Return to Python for DevOps, Awesome Python DevOps, Python DevOps Bibliography, DevOps by language

Py4DO 2020

ab (Apache Benchmark), Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)

ACM (AWS Certificate Manager]]), Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate

adhoc predict, adhoc_predict, adhoc_predict from Pickle

Algolia index, creating/updating, Creating an Algolia Index and Updating It

Amazon EC2, Infrastructure as [[Code

Amazon Identity and Access Management (IAM) service, Reading and Writing Files

Amazon Resource Name (ARN), Provisioning an Amazon CloudFront Distribution

Amazon Web Services]] (see AWS)

anonymous function]]s, Anonymous Function]]s

Ansible]], Reading and Writing Files

antipatterns, DevOps, DevOps Antipatterns-Principled]] leadership]]lack of automated build server, No Automated Build Server Antipattern

lack of clear, elevating goal, A clear, elevating goal

lack of collaborative climate, A collaborative climate

lack of competent team members, Competent team members

lack of coordination, Difficulties in Coordination as an Ongoing Accomplishment

lack of external support and recognition, External support and recognition

lack of logging, Flying Blind

lack of principled]] leadership]], Principled]] leadership]]

lack of results-driven structure, A results-driven structure

lack of standards of excellence, Standards of excellence

lack of teamwork, No Teamwork

lack of unified commitment]], Unified commitment]]

Apache Benchmark (ab), Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)

Apache HTTP server, Using Regular Expression]]s to Search Text

API Gateway methods, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

argparse, Using argparse-Using argparse

ARN (Amazon Resource Name), Provisioning an Amazon CloudFront Distribution

assert statement, The Amazing assert

a[[symmetric key encryption, Encryption with Cryptography

automating files and the filesystem, Automating Files and the Filesystem-Paths as Objects with Pathlibencrypting text, Encrypting Text-Encryption with Cryptography

hashing with hashlib, Hashing with Hashlib

large files, Dealing with Large Files

managing files and directories with os.path, Managing Files and Directories Using os.path-Managing Files and Directories Using os.path

os module features, The os Module-The os Module

paths as object with pathlib, Paths as Objects with Pathlib

reading/writing file]]s, Reading and Writing Files-Reading and Writing Files

searching text with regular expression]]s, Using Regular Expression]]s to Search Text

walking directory trees with os.walk, Walking Directory Trees Using os.walk

automation tools, IaC with, A Classification of Infrastructure Automation Tools

AutoML, Level 5: Continuous Delivery of Traditional ML and AutoML

AWS (Amazon Web Services]])about, Infrastructure as [[Code

ACM, Provisioning an SSL Certificate with AWS ACM

CDK, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

CloudFront distribution, Provisioning an Amazon CloudFront Distribution-Provisioning an Amazon CloudFront Distribution

CloudWatch logging, Using Amazon CloudWatch Logging with AWS Lambda

Code Pipeline, Deploying with AWS CodePipeline

creating new Pulumi project for, Creating a New Pulumi Python Project for AWS-Creating a New Pulumi Python Project for AWS

Sagemaker, Cloud Machine learning Platforms

AWS Certificate Manager]] (ACM), Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate

AWS Lambda, Deploying Python Function to AWS Lambda-Deploying Python Function to AWS LambdaAWS CloudWatch logging with, Using Amazon CloudWatch Logging with AWS Lambda

CloudWatch events, Using AWS Lambda with CloudWatch Events

populating AWS [[SQS with, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service

reading AWS [[SQS events from, Reading Amazon [[SQS Events from AWS Lambda-Conclusion

AWS [[SQS (Simple Queuing]] Service)AWS Lambda populating, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service

reading events from AWS Lambda, Reading Amazon [[SQS Events from AWS Lambda-Conclusion

Azure, Deploying Python Function to Azure-Deploying Python Function to Azure

basename() method, Managing Files and Directories Using os.path

Bash (see shells, Bash/ZSH)

basicconfig, The basicconfig

benchmarking, Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)

big data]], Big Data]]-Data Storagedata storage, Data Storage

defining, Big Data]]-Big Data]]

file systems, Filesystems

sources, Data Sources

storage, Data Storage

tools, components, and platforms, Big Data]] Tools, Components, and Platforms-Data Storage

big endian, Talking to the Interpreter with the sys Module

binariesDebian, Producing the binary

RPM, Producing the binary

blkid, Retrieving Specific Device Information

brew, Automated Infrastructure Provisioning with Terraform

C

capstone project, Capstone Project

capsys, Built-in Fixtures

CD (see continuous integration/continuous deployment (CI/CD))

centralized]] logging, Centralized]] Logging

changelogDebian packaging, The changelog file

package management, The changelog

character [[classes, in regex searches, Character [[Classes

character sets, in regex searches, Character Sets

chown tool, strace

CI (see continuous integration)

CI/CD (see continuous integration/continuous deployment)

circus, Management with systemd

class definition, Using click

click, Using click-Using click

click.command, Using click

click.group, Using click

click.option, Using click

cloud computing, Cloud Computing-Case Study Questionscloud services, Types of Cloud Services-Software as a Service

containers, Containers(see also Docker; Kubernetes)

continuous delivery, Continuous Delivery

distributed [[computing challenges and opport[[unities]], Challenges and Opport[[unities]] in Distributed [[Computing

FaaS and serverless, Function as a Service and Serverless

forking process with Pool(), Forking Processes with Pool()-Forking Processes with Pool()

foundations, Cloud Computing Foundations-Cloud Computing Foundations

hardware virtualization, Hardware Virtualization

high-performance servers, Using High-Performance Servers

Iaas, Infrastructure as a Service-Infrastructure as a Service

IaC, Infrastructure as [[Code

MaaS, Metal as a Service

machine learning platforms, Cloud Machine learning Platforms

managing processes with subprocess, Manage Processes with Subprocess-The problem with Python threads

multiprocessing, Using Multiprocessing to Solve Problems

Numba, High Performance Python with Numba

Numba JIT, Using Numba Just in Time Compiler

P[[aaS, Platform as a Service

process management, Process Management-Using High-Performance Servers

Python concurrency/performance/process management in the cloud era, Python Concurrency, Performance, and Process Management in the Cloud Era

