Python for DevOps. Learn Ruthlessly Effective Automation (ebook)(audiobook)(audiobook)

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• Preface
  • What Does DevOps Mean to the Authors?
  • How to Use This Book
    • Conceptual Topics
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Noah
    • Kennedy
    • Alfredo
    • Grig
• 1. Python Essentials for DevOps
  • Installing and Running Python
    • The Python Shell
      • Python scripts
      • IPython
    • Jupyter Notebooks
  • Procedural Programming
    • Variables
    • Basic Math
    • Comments
    • Built-in Functions
    • Print
    • Range
  • Execution Control
    • if/elif/else
    • for Loops
      • continue
  • while Loops
  • Handling Exceptions
  • Built-in Objects
    • What Is an Object?
    • Object Methods and Attributes
    • Sequences
      • Sequence operations
      • Lists
      • Strings
      • Dicts
  • Functions
    • Anatomy of a Function
    • Functions as Objects
    • Anonymous Functions
  • Using Regular Expressions
    • Searching
    • Character Sets
    • Character Classes
    • Groups
    • Named Groups
    • Find All
    • Find Iterator
    • Substitution
    • Compiling
  • Lazy Evaluation
    • Generators
    • Generator Comprehensions
  • More IPython Features
    • Using IPython to Run Unix Shell Commands
      • Using IPython magic commands
  • Exercises
• 2. Automating Files and the Filesystem
  • Reading and Writing Files
  • Using Regular Expressions to Search Text
  • Dealing with Large Files
  • Encrypting Text
    • Hashing with Hashlib
    • Encryption with Cryptography
  • The os Module
  • Managing Files and Directories Using os.path
  • Walking Directory Trees Using os.walk
  • Paths as Objects with Pathlib
• 3. Working with the Command Line
  • Working with the Shell
    • Talking to the Interpreter with the sys Module
    • Dealing with the Operating System Using the os Module
    • Spawn Processes with the subprocess Module
  • Creating Command-Line Tools
    • Using sys.argv
    • Using argparse
    • Using click
    • fire
    • Implementing Plug-ins
  • Case Study: Turbocharging Python with Command-Line Tools
    • Using the Numba Just-in-Time (JIT) Compiler
    • Using the GPU with CUDA Python
    • Running True Multicore Multithreaded Python Using Numba
    • KMeans Clustering
  • Exercises
• 4. Useful Linux Utilities
  • Disk Utilities
    • Measuring Performance
      • Accurate tests with fio
    • Partitions
    • Retrieving Specific Device Information
  • Network Utilities
    • SSH Tunneling
    • Benchmarking HTTP with Apache Benchmark (ab)
    • Load Testing with molotov
  • CPU Utilities
    • Viewing Processes with htop
  • Working with Bash and ZSH
    • Customizing the Python Shell
    • Recursive Globbing
    • Searching and Replacing with Confirmation Prompts
    • Removing Temporary Python Files
    • Listing and Filtering Processes
    • Unix Timestamp
  • Mixing Python with Bash and ZSH
    • Random Password Generator
    • Does My Module Exist?
    • Changing Directories to a Modules Path
    • Converting a CSV File to JSON
  • Python One-Liners
    • Debuggers
    • How Fast Is this Snippet?
  • strace
  • Exercises
  • Case Study Question
• 5. Package Management
  • Why Is Packaging Important?
    • When Packaging Might Not Be Needed
  • Packaging Guidelines
    • Descriptive Versioning
    • The changelog
  • Choosing a Strategy
  • Packaging Solutions
    • Native Python Packaging
      • Package files
      • The Python Package Index
      • Hosting an internal package index
    • Debian Packaging
      • Package files
        • The changelog file
        • The control file
        • Other required files
      • Producing the binary
      • Debian repositories
    • RPM Packaging
      • The spec file
      • Producing the binary
      • RPM repositories
  • Management with systemd
    • Long-Running Processes
    • Setting It Up
    • The systemd Unit File
  • Installing the Unit
    • Log Handling
  • Exercises
  • Case Study Question
• 6. Continuous Integration and Continuous Deployment
  • Real-World Case Study: Converting a Poorly Maintained WordPress Site to Hugo
    • Setting Up Hugo
    • Converting WordPress to Hugo Posts
    • Creating an Algolia Index and Updating It
    • Orchestrating with a Makefile
    • Deploying with AWS CodePipeline
  • Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build
  • Real-World Case Study: NFSOPS
• 7. Monitoring and Logging
  • Key Concepts in Building Reliable Systems
  • Immutable DevOps Principles
    • Centralized Logging
    • Case Study: Production Database Kills Hard Drives
    • Did You Build It or Buy It?
