| dnf clean all -y |
| mysqlSetupJob: | |
| enabled: false | |
| postgresqlSetupJob: | |
| enabled: true | |
| elasticsearchSetupJob: | |
| extraEnvs: | |
| - name: USE_AWS_ELASTICSEARCH |
| #!/usr/bin/python3 | |
| from subprocess import Popen, PIPE | |
| def start_monitor(domain, interval: int = 10): | |
| proc = Popen(['virsh','dommemstat', domain, '--period', str(interval)], | |
| stdout=PIPE) | |
| proc.wait() | |
| import openai | |
| class SentimentAnalyzer(object): | |
| def __init__(self, language): | |
| self._language = language | |
| def _prompt(self): | |
| prompt = """ | |
| The following sentence is in %s language. |
The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 14 Nov 2012 03:35:10 GMT till Thu, 14 Nov 2013 03:35:10 GMT.
Only first 1000 GitHub users according to the count of followers are taken. This is because of limitations of GitHub search. Sorting algo in pseudocode:
githubUsers
.filter((user) -> user.followers > 0)
| Computer Information: | |
| Manufacturer: Unknown | |
| Model: Unknown | |
| Form Factor: Desktop | |
| No Touch Input Detected | |
| Processor Information: | |
| CPU Vendor: GenuineIntel | |
| CPU Brand: Intel(R) Core(TM) i5-4460 CPU @ 3.20GHz | |
| CPU Family: 0x6 |
| # Data cleansing helper functions | |
| from pyspark.sql import functions as F | |
| from pyspark.sql import Window, DataFrame | |
| from IPython.display import HTML, Markdown | |
| from pyspark.ml.feature import VectorAssembler | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| def transform(self, function, *args, **kwargs): | |
| return function(self, *args, **kwargs) |
| # to use this, set REALM to your KRB realm, and create keytabs for each user in | |
| # /etc/security/keystabs/<username>.jupyter.keytab | |
| # | |
| # Save this file in your site-packages directory as krbspawner.py | |
| # | |
| # then in /etc/jupyterhub_config.py, set: | |
| # | |
| # c.JupyterHub.spawner_class = 'krbspawner.KerberosSpawner' | |