S[[aaS, Software as a Service

SDNs, Software Defined Networks

SDS, Software Defined Storage

serverless computing, Serverless Computing-Serverless Computing

types of cloud computing, Types of Cloud Computing

virtualization, Virtualization and Containers-Containers

Cloud9, Serverless Computing

CloudFront distribution, provisioning, Provisioning a CloudFront Distribution

CloudWatch event trigger, Wiring Up CloudWatch Event Trigger

CloudWatch events, Using AWS Lambda with CloudWatch Events

command line, Working with the Command Line-Exercisescase study: turbocharging Python with command-line tool]]s, Case Study: Turbocharging Python with Command-Line Tools-KMeans Clustering

creating command-line tool]]s (see command-line tool]]s)

KMeans clustering, KMeans Clustering

os module, Dealing with the Operating System Using the os Module

running true multicore multithreaded Python using Numba, Running True Multicore Multithreaded Python Using Numba

shells, Working with the Shell-Spawn Processes with the subprocess Module

subprocess module, Spawn Processes with the subprocess Module

sys module, Talking to the Interpreter with the sys Module

command-line tool]]s, Creating Command-Line Tools-Implementing Plug-inscase study: turbocharging Python with, Case Study: Turbocharging Python with Command-Line Tools-KMeans Clustering

creating using argparse, Using argparse-Using argparse

creating using click, Using click-Using click

creating using fire, fire-fire

creating using sys.argv, Using sys.argv-Using sys.argv

implementing plugins, Implementing Plug-ins

KMeans clustering, KMeans Clustering

Numba JIT compiler, Using the Numba Just-in-Time (JIT) Compiler-Using the Numba Just-in-Time (JIT) Compiler

running code on GPU with CUDA Python, Using the GPU with CUDA Python

running true multicore multithreaded Python using Numba, Running True Multicore Multithreaded Python Using Numba

compiling, regex, Compiling

configuration values, staging stack with, Creating Configuration Values for the Staging Stack

confirmation prompts, searching and replacing, Searching and Replacingwith Confirmation Prompts

conftest.py, conftest.py

containers, Containers

context, Kubernetes cluster and, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

continue statement]], continue

continuous delivery, Continuous Delivery

continuous integration/continuous deployment (CI/CD), Continuous Integration and Continuous Deployment-Real-World]] Case Study: NFSOPSAWS Code Pipeline, Deploying with AWS CodePipeline

case study: converting a poorly maintained WordPress site to Hugo, Real-World]] Case Study: Converting a Poorly Maintained WordPress Site to Hugo-Deploying with AWS CodePipeline

case study: deploying a Python App Engine application with Google [[Cloud Build, Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build-Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build

case study: NFSOPS, Real-World]] Case Study: NFSOPS

cloud computing, Containers

converting WordPress to Hugo posts, Converting WordPress to Hugo Posts

creating/updating Algolia index, Creating an Algolia Index and Updating It

deployment with AWS Code Pipeline, Deploying with AWS CodePipeline

Hugo setup, Setting Up Hugo

orchestrating with Makefile, Orchestrating with a Makefile

control file, The control file

CPU utilities, CPU Utilities-Viewing Processes with htop

createrepo, RPM repositories

cryptography library, Encryption with Cryptography-Encryption with Cryptography

CSV, JSON converted from, Converting a CSV File to JSON

CUDA Python, Using the GPU with CUDA Python

D

data [[engineering, Data [[Engineering-Case Study Questionbig data]], Big Data]]-Data Storage

case study, Case Study: Building a Homegrown Data [[Pipeline

creating event-driven lambdas, Creating Event-[[Driven Lambdas

generator pipeline to read and process lines, Generator Pipeline to Read and Process Lines

reading a file, Read a File

reading AWS [[SQS events from AWS Lambda, Reading Amazon [[SQS Events from AWS Lambda-Conclusion

real-time streaming ingestion, Real-Time Streaming Ingestion

serverless, Serverless Data [[Engineering-Conclusion

small data, Small Data

using AWS CloudWatch logging with AWS Lambda, Using Amazon CloudWatch Logging with AWS Lambda

using AWS Lambda to populate AWS [[SQS, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service

using AWS Lambda with CloudWatch events, Using AWS Lambda with CloudWatch Events

wiring up CloudWatch event trigger, Wiring Up CloudWatch Event Trigger

writing a file]], Write a File

YAML, Using YAML

Data Lake, Data Storage

Debian packaging, Debian Packaging-Debian repositorieschangelog file, The changelog file

control file, The control file

Debian repositories, Debian repositories

package files, Package files

producing the binary, Producing the binary

required files, Other required files

Debian repositories, Debian repositories

debuild command-line tool]], Producing the binary

deep learningdefined, Machine Learning Key Terminology

PyTorch, Deep Learning with PyTorch-Print RMSE

describe() method, Reading and Writing Files, Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build

descriptive statistics, Descriptive Statistics

descriptive versioning, Descriptive Versioning

dicts, Dicts

direnv, Reading and Writing Files

dirname() method, Managing Files and Directories Using os.path, Managing Files and Directories Using os.path

disk utilities, Disk Utilities-Retrieving Specific Device Informationmeasuring performance, Measuring Performance-Accurate tests with fio

partitions, Partitions

retrieving specific device information, Retrieving Specific Device Information

Docker, Container Technologies: Docker and Docker Compose-Exercisescreating/building/running/removing images/containers, Creating, Building, Running, and Removing Docker Images and Containers-Creating, Building, Running, and Removing Docker Images and Containers

defining, What Is a Docker Container?

development, Infrastructure as [[Code

porting docker-compose services to new host and operating system, Porting the docker-compose Services to a New Host and Operating System-Porting the docker-compose Services to a New Host and Operating System

publishing images to Docker registry, Publishing Docker Images to a Docker Registry

running container with same image on different host, Running a Docker Container with the Same Image on a Different Host-Running a Docker Container with the Same Image on a Different Host

running multiple containers with Docker Compose, Running Multiple Docker Containers with Docker Compose-Running Multiple Docker Containers with Docker Compose

sklearn flask, Sklearn Flask with Kubernetes and Docker-Scale Input

sklearn-based machine learning model with (see sklearn-based machine learning model)

docker build command, Creating, Building, Running, and Removing Docker Images and Containers

Docker Composeporting to new host and operating system, Porting the docker-compose Services to a New Host and Operating System-Porting the docker-compose Services to a New Host and Operating System

running multiple containers, Running Multiple Docker Containers with Docker Compose-Running Multiple Docker Containers with Docker Compose

docker compose logs command, Running Multiple Docker Containers with Docker Compose

docker images command, Creating, Building, Running, and Removing Docker Images and Containers

docker kill command, Creating, Building, Running, and Removing Docker Images and Containers

docker ps, Running Multiple Docker Containers with Docker Compose

Docker registry, publishing images to, Publishing Docker Images to a Docker Registry

docker rmi command, Creating, Building, Running, and Removing Docker Images and Containers

docker run command, Creating, Building, Running, and Removing Docker Images and Containers

docker stop command, Creating, Building, Running, and Removing Docker Images and Containers

docker-compose exec db, Running Multiple Docker Containers with Docker Compose

docker-compose up -d db command, Running Multiple Docker Containers with Docker Compose

Dockerfile format, Containers

DynamoDB]] table, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

E

EDA (Exploratory Data Analysis), EDAcontinuous delivery, Level 4: Continuous Delivery of Exploratory Data Analysis

supervise]]d machine learning, EDA

elasticsearch, The ELK Stack, Elasticsearch and Kibana-Elasticsearch and Kibana

ELK (Elasticsearch, Logstash, Kibana) stack, The ELK Stack-Elasticsearch and Kibanaelasticsearch and kibana, Elasticsearch and Kibana-Elasticsearch and Kibana

Logstash, Logstash-Logstash

encrypting text, Encrypting Text-Encryption with Cryptographycryptography library, Encryption with Cryptography-Encryption with Cryptography

hashing with hashlib, Hashing with Hashlib

EnvironFilter, Deeper Configuration

event-driven lambda, Creating Event-[[Driven Lambdas

exception [[handling, Handling Exceptions

execution control, Execution Control-continuefor loops, for Loops

if/elif/else statements, if/elif/else

expanduser() function, Managing Files and Directories Using os.path

expensive_operation(), Common Patterns

Exploratory Data Analysis (see EDA)

F

FaaS (see function as a service)

faas-cli build command, Deploying Python Function to OpenFaaS

faas-cli login command, Deploying Python Function to OpenFaaS

faas-cli push command, Deploying Python Function to OpenFaaS

fault [[tolerance, Fault [[Tolerance

f[[disk, Retrieving Specific Device Information

feature, defined, Machine Learning Key Terminology

files, automating (see automating files and the filesystem)

filtering]] processes, Listing and Filtering]] Processes

findall() command, Find All

finditer() method, Find Iterator, Using Regular Expression]]s to Search Text

find_packages() function, Package files

fio, Accurate tests with fio

fire, fire-fire

fitting, of machine learning model, Fit the model, Fit Model

Foord, Michael, Michael Foord-Michael Foord

for loops, for Loops

forking process with pool(), Forking Processes with Pool()-Forking Processes with Pool()

function as a service (FaaS), Function as a Service and ServerlessopenFaas, Deploying Python Function to OpenFaaS-Deploying Python Function to OpenFaaS

self-hosted, Deploying a Python Function to Self-Hosted FaaS Platforms-Deploying Python Function to OpenFaaS

Function as a Service (FaaS), Serverless Technologies

function decorators, Using click

functions (generally), Functions-Anonymous Function]]sanatomy, Anatomy of a Function

anonymous, Anonymous Function]]s

as objects, Functions as Objects

G

garbage collection, Dealing with Large Files

GCP (Google [[Cloud Platform), launching GKE Kubernetes cluster in, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi

generate_latest(), Prometheus

generator comprehensions, Generator Comprehensions

generator pipeline, read and process lines with, Generator Pipeline to Read and Process Lines

generators, Generators

getLogger(), Deeper Configuration

get_prefix(), Instrumentation

Gitnested commands, Using argparse

tagging in, When Packaging Might Not Be Needed

GKE Kubernetes clusterdeploying flask example application to, Deploying the Flask Example Application to GKE-Deploying the Flask Example Application to GKE

destroying, Destroying the GKE Cluster

launching in GCP with Pulumi, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi

Google [[Cloud Build, Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build-Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build

Google [[Cloud Functions, Deploying Python Function to Google [[Cloud Functions-Deploying Python Function to Google [[Cloud Functions

Google [[Cloud Platform (GCP), launching GKE Kubernetes cluster in, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi

Google Container Engine]] (GKE) (see GKE Kubernetes cluster)

GPU (Graphics Processing Unit), Using the GPU with CUDA Python

Grafana Helm charts, Installing Prometheus and Grafana Helm Charts-Installing Prometheus and Grafana Helm Charts