    • Fault Tolerance
  • Monitoring
    • Graphite
    • StatsD
    • Prometheus
  • Instrumentation
    • Naming Conventions
  • Logging
    • Why Is It Hard?
    • The basicconfig
    • Deeper Configuration
    • Common Patterns
  • The ELK Stack
    • Logstash
    • Elasticsearch and Kibana
  • Exercises
  • Case Study Question
• 8. Pytest for DevOps
  • Testing Superpowers with pytest
  • Getting Started with pytest
    • Testing with pytest
      • Layouts and conventions
    • Differences with unittest
  • pytest Features
    • conftest.py
    • The Amazing assert
    • Parametrization
  • Fixtures
    • Getting Started
    • Built-in Fixtures
  • Infrastructure Testing
    • What Is System Validation?
    • Introduction to Testinfra
    • Connecting to Remote Nodes
    • Features and Special Fixtures
  • Examples
  • Testing Jupyter Notebooks with pytest
  • Exercises
  • Case Study Question
• 9. Cloud Computing
  • Cloud Computing Foundations
  • Types of Cloud Computing
  • Types of Cloud Services
    • Infrastructure as a Service
    • Metal as a Service
    • Platform as a Service
    • Serverless Computing
    • Software as a Service
  • Infrastructure as Code
  • Continuous Delivery
  • Virtualization and Containers
    • Hardware Virtualization
    • Software Defined Networks
    • Software Defined Storage
    • Containers
  • Challenges and Opportunities in Distributed Computing
  • Python Concurrency, Performance, and Process Management in the Cloud Era
  • Process Management
    • Manage Processes with Subprocess
      • Avoid shell=True
      • Set timeouts and handle them when appropriate
      • The problem with Python threads
    • Using Multiprocessing to Solve Problems
    • Forking Processes with Pool()
    • Function as a Service and Serverless
    • High Performance Python with Numba
    • Using Numba Just in Time Compiler
    • Using High-Performance Servers
  • Conclusion
  • Exercises
  • Case Study Questions
• 10. Infrastructure as Code
  • A Classification of Infrastructure Automation Tools
  • Manual Provisioning
  • Automated Infrastructure Provisioning with Terraform
    • Provisioning an S3 Bucket
    • Provisioning an SSL Certificate with AWS ACM
    • Provisioning an Amazon CloudFront Distribution
    • Provisioning a Route 53 DNS Record
    • Copying Static Files to S3
    • Deleting All AWS Resources Provisioned with Terraform
  • Automated Infrastructure Provisioning with Pulumi
    • Creating a New Pulumi Python Project for AWS
    • Creating Configuration Values for the Staging Stack
    • Provisioning an ACM SSL Certificate
    • Provisioning a Route 53 Zone and DNS Records
    • Provisioning a CloudFront Distribution
    • Provisioning a Route 53 DNS Record for the Site URL
    • Creating and Deploying a New Stack
  • Exercises
• 11. Container Technologies: Docker and Docker Compose
  • What Is a Docker Container?
