Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach

Table of Contents
About the Author
About the Technical Reviewer
Introduction
Chapter 1: Fundamental Vocabulary
	Process
	Thread
	Stack Trace (Backtrace, Traceback)
	Symbol Files
	Module
	Memory Dump
	Crash
	Hang
	Summary
Chapter 2: Pattern-Oriented Debugging
	The History of the Idea
	Patterns and Analysis Patterns
	Development Process
	Development Patterns
	Debugging Process and Patterns
		Elementary Diagnostics Patterns
		Debugging Analysis Patterns
		Debugging Architecture Patterns
		Debugging Design Patterns
		Debugging Implementation Patterns
		Debugging Usage Patterns
		Debugging Presentation Patterns
	Summary
Chapter 3: Elementary Diagnostics Patterns
	Functional Patterns
		Use-Case Deviation
	Non-Functional Patterns
		Crash
			How to Enable Process Core Dumps on Linux
			How to Enable Process Memory Dumps on Windows
		Hang
			How to Generate Process Core Dumps on Linux
			How to Generate Process Memory Dumps on Windows
		Counter Value
		Error Message
	Summary
Chapter 4: Debugging Analysis Patterns
	Paratext
	State Dump
	Counter Value
	Stack Trace Patterns
		Stack Trace
		Runtime Thread
		Managed Stack Trace
		Source Stack Trace
		Stack Trace Collection
		Stack Trace Set
	Exception Patterns
		Managed Code Exception
		Nested Exception
		Exception Stack Trace
		Software Exception
	Module Patterns
		Module Collection
		Not My Version
		Exception Module
		Origin Module
	Thread Patterns
		Spiking Thread
		Active Thread
		Blocked Thread
		Blocking Module
	Synchronization Patterns
		Wait Chain
		Deadlock
		Livelock
	Memory Consumption Patterns
		Memory Leak
		Handle Leak
	Case Study
	Summary
Chapter 5: Debugging Implementation Patterns
	Overview of Patterns
		Break-Ins
		Code Breakpoint
		Code Trace
		Scope
		Variable Value
		Type Structure
		Breakpoint Action
		Usage Trace
	Case Study
		Elementary Diagnostics Patterns
		Debugging Analysis Patterns
		Debugging Implementation Patterns
	Summary
Chapter 6: IDE Debugging in the  Cloud
	Visual Studio Code
		WSL Setup
		Cloud SSH Setup
		Case Study
	Summary
Chapter 7: Debugging Presentation Patterns
	Python Debugging Engines
	Case Study
	Suggested Presentation Patterns
	Summary
Chapter 8: Debugging Architecture Patterns
	The Where? Category
		In Papyro
		In Vivo
		In Vitro
		In Silico
		In Situ
		Ex Situ
	The When? Category
		Live
		JIT
		Postmortem
	The What? Category
		Code
		Data
		Interaction
	The How? Category
		Software Narrative
		Software State
	Summary
Chapter 9: Debugging Design Patterns
	CI Build Case Study
		Elementary Diagnostics
		Analysis
		Architecture
		Design
		Implementation
	Data Processing Case Study
		Elementary Diagnostics
		Analysis
		Architecture
		Design
		Implementation
	Summary
Chapter 10: Debugging Usage Patterns
	Exact Sequence
	Scripting
	Debugger Extension
	Abstract Command
	Space Translation
	Lifting
	Gestures
	Summary
Chapter 11: Case Study: Resource Leaks
	Elementary Diagnostics
	Debugging Analysis
	Debugging Architecture
	Debugging Implementation
	Summary
Chapter 12: Case Study: Deadlock
	Elementary Diagnostics
	Debugging Analysis
	Debugging Architecture
	Exceptions and Deadlocks
	Summary
Chapter 13: Challenges of Python Debugging in Cloud Computing
	Complex Distributed Systems
		Granularity of Services
			Service Multiplicity
			Localization of Issues
		Communication Channels Overhead
			Nature of Communication
			Payload Discrepancies
			Latency Concerns
			Timeout Configurations
		Inter-Service Dependencies
			Service Chaining
			Service Interactions
			Data Consistency
	Layers of Abstraction
		Opaque Managed Services
		Serverless and Function as a Service
		Container Orchestration Platforms
	Continuous Integration/Continuous Deployment
		Pipeline Failures
			Understanding Failures
			Code Analysis Tools
			Environment Discrepancies
			Environment Simulations
		Rollbacks and Versioning
			Identifying Faulty Deployments
			Efficient Rollbacks
			Blue-Green Deployments
			Database Migrations
		Immutable Infrastructure
			State Preservation
			Resource Proliferation
	Diversity of Cloud Service Models
		Infrastructure as a Service
			Direct Resource Management
			Network Complexity
		Platform as a Service
			Platform Restrictions
			Service Limitations
		Software as a Service
