Notes for using Pulumni and store the deployed state to MinIO (s3)
Start MinIO, log in with minioadmin:minioadmin
and create the bucket my-bucket
.
docker run -p 9000:9000 -p 9001:9001 quay.io/minio/minio server /data --console-address ":9001"
#!/bin/bash | |
USER=rueedlinger | |
REPO=autoencoder-tf | |
NBVIEWER_LINK="https://nbviewer.jupyter.org/github/$USER/$REPO/blob/master" | |
quit() { | |
echo "Quit export notebook script!" | |
exit 1 | |
} |
Notes for using Pulumni and store the deployed state to MinIO (s3)
Start MinIO, log in with minioadmin:minioadmin
and create the bucket my-bucket
.
docker run -p 9000:9000 -p 9001:9001 quay.io/minio/minio server /data --console-address ":9001"
#!/bin/bash | |
DIR_NOTEBOOKS="notebooks" | |
FORMAT="markdown" | |
NBVIEWER_LINK="https://nbviewer.jupyter.org/github/rueedlinger/machine-learning-snippets/blob/master" | |
quit() { | |
echo "quit export notebook script!" | |
exit 1 |
name: Build and Publish Hugo Site (MASTER) | |
on: | |
push: | |
branches: | |
- master | |
jobs: | |
build: | |
name: Publish Hugo Site (MASTER) | |
runs-on: ubuntu-latest | |
steps: |
import sys | |
import os | |
import json | |
import argparse | |
PYTHON_MAJOR_VERSION = sys.version_info.major | |
DEFAULT_HOST = 'localhost' | |
DEFAULT_PORT = '8083' | |
BASE_PATH = '/connectors' |
import numpy as np | |
n = list(range(1, 43)) | |
g = list(range(1, 7)) | |
for i in range(1,15): | |
l = sorted(np.random.choice(n, 6, replace=False)) | |
print(i, '-> ', l , " - ", np.random.choice(g,1)) |
#!/bin/bash | |
fswebcam -r 800x600 input.jpg |
Import-Module ActiveDirectory | |
$ADSearchBase = "OU=Employees,OU=Users,OU=XYZ,DC=ads,DC=XYZ,DC=com" | |
$OutFile = "C:\tmp\ad_export.csv" | |
Get-ADUser -filter * -properties GivenName,Surname,CN,Title,Department,Country,City,EmailAddress,whenCreated -SearchBase $ADSearchBase | select GivenName,Surname,CN,Title,Department,Country,City,EmailAddress,whenCreated | Export-Csv $OutFile -NoTypeInformation -Encoding "UTF8" |