mullikine

Conquering Kubernetes with Emacs

Specific Annoying workflow

Listing pods with kubectl get pods, then select a pod name and copy paste it into kubectl logs [pod name]

Why?

• I want to streamline my workflow and stop using the terminal
• learn more about kubernetes
• main kubernetes extension for Emacs out there is greedy for permissions

mullikine

#!/usr/bin/env python3.6
# -*- coding: utf-8 -*-

# Download a range of URLs in parallel.
# https://gist.github.com/kylemcdonald/3cbd09752e340849e4b3cb4f12dd8c85

from multiprocessing.dummy import Pool
from tqdm import tqdm
from urllib.parse import urlsplit
import urllib3

mullikine

;;; Commentary
;;
;; Allows for more compact anonymous functions.  The following
;; examples demonstrate the usage.
;;
;;     ;; partial application with `curry'
;;     (mapcar (» #'+ 2) '(1 2 3 4)) ; => (3 4 5 6)
;;
;;     ;; alternate order of arguments with `rcurry'
;;     (mapcar (« #'- 1) '(1 2 3 4)) ; => (0 1 2 3)

mullikine

#!/bin/bash
# A bash-wrapped version of http://www.commandlinefu.com/commands/view/9536/un-unzip-a-file
if [ $# -eq 0 ]; then
    echo "Un-unzips a file without a root folder."
    echo "Usage: ununzip <filename>"
    exit 1
elif [ ! -e $1 ]; then
    echo "File not found: $1"
    exit 1
fi

mullikine

# Images
docker build -t friendlyname .  # Create image using this directory's Dockerfile
docker image ls -a                             # List all images on this machine
docker image rm <image id>            # Remove specified image from this machine
docker image rm $(docker image ls -a -q)   # Remove all images from this machine
docker image prune                                      # remove dangling images
docker tag <image> username/repository:tag  # Tag <image> for upload to registry
docker push username/repository:tag            # Upload tagged image to registry
docker run username/repository:tag                   # Run image from a registry

mullikine

Docker Commands, Help & Tips

Show commands & management commands

$ docker

Docker version info

mullikine

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

mullikine

mullikine

# coding: utf-8

# # Predictive text with concatenated word vectors
#
# By [Allison Parrish](http://www.decontextualize.com/)
#
# This notebook demonstrates one way to implement predictive text like [iOS QuickType](https://www.apple.com/sg/ios/whats-new/quicktype/). It works sort of like a [Markov chain text generator](https://github.com/aparrish/rwet/blob/master/ngrams-and-markov-chains.ipynb), but uses nearest-neighbor lookups on a database of concatenated [word vectors](https://github.com/aparrish/rwet/blob/master/understanding-word-vectors.ipynb) instead of n-grams of tokens. You can build this database with any text you want!
#
# To get this code to work, you'll need to [install spaCy](https://spacy.io/usage/#section-quickstart), download a [spaCy model with word vectors](https://spacy.io/usage/models#available) (like `en_core_web_lg`). You'll also need [Simple Neighbors](https://github.com/aparrish/simpleneighbors), a Python library I made for easy nearest neighbor lookups:

mullikine

#!/usr/bin/env python3.6
# -*- coding: utf-8 -*-
# coding: utf-8

# # Semantic similarity chatbots from plain-text files
#
# By [Allison Parrish](http://www.decontextualize.com/)
#
# This needs copy, sorry :( :( :( But it works!