	Evolving Cloud Platforms
		Adapting to Changes
			Service Evolution
			API Changes
			Feature Deprecations
		Staying Updated
			Continuous Learning
			Community Engagement
			Documentation
	Environment Parity
		Library and Dependency Disparities
			Version Variabilities
			Deprecations and Updates
		Configuration Differences
			Environment-Specific Configs
			Secret Management
		Underlying Infrastructure Differences
		Service Variabilities
	Limited Visibility
		Transient Resources
			Ephemeral Instances
			State Replication Challenges
		Log Management
			Volume and Veracity
			Centralization Issues
			Contextual Logging
			Correlating Logs
		Monitoring and Alerting
			Granular Monitoring
			Alert Fatigue
	Latency and Network Issues
		Network Instabilities
		Service-to-Service Communication
		Resource Leaks and Performance
			Slow Degradation
			Garbage Collection
			Profiling
			Tooling Limitations
		Resource Starvation
			Subtle Indicators
			Throttling
			Auto-scaling
			External Influences
	Concurrency Issues
		Race Conditions
		Deadlocks
	Security and Confidentiality
		Debugger Access Control Restrictions
			Limited Access
			Role-Based Access Controls
			Identity and Access Management Policies
			Virtual Private Clouds and Networks
		Sensitive Data Exposure
			Logs and Metrics
			Data Dumps
			Debug Endpoints
			Data Integrity
		Limited Access
	Cost Implications
		Extended Sessions
		Resource Provisioning and Deprovisioning
			Temporary Resources
			Resource Scaling
		Data Transfer and Storage Fees
	State Management
		Stateful Services
		Data Volume
	Limited Tooling Compatibility
	Versioning Issues
		Deprecations and Changes
		SDK and Library Updates
	Real-time Debugging and User Experience
	External Service Dependencies
		Dependency Failures
		Rate Limiting and Quotas
	Asynchronous Operations
		Flow Tracking
		Error Propagation
	Scaling and Load Challenges
		Load-Based Issues
		Resource Contention
	Multi-Tenancy Issues
		Resource Contention
			Isolation
			Rate Limiting
		Data Security
	Reliability and Redundancy Issues
		Service Failures
			Failover Mechanisms
			Backup and Recovery
		Data Durability
			Replication
			Disaster Recovery
	Summary
Chapter 14: Challenges of Python Debugging in AI and Machine Learning
	The Nature of Defects in AI/ML
		Complexity and Abstraction Layers
		Non-Determinism and Reproducibility
		Large Datasets
		High-Dimensional Data
		Long Training Times
		Real-Time Operation
		Model Interpretability
		Hardware Challenges
		Version Compatibility and Dependency Hell
	Data Defects
		Inconsistent and Noisy Data
		Data Leakage
		Imbalanced Data
		Data Quality
		Feature Engineering Flaws
	Algorithmic and Model-Specific Defects
		Gradients, Backpropagation, and Automatic Differentiation
		Hyperparameter Tuning
		Overfitting and Underfitting
		Algorithm Choice
	Deep Learning Defects
		Activation and Loss Choices
		Learning Rate
	Implementation Defects
		Tensor Shapes
		Hardware Limitations and Memory
		Custom Code
		Performance Bottlenecks
	Testing and Validation
		Unit Testing
		Model Validation
		Cross-Validation
		Metrics Monitoring
	Visualization for Debugging
		TensorBoard
		Matplotlib and Seaborn
		Model Interpretability
	Logging and Monitoring
		Checkpoints
		Logging
		Alerts
		Error Tracking Platforms
	Collaborative Debugging
		Forums and Communities
		Peer Review
	Documentation, Continuous Learning, and Updates
		Maintaining Documentation
		Library Updates
		Continuous Learning
	Case Study
	Summary
Chapter 15: What AI and Machine Learning Can Do for Python Debugging
	Automated Error Detection
	Intelligent Code Fix Suggestions
	Interaction Through Natural Language Queries
	Visual Debugging Insights
	Diagnostics and Anomaly Detection
	Augmenting Code Reviews
	Historical Information Analysis and Prognostics
	Adaptive Learning and Personalized Debugging Experience
	Test Suite Integration and Optimization
	Enhanced Documentation and Resource Suggestions
	Problem Modeling
	Generative Debugging Strategy
	Help with In Papyro Debugging
	Summary
Chapter 16: The List of Debugging Patterns
	Elementary Diagnostics Patterns
	Debugging Analysis Patterns
	Debugging Architecture Patterns
	Debugging Design Patterns
	Debugging Implementation Patterns
	Debugging Usage Patterns
	Debugging Presentation Patterns
Index