Graphite, Graphite

groups, defining in regex searches, Groups

H

hardware virtualization, Hardware Virtualization

Harrison, Matt, Matt Harrison-Matt Harrison

hash function]]s, Hashing with Hashlib

hashing, Hashing with Hashlib

hashlib, Hashing with Hashlib

head() method, Reading and Writing Files

hello python function, Deploying Python Function to OpenFaaS

helpers, Customizing the Python Shell

helpers.uuid4(), Customizing the Python Shell

Holzer, Teijo, Teijo Holzer

host.addr, Features and Special Fixtures

host.ansible, Features and Special Fixtures

host.check_output, Features and Special Fixtures

host.docker, Features and Special Fixtures

host.interface, Features and Special Fixtures

host.iptables, Features and Special Fixtures

host.mount_point, Features and Special Fixtures

host.package, Features and Special Fixtures

host.process, Features and Special Fixtures

host.run, Features and Special Fixtures

host.run_expect, Features and Special Fixtures

host.sudo, Features and Special Fixtures

host.system_info, Features and Special Fixtures

htop, Viewing Processes with htop

HTTPbenchmarking with Apache Benchmark, Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)

pytest and, pytest Features

Hugoconverting WordPress posts to Hugo posts, Converting WordPress to Hugo Posts

converting WordPress site to, Real-World]] Case Study: Converting a Poorly Maintained WordPress Site to Hugo-Deploying with AWS CodePipeline

setting up, Setting Up Hugo

hybrid cloud, Types of Cloud Computing

I

Iaas (Infrastructure as a Service), Infrastructure as a Service-Infrastructure as a Service

IaC (see Infrastructure as [[Code)

IAM (Identity and Access Management) service, Reading and Writing Files

IDEs (Integrated Development Environment]]s), Getting Started with pytest

if/elif/else statements, if/elif/else

Infrastructure as a Service (Iaas), Infrastructure as a Service-Infrastructure as a Service

Infrastructure as [[Code (IaC)about, Infrastructure as [[Code, Infrastructure as [[Code

automated infrastructure provisioning with Pulumi, Infrastructure as [[Code-Creating and Deploying a New Stack(see also Pulumi)

automated infrastructure provisioning with Terraform, Automated Infrastructure Provisioning with Terraform-Deleting All AWS Resources Provisioned with Terraform(see also Terraform)

classification of infrastructure automation tools, A Classification of Infrastructure Automation Tools

creating new Pulumi project for AWS, Creating a New Pulumi Python Project for AWS-Creating a New Pulumi Python Project for AWS

manual provisioning, Manual Provisioning

infrastructure testing, Infrastructure Testing-Features and Special Fixturesconnecting to remote nodes, Connecting to Remote Nodes-Connecting to Remote Nodes

features and special fixtures, Features and Special Fixtures

system validation, What Is System Validation?

Testinfra, Introduction to Testinfra

init method, Using click

init system (see systemd)

__init__() method, Using click

Integrated Development Environment]]s (IDEs), Getting Started with pytest

internal package index, Hosting an internal package index-Hosting an internal package index

interpreter, sys module and, Talking to the Interpreter with the sys Module

interviews, Glenn Solomon-Michael FoordAndrew Nguyen, Andrew Nguyen-Andrew Nguyen

Gabriella Roman, Gabriella Roman

Glenn Solomon, Glenn Solomon

Jonathan LaCour, Jonathan LaCour

Joseph Reis, Joseph Reis-Joseph Reis

Matt Harrison, Matt Harrison-Matt Harrison

Michael Foord, Michael Foord-Michael Foord

Rigoberto Roche, Rigoberto Roche-Rigoberto Roche

Teijo Holzer, Teijo Holzer

Ville Tuulos, Ville Tuulos-Ville Tuulos

iostat, Measuring Performance

IPython, IPythonadvanced features, More IPython Features

magic commands, Using IPython magic commands

running Unix shell commands, Using IPython to Run Unix Shell Commands

J

JSON (Javascript Object Notation)about, Reading and Writing Files

CSV converted to, Converting a CSV File to JSON

workflow, JSON Workflow

json.dump() method, Reading and Writing Files

Jupyter Notebook]]s, Jupyter Notebook]]s, Testing Jupyter Notebook]]s with pytest

K

keys() method, Dicts

kibana, The ELK Stack, Elasticsearch and Kibana-Elasticsearch and Kibana

KMeans clustering, KMeans Clustering

kubectl create -f command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

kubectl create command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

kubectl get pvc command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

kubectl logs command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

kubectl port-forward command, Installing Prometheus and Grafana Helm Charts

Kubernetes, Container Orchestration]]: Kubernetes-Exercisesdeploying flask example application to GKE, Deploying the Flask Example Application to GKE-Deploying the Flask Example Application to GKE

deploying Kubernetes manifests to a local Kubernetes cluster based on minikube, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube-Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

destroying GKE cluster, Destroying the GKE Cluster

installing Prometheus and Grafana Helm charts, Installing Prometheus and Grafana Helm Charts-Installing Prometheus and Grafana Helm Charts

launching GKE cluster in GCP with Pulumi, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi

overview of concepts, Short Overview of Kubernetes Concepts

sklearn flask, Sklearn Flask with Kubernetes and Docker-Scale Input

sklearn-based machine learning model with (see sklearn-based machine learning model)

using Kompose to create Kubernetes manifests from docker-compose.yaml, Using Kompose to Create Kubernetes Manifests from docker-compose.yaml

Kubernetes Management Service, Containers

L

LaCour, Jonathan, Jonathan LaCour

Lambda functions, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

large files, automating, Dealing with Large Files

lazy evaluation]], Lazy Evaluation]]generator comprehensions, Generator Comprehensions

generators, Generators

linear regression model, print accuracy of, Print accuracy of linear regression model

Linux utilities, Useful Linux Utilities-Case Study Questionabout, Useful Linux Utilities

Bash and ZSH basics, Working with Bash and ZSH-Converting a CSV File to JSON

CPU utilities, CPU Utilities-Viewing Processes with htop

disk utilities, Disk Utilities-Retrieving Specific Device Information

mixing Python with Bash and ZSH, Mixing Python with Bash and ZSH-Converting a CSV File to JSON

network utilities, Network Utilities-Load Testing with molotov

Python one-liners, Python One-Liners-How Fast Is this Snippet?

strace, strace-strace

listing processes, Listing and Filtering]] Processes

lists, Lists-Lists

little endian, Talking to the Interpreter with the sys Module

LLVM compiler, Using Numba Just in Time Compiler

load testing, Load Testing with molotov-Load Testing with molotov

log handling, Log Handling

logging, Logging-Common Patterns(see also monitoring and logging)

basicconfig, The basicconfig

challenges, Why Is It Hard?

common patterns, Common Patterns

deeper configuration, Deeper Configuration-Deeper Configuration

Logstash, The ELK Stack, Logstash-Logstash

lsblk, Retrieving Specific Device Information

lsof, Viewing Processes with htop

M

Maas (Metal as a Service), Metal as a Service

machine learningcloud machine learning platforms, Cloud Machine learning Platforms

deep learning with PyTorch, Deep Learning with PyTorch-Print RMSE

defining, What Is Machine Learning?-Plot predicted height versus actual height, Machine Learning Key Terminology

key terminology, Machine Learning Key Terminology

maturity model, Machine learning Maturity Model-Level 6: ML Ope[[rational Feedback]] Loop

modeling, Modeling-Plot predicted height versus actual height

Python machine learning ecosystem, Python Machine learning Ecosystem-Print RMSE

sklearn flask with Kubernetes and Docker, MLOps and Machine learning Engineering-Learning Assessments

sklearn regression model, Sklearn Regression Model

supervise]]d machine learning, Supervise]]d Machine Learning-Kernel Density Distribution

major.minor, Descriptive Versioning

major.minor.micro, Descriptive Versioning

Makefile, orchestrating with, Orchestrating with a Makefile

manual provisioningAWS CloudFront distribution, Provisioning an Amazon CloudFront Distribution-Provisioning an Amazon CloudFront Distribution

copying static files to S3, Copying Static Files to S3

deleting all AWS resources, Deleting All AWS Resources Provisioned with Terraform

IaC, Manual Provisioning

provisioning an SSL certificate with AWS ACM, Provisioning an SSL Certificate with AWS ACM

route53]] DNS record, Provisioning a Route 53]] DNS Record

s3 bucket, Provisioning an S3 Bucket]]-Provisioning an S3 Bucket]]

maturity model, for machine learning, Machine learning Maturity Model-Level 6: ML Ope[[rational Feedback]] Loopkey terminology, Machine Learning Key Terminology

level 1: framing, scoped identification, and problem definition, Level 1: Framing, Scoped Identification, and Problem Definition

level 2: continuous delivery of data, Level 2: Continuous Delivery of Data

level 3: continuous delivery of clean data, Level 3: Continuous Delivery of Clean Data-Level 3: Continuous Delivery of Clean Data

level 4: continuous delivery of EDA, Level 4: Continuous Delivery of Exploratory Data Analysis

level 5: continuous delivery of traditional ML and AutoML, Level 5: Continuous Delivery of Traditional ML and AutoML

level 6: ML ope[[rational feedback]] loop, Level 6: ML Ope[[rational Feedback]] Loop

Metal as a Service (Maas), Metal as a Service

minikube, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube-Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube

MLOps (see machine learning)

modeling, for machine learning, Modeling-Plot predicted height versus actual heightdefined, Machine Learning Key Terminology

extract/inspect feature and target, Extract and inspect feature and target

fitting model, Fit the model

plot predicted versus actual height, Plot predicted height versus actual height

print accuracy of linear regression model, Print accuracy of linear regression model

sklearn regression model, Sklearn Regression Model

splitting data, Split the data

molotov, load testing with, Load Testing with molotov-Load Testing with molotov

monitoring and logging, Monitoring-Prometheus, Logging-Common Patternsbasicconfig, The basicconfig

build versus buy decision, Did You Build It or Buy It?

case study, Case Study: Production Database Kills Hard [[Drives

centralized]] logging, Centralized]] Logging

challenges, Why Is It Hard?

common patterns, Common Patterns

deeper configuration, Deeper Configuration-Deeper Configuration

ELK stack, The ELK Stack-Elasticsearch and Kibana

fault [[tolerance, Fault [[Tolerance

Graphite, Graphite

immutable DevOps principles, Immutable DevOps Principles-Fault [[Tolerance

instrumentation, Instrumentation-Naming Conventions

key concepts in building reliable systems, Key Concepts in Building Reliable Systems

namespaces, Naming Conventions

Prometheus, Prometheus-Prometheus

StatsD, StatsD

monkey[[patch, Built-in Fixtures

multicloud, Types of Cloud Computing

multiprocessing, Using Multiprocessing to Solve Problems

N

named groups, in regex searches, Named Groups

namespaces, Naming Conventions

namespacing, Reading and Writing Files

naming conventions, Naming Conventions

native Python packaging, Native Python Packaging-Hosting an internal package indexhosting internal package index, Hosting an internal package index-Hosting an internal package index

package files, Package files-Package files

PyPI, The Python Package Index

Netflix, Cloud Computing Foundations

network utilities, Network Utilities-Load Testing with molotovbenchmarking HTTP with Apache Benchmark, Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)

load testing with molotov, Load Testing with molotov-Load Testing with molotov

SSH tunneling, SSH Tunneling

new stack, creation and deployment of, Creating and Deploying a New Stack-Creating and Deploying a New Stack

NFSOPS, Real-World]] Case Study: NFSOPS

Nginx package, Examples

Nguyen, Andrew, Andrew Nguyen-Andrew Nguyen

Numba, Running True Multicore Multithreaded Python Using Numba, High Performance Python with Numba

Numba Just-in-Time (JIT) Compiler, Using the Numba Just-in-Time (JIT) Compiler-Using the Numba Just-in-Time (JIT) Compiler, Using Numba Just in Time Compiler

Numpy, Machine Learning Key Terminology

O

objectsdefined, What Is an Object?

