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دانلود کتاب Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach
دانلود کتاب اشکال زدایی پایتون برای هوش مصنوعی، یادگیری ