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davidlenz /
Created July 15, 2022 16:42
Get the parameters for job queues and job definitions
def get_job_queues(client):
Get all job queues from aws batch service. Transform into dataframe and filter for valid ones.
extract the repo name from the job queue name. Transform into dict and return it.
:param client:
jq = client.describe_job_queues()["jobQueues"]
jq = pd.DataFrame.from_records(jq)
davidlenz /
Created July 10, 2022 15:57
list of aws instances that can be used with batch
Instance type can only be one of [m6g.xlarge, optimal, m5n, m3.xlarge, r4.16xlarge, r5a.2xlarge, m6gd.xlarge, c5a.2xlarge, c6gd.12xlarge, r5b.16xlarge, m5.large, m6g.2xlarge, m5dn.2xlarge, m6g, m6i, g4dn.2xlarge, i3en.6xlarge, r6g.xlarge, c3, c4, c5ad.xlarge, c5, m6gd, m6gd.12xlarge, i2.xlarge, m5d.12xlarge, m5.metal, m4.4xlarge, inf1, m6g.12xlarge, r5n.4xlarge, c4.large, c5d.2xlarge, z1d, d2, d3, r5d.2xlarge, r5.xlarge, r5b.24xlarge, c4.4xlarge, r6gd.12xlarge, c6gd.2xlarge, m5.8xlarge, c5n.9xlarge, c5n.2xlarge, m6i.24xlarge, r4.2xlarge, c6gn.4xlarge, m5zn.6xlarge, m5d.4xlarge, c6gn, r5ad.xlarge, c6gd, c6g.12xlarge, r5b.xlarge, c5.12xlarge, m6gd.large, m6i.8xlarge, f1, r5n.2xlarge, d3.xlarge, x1.16xlarge, r5n.12xlarge, m5a.4xlarge, g4ad.8xlarge, c5n.4xlarge, g2, g3, i2.8xlarge, m5dn.4xlarge, x1e, m5.24xlarge, g4dn.12xlarge, r5d.12xlarge, m3.2xlarge, m5ad.8xlarge, i3en.3xlarge, c5d.12xlarge, r5a.4xlarge, m5n.xlarge, c6gn.medium, i3.large, c6gd.large, m3.medium, c5a.12xlarge, c5n.xlarge, r5ad.16xlarge, i3.metal
davidlenz /
Created July 5, 2022 18:55
get spot price history
import pandas as pd
from datetime import datetime
from datetime import timedelta
ec2c = boto3.client('ec2')
ec2r = boto3.resource('ec2')
#### The rest of this code maps the instance details to spot price in case you are looking for certain memory or cpu
paginator = ec2c.get_paginator('describe_instance_types')
response_iterator = paginator.paginate( )
davidlenz / cancel jobs in aws batch job queue
Last active February 25, 2022 17:41
cancel all jobs in a job queue
import time
import boto3
job_queue = <'job-queue-name'>
for state in states:
davidlenz / Export-Chocolatey.ps1
Created May 17, 2019 14:16 — forked from alimbada/Export-Chocolatey.ps1
Export installed Chocolatey packages as packages.config - thanks to Matty666
#Put this in Export-Chocolatey.ps1 file and run it:
#Export-Chocolatey.ps1 > packages.config
#You can install the packages using
#choco install packages.config -y
Write-Output "<?xml version=`"1.0`" encoding=`"utf-8`"?>"
Write-Output "<packages>"
choco list -lo -r -y | % { " <package id=`"$($_.SubString(0, $_.IndexOf("|")))`" version=`"$($_.SubString($_.IndexOf("|") + 1))`" />" }
Write-Output "</packages>"
davidlenz /
Created July 25, 2018 09:40
Send Emails using python.
#!/usr/bin/env python
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from smtplib import SMTP
import smtplib
subject = 'Example header'
message = 'Subject: Happy Australia Day!\nHi Everyone! Happy Australia Day! Cheers, Julian'
davidlenz /
Last active August 6, 2019 18:12
Scrape the heise newsticker archive ( using beatifulsoup.
import requests
import bs4 as bs
from bs4 import BeautifulSoup
import pandas as pd
import os
def get_timestamp():
import time, datetime
date_n_time = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H-%M-%S')
return date_n_time
davidlenz /
Last active March 4, 2023 15:09
20 newsgroup dataset from sklearn to csv.
from sklearn.datasets import fetch_20newsgroups
import pandas as pd
def twenty_newsgroup_to_csv():
newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
df = pd.DataFrame([,]).T
df.columns = ['text', 'target']
targets = pd.DataFrame( newsgroups_train.target_names)
davidlenz /
Last active June 16, 2018 18:39
Collect environment information and store to file.
import socket
import os, json
import pip, sys
import platform
env = {}
import aetros
env['aetros_version'] = aetros.__version__
davidlenz /
Last active June 4, 2018 17:51
Download pdf files from arxiv based on a search query.
import time, os
import arxiv
SLEEPTIME = 0.1 # seconds
savedir = './arxiv_papers/{}/'.format(QUERY)
if not os.path.exists(savedir):