functions as, Functions as Objects

lists, Lists-Lists

methods and attributes, Object Methods and Attributes

paths as, Paths as Objects with Pathlib

sequences, Sequences-Dicts, Sequence operations

one-liners (see Python one-liners)

open() function, Reading and Writing Files, Reading and Writing Files

openFaas, Deploying Python Function to OpenFaaS-Deploying Python Function to OpenFaaS

ope[[rational feedback]] loop, Level 6: ML Ope[[rational Feedback]] Loop

os module, The os Module-The os Module, Dealing with the Operating System Using the os Module

os.path, Managing Files and Directories Using os.path-Managing Files and Directories Using os.path

os.walk, Walking Directory Trees Using os.walk

P

P[[aaS (Platform as a Service), Platform as a Service

package filesfor Debian packaging, Package files

for native Python packaging, Package files-Package files

package management, Package Management-Case Study Questionabout, Package Management

changelog, The changelog

Debian packaging, Debian Packaging-Debian repositories

descriptive versioning, Descriptive Versioning

guideline]]s, Packaging Guideline]]s-The changelog

importance of, Why Is Packaging Important?

log handling, Log Handling

native Python packaging, Native Python Packaging-Hosting an internal package index

RPM packaging, RPM Packaging-RPM repositories

solutions, Packaging Solutions-RPM repositories

strategies, Choosing a Strategy

systemd for, Management with systemd-The systemd Unit File

unit installation, Installing the Unit

when packaging might not be needed, When Packaging Might Not Be Needed

Pandas package, Reading and Writing Filesabout, Machine Learning Key Terminology

small data tools, Big Data]]

parametrization, Parametrization

parted, Partitions

partitions, Partitions

pathlib, Reading and Writing Files, Paths as Objects with Pathlib

paths, as object with pathlib, Paths as Objects with Pathlib

pecan command-line tool]], Setting It Up

Platform as a Service (P[[aaS), Platform as a Service

plugins, Implementing Plug-ins

pool(), Forking Processes with Pool()-Forking Processes with Pool()

print accuracy of linear regression model, Print accuracy of linear regression model

print(), Debuggers

print() function, Print

private cloud, Types of Cloud Computing

process management, Process Management-Using High-Performance Serversavoiding shell=True, Avoid shell=True

FaaS, Function as a Service and Serverless

forking process with Pool(), Forking Processes with Pool()-Forking Processes with Pool()

high-performance servers, Using High-Performance Servers

multiprocessing, Using Multiprocessing to Solve Problems

Numba, High Performance Python with Numba

Numba JIT, Using Numba Just in Time Compiler

Python thread problems, The problem with Python threads

setting timeouts and handling when appropriate, Set timeouts and handle them when appropriate

subprocess, Manage Processes with Subprocess-The problem with Python threads

Prometheus, Prometheus-Prometheus, Installing Prometheus and Grafana Helm Charts-Installing Prometheus and Grafana Helm Charts

provisioning, manual (see manual provisioning)

ps command, CPU Utilities

psql -U postgres]] command, Porting the docker-compose Services to a New Host and Operating System

public cloud, Types of Cloud Computing

public_read_policy_for_bucket, Creating a New Pulumi Python Project for AWS

Pulumi, Infrastructure as [[Code-Creating and Deploying a New Stackabout, Automated Infrastructure Provisioning with Pulumi

creating configuration values for staging stack, Creating Configuration Values for the Staging Stack

creating new project for AWS, Creating a New Pulumi Python Project for AWS-Creating a New Pulumi Python Project for AWS

creating/deploying a new stack, Creating and Deploying a New Stack-Creating and Deploying a New Stack

launching GKE Kubernetes cluster in GCP, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi

provisioning a CloudFront distribution, Provisioning a CloudFront Distribution

provisioning a Route53]] zone and DNS Records, Provisioning a Route 53]] Zone and DNS Records-Provisioning a Route 53]] Zone and DNS Records

provisioning an ACM SSL certificate, Provisioning an ACM SSL Certificate

pulumi destroy, Destroying the GKE Cluster

pulumi login command, Automated Infrastructure Provisioning with Pulumi

pulumi new command, Launching a GKE Kubernetes Cluster in GCP with Pulumi

pulumi stack init, Creating and Deploying a New Stack

pulumi up, Provisioning a Route 53]] Zone and DNS Records, Provisioning a CloudFront Distribution

pycache, Removing Temporary Python Files

pyclean, Removing Temporary Python Files

PyPI (Python Package Index), Package Management, The Python Package Index

pytest, Pytest for DevOps-Case Study Questionassert statement, The Amazing assert

built-in fixtures, Built-in Fixtures-Built-in Fixtures

conftest.py, conftest.py

connecting to remote nodes, Connecting to Remote Nodes-Connecting to Remote Nodes

examples, Examples-Examples

features, pytest Features-Parametrization, Features and Special Fixtures

fixtures, Fixtures-Built-in Fixtures, Features and Special Fixtures

getting started, Getting Started with pytest-Differences with unittest

infrastructure testing, Infrastructure Testing-Features and Special Fixtures

layouts and conventions, Layouts and conventions

parametrization, Parametrization

system validation, What Is System Validation?

Testinfra, Introduction to Testinfra

testing Jupyter Notebook]]s, Testing Jupyter Notebook]]s with pytest

testing superpowers with, Testing Superpowers with pytest

testing with, Testing with pytest-Layouts and conventions

unittest versus, Differences with unittest-Differences with unittest

Python (basics), Python Essentials]] for DevOps-Exercisesbasic math, Basic Math

built-in functions, Built-in Function]]s

built-in objects, Built-in Objects-Dicts

comments, Comments

execution control, Execution Control-continue

functions, Functions-Anonymous Function]]s

handling exceptions, Handling Exceptions

installing/running, Installing and Running Python

IPython, IPython

IPython advanced features, More IPython Features

Jupyter Notebook]]s, Jupyter Notebook]]s

lazy evaluation]], Lazy Evaluation]]

print function, Print

procedural programming, Procedural Programming-Comments

Python scripts, Python scripts

Python shell, The Python Shell

range function, Range

regular expression]]s, Using Regular Expression]]s-Compiling

variables, Variables

while loops, while Loops

Python App Engine, Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build-Real-World]] Case Study: Deploying a Python App Engine Application with Google [[Cloud Build

Python one-liners, Python One-Liners-How Fast Is this Snippet?debuggers, Debuggers

performance measurement, How Fast Is this Snippet?