  • Creating, Building, Running, and Removing Docker Images and Containers
  • Publishing Docker Images to a Docker Registry
  • Running a Docker Container with the Same Image on a Different Host
  • Running Multiple Docker Containers with Docker Compose
  • Porting the docker-compose Services to a New Host and Operating System
  • Exercises
• 12. Container Orchestration: Kubernetes
  • Short Overview of Kubernetes Concepts
  • Using Kompose to Create Kubernetes Manifests from docker-compose.yaml
  • Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
  • Launching a GKE Kubernetes Cluster in GCP with Pulumi
  • Deploying the Flask Example Application to GKE
  • Installing Prometheus and Grafana Helm Charts
  • Destroying the GKE Cluster
  • Exercises
• 13. Serverless Technologies
  • Deploying the Same Python Function to the Big Three Cloud Providers
    • Installing Serverless Framework
    • Deploying Python Function to AWS Lambda
    • Deploying Python Function to Google Cloud Functions
    • Deploying Python Function to Azure
  • Deploying a Python Function to Self-Hosted FaaS Platforms
    • Deploying Python Function to OpenFaaS
  • Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
  • Exercises
• 14. MLOps and Machine learning Engineering
  • What Is Machine Learning?
    • Supervised Machine Learning
      • Ingest
      • EDA
        • Scatterplot
      • Descriptive Statistics
      • Kernel Density Distribution
    • Modeling
      • Sklearn Regression Model
        • Extract and inspect feature and target
        • Split the data
        • Fit the model
        • Print accuracy of linear regression model
        • Plot predicted height versus actual height
  • Python Machine learning Ecosystem
    • Deep Learning with PyTorch
      • Regression with PyTorch
        • Plot predicted height versus actual height
        • Print RMSE
  • Cloud Machine learning Platforms
  • Machine learning Maturity Model
    • Machine Learning Key Terminology
    • Level 1: Framing, Scope Identification, and Problem Definition
    • Level 2: Continuous Delivery of Data
    • Level 3: Continuous Delivery of Clean Data
    • Level 4: Continuous Delivery of Exploratory Data Analysis
    • Level 5: Continuous Delivery of Traditional ML and AutoML
    • Level 6: ML Operational Feedback Loop
  • Sklearn Flask with Kubernetes and Docker
  • Sklearn Flask with Kubernetes and Docker
    • EDA
    • Modeling
      • Split Data
    • Tune Scaled GBM
    • Fit Model
    • Evaluate
    • adhoc_predict
    • JSON Workflow
    • Scale Input
      • Pickling sklearn
      • Unpickle and predict
    • adhoc_predict from Pickle
    • Scale Input
  • Exercises
  • Case Study Question
  • Learning Assessments
• 15. Data Engineering
  • Small Data
    • Dealing with Small Data Files
  • Write a File
  • Read a File
  • Generator Pipeline to Read and Process Lines
  • Using YAML
  • Big Data
  • Big Data Tools, Components, and Platforms
    • Data Sources
    • Filesystems
    • Data Storage
  • Real-Time Streaming Ingestion
  • Case Study: Building a Homegrown Data Pipeline
  • Serverless Data Engineering
    • Using AWS Lambda with CloudWatch Events
    • Using Amazon CloudWatch Logging with AWS Lambda
    • Using AWS Lambda to Populate Amazon Simple Queue Service
    • Wiring Up CloudWatch Event Trigger
    • Creating Event-Driven Lambdas
    • Reading Amazon SQS Events from AWS Lambda
  • Conclusion
  • Exercises
  • Case Study Question
• 16. DevOps War Stories and Interviews
  • Film Studio Cant Make Film
  • Game Studio Cant Ship Game
  • Python Scripts Take 60 Seconds to Launch
  • Putting Out a Fire with a Cache and Intelligent Instrumentation
  • Youll Automate Yourself Out of a Job!
  • DevOps Antipatterns
    • No Automated Build Server Antipattern
    • Flying Blind
    • Difficulties in Coordination as an Ongoing Accomplishment
    • No Teamwork
      • A clear, elevating goal
      • A results-driven structure
      • Competent team members
      • Unified commitment
      • A collaborative climate
      • Standards of excellence
      • External support and recognition
      • Principled leadership
  • Interviews
    • Glenn Solomon
    • Andrew Nguyen
    • Gabriella Roman
    • Rigoberto Roche
    • Jonathan LaCour
    • Ville Tuulos
    • Joseph Reis
    • Teijo Holzer
    • Matt Harrison
    • Michael Foord
  • Recommendations
  • Exercises
  • Challenges
  • Capstone Project
• Index

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