Python Package Index (PyPI), Package Management, The Python Package Index

Python shell, The Python Shell

PyTorchdeep learning, Deep Learning with PyTorch-Print RMSE

plot predicted versus actual height, Plot predicted height versus actual height

print RMSE, Print RMSE

regression, Regression with PyTorch

R

range() function, Range

read and process lines, generator pipeline to, Generator Pipeline to Read and Process Lines

read() function, Reading and Writing Files

reading a file, Reading and Writing Files-Reading and Writing Files, Read a File

readlines() method, Reading and Writing Files, Read a File

real-time streaming ingestion, Real-Time Streaming Ingestion

recursive globbing, Recursive Globbing

regular expression]]s, Using Regular Expression]]s-Compilingcharacter [[classes, Character [[Classes

character sets, Character Sets

compiling, Compiling

find all, Find All

find iterator, Find Iterator

groups, Groups

named groups, Named Groups

pulling information from log with, Using Regular Expression]]s to Search Text

searching, Searching

substitution, Substitution

Reis, Joseph, Joseph Reis-Joseph Reis

removing temporary Python files, Removing Temporary Python Files

Roche, Rigoberto, Rigoberto Roche-Rigoberto Roche

Roman, Gabriella, Gabriella Roman

route53]] DNS record, provisioning, Provisioning a Route 53]] DNS Record, Provisioning a Route 53]] Zone and DNS Records-Provisioning a Route 53]] Zone and DNS Records, Provisioning a Route 53]] DNS Record for the Site URL

RPM packaging, RPM Packaging-RPM repositoriesproducing the binary, Producing the binary

repositories, RPM repositories-RPM repositories

spec file, The spec file

rpmbuild command-line tool]], Producing the binary

S

s3 bucketcopying static files to, Copying Static Files to S3

provisioning, Provisioning an S3 Bucket]]-Provisioning an S3 Bucket]]

S[[aaS (Software as a service), Software as a Service

scale input, sklearn flask and, Scale Input, Scale Input

scatterplot, Scatterplot

Scikit-Learn, Machine Learning Key Terminology

SDNs (Software Defined Networks), Software Defined Networks

SDS (Software Defined Storage), Software Defined Storage

searches, regex, Searching

searching, Using Regular Expression]]s to Search Text

sequence operations, Sequence operations

sequences, Sequences-Dictsdicts, Dicts

lists, Lists-Lists

operations, Sequence operations

serverless computing, Serverless Computing-Serverless Computing

serverless data [[engineering, Serverless Data [[Engineering-ConclusionAWS Lambda populating AWS [[SQS, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service

creating event-driven lambdas, Creating Event-[[Driven Lambdas

reading AWS [[SQS events from AWS Lambda, Reading Amazon [[SQS Events from AWS Lambda-Conclusion

using AWS CloudWatch logging with AWS Lambda, Using Amazon CloudWatch Logging with AWS Lambda

using AWS Lambda with CloudWatch events, Using AWS Lambda with CloudWatch Events

wiring up CloudWatch event trigger, Wiring Up CloudWatch Event Trigger

serverless deploy command, Deploying Python Function to AWS Lambda

serverless invoke command, Deploying Python Function to AWS Lambda

serverless technologies, Serverless Technologies-Exercisesabout, Serverless Technologies-Serverless Technologies

deploying Python function to AWS Lambda, Deploying Python Function to AWS Lambda-Deploying Python Function to AWS Lambda

deploying Python function to Azure, Deploying Python Function to Azure-Deploying Python Function to Azure

deploying Python function to Google [[Cloud Functions, Deploying Python Function to Google [[Cloud Functions-Deploying Python Function to Google [[Cloud Functions

deploying Python function to OpenFaas, Deploying Python Function to OpenFaaS-Deploying Python Function to OpenFaaS

deploying Python function to self-hosted Faas platforms, Deploying a Python Function to Self-Hosted FaaS Platforms-Deploying Python Function to OpenFaaS

deploying the same Python function to the Big Three cloud providers, Deploying the Same Python Function to the “Big Three” Cloud Providers-Deploying Python Function to Azure

installing the Serverless framework, Installing Serverless Framework

provisioning DynamoDB]] table/Lambda functions/API Gateway methods using the AWS CDK, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

setattr, Built-in Fixtures

setup() function, Package files

setuptools, The spec file

shells, Working with the Shell-Spawn Processes with the subprocess Modulecustomizing, Customizing the Python Shell

os module functions, Dealing with the Operating System Using the os Module

spawning processes with subprocess module, Spawn Processes with the subprocess Module

sys module, Talking to the Interpreter with the sys Module

shells, Bash/ZSH, Working with Bash and ZSH-Converting a CSV File to JSONchanging directories to module's path, Changing Directories to a Module’s Path

converting CSV file to JSON, Converting a CSV File to JSON

customizing the Python shell, Customizing the Python Shell

determining module existence and finding path to module, Does My Module Exist?

listing and filtering]] processes, Listing and Filtering]] Processes

mixing Python, Mixing Python with Bash and ZSH-Converting a CSV File to JSON

random password generator, Random Password Generator

recursive globbing, Recursive Globbing

removing temporary Python files, Removing Temporary Python Files

searching and replacing with confirmation prompts, Searching and Replacingwith Confirmation Prompts

Unix timestamp, Unix Timestamp

site URL, route53]] DNS record provisioning for, Provisioning a Route 53]] DNS Record for the Site URL

sklearn-based machine learning model, Sklearn Flask with Kubernetes and Docker-Scale Inputadhoc predict, adhoc_predict, adhoc_predict from Pickle

EDA, EDA

evaluating model, Evaluate

fitting model, Fit Model

JSON workflow, JSON Workflow

Kubernetes and Docker, Sklearn Flask with Kubernetes and Docker-Scale Input

modeling, Modeling

scale input, Scale Input, Scale Input

tune scaled GBM, Tune Scaled GBM

sleep command, Set timeouts and handle them when appropriate

small data, Small Data

Software as a service (S[[aaS), Software as a Service

Software Defined Networks (SDNs), Software Defined Networks

Software Defined Storage (SDS), Software Defined Storage

Solomon, Glenn, Glenn Solomon

spawn processes, Spawn Processes with the subprocess Module

spec file, The spec file

split() method, Managing Files and Directories Using os.path

splitting data, Split the data

SQS (Simple Queuing]] Service)AWS Lambda populating, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service

reading events from AWS Lambda, Reading Amazon [[SQS Events from AWS Lambda-Conclusion

SSH tunneling, SSH Tunneling

SSL certificate, provisioning with ACM, Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate

staging stack, Creating Configuration Values for the Staging Stack

static files, copying, Copying Static Files to S3

statistics, descriptive, Descriptive Statistics

StatsD, StatsD, Instrumentation

storage, big data]], Data Storage

strace, Viewing Processes with htop, strace-strace

strings, Strings-Strings

string_to_bool() function, Parametrization

subprocess, Manage Processes with Subprocess-The problem with Python threadsavoiding shell=True, Avoid shell=True

Python thread problems, The problem with Python threads

setting timeouts and handling when appropriate, Set timeouts and handle them when appropriate

spawn processes, Spawn Processes with the subprocess Module

subprocess.run() function, Spawn Processes with the subprocess Module

substitution, in regex searches, Substitution

Subversion (SVN), Useful Linux Utilities

supervise]]d machine learning, Supervise]]d Machine Learning-Kernel Density Distributiondefined, Machine Learning Key Terminology

descriptive statistics, Descriptive Statistics

EDA, EDA

ingest, Ingest

kernel density distribution, Kernel Density Distribution

scatterplot, Scatterplot

supervisord, Management with systemd

SVN (Subversion), Useful Linux Utilities

symmetric key encryption, Encryption with Cryptography

sys module, Talking to the Interpreter with the sys Module

sys.argv, Using sys.argv-Using sys.argv

sys.byteorder, Talking to the Interpreter with the sys Module

sys.getsizeof, Talking to the Interpreter with the sys Module

sys.version_info, Talking to the Interpreter with the sys Module

system validation, What Is System Validation?

systemd, Choosing a Strategy, Management with systemd-The systemd Unit Filelong-running processes, Long-Running Processes

setup, Setting It Up-Setting It Up

unit file, The systemd Unit File

T

target, defined, Machine Learning Key Terminology

temporary Python files, removing, Removing Temporary Python Files

Terraform, Automated Infrastructure Provisioning with Terraform-Deleting All AWS Resources Provisioned with Terraformcopying static files to S3, Copying Static Files to S3

deleting all AWS resources provisioned with Terraform, Deleting All AWS Resources Provisioned with Terraform

provisioning a Route53]] DNS record, Provisioning a Route 53]] DNS Record

provisioning an AWS CloudFront distribution, Provisioning an Amazon CloudFront Distribution-Provisioning an Amazon CloudFront Distribution

provisioning an s3 bucket, Provisioning an S3 Bucket]]-Provisioning an S3 Bucket]]

provisioning an SSL certificate with AWS ACM, Provisioning an SSL Certificate with AWS ACM

terraform apply, Provisioning an S3 Bucket]], Provisioning an Amazon CloudFront Distribution

terraform destroy, Deleting All AWS Resources Provisioned with Terraform

terraform init command, Provisioning an S3 Bucket]]

terraform plan command, Provisioning an S3 Bucket]]

Testinfra, Introduction to Testinfra

test_list_todos() function, Provisioning DynamoDB]] Table, Lambda Functions, and API Gateway Methods Using the AWS CDK

timeit module, How Fast Is this Snippet?

touch README command, Package files

traditional machine learning, continuous delivery of, Level 5: Continuous Delivery of Traditional ML and AutoML

Tuulos, Ville, Ville Tuulos-Ville Tuulos

U

unittest, pytest versus, Differences with unittest-Differences with unittest

unix timestamp, Unix Timestamp

unsupervise]]d machine learning, Machine Learning Key Terminology

V

variables, Variables

vendor lock-in, Did You Build It or Buy It?

versioning, descriptive, Descriptive Versioning

virtualization, Virtualization and Containers-Containers

W

walking directory trees, Walking Directory Trees Using os.walk

war stories, DevOpsautomating yourself out of a job, You’ll Automate Yourself Out of a Job!

film [[studio can't make film, Film Studio Can’t Make Film-Film Studio Can’t Make Film

game studio can't ship game, Game Studio Can’t Ship Game-Game Studio Can’t Ship Game

putting out a fire with a cache and intelligent instrumentation, Putting Out a Fire with a Cache and Intelligent Instrumentation

Python scripts take 60 seconds to launch, Python Scripts Take 60 Seconds to Launch

while loops, while Loops

with statement, Dealing with Small Data Files

write() function, Reading and Writing Files

writing a file]], Reading and Writing Files-Reading and Writing Files

X

xclip, Random Password Generator

XML (Extensible Markup Language), Reading and Writing Files

Y

YAML, Reading and Writing Files, Using YAML

Z

ZSH (see shells, Bash/ZSH)

Fair Use Sources:

• Py4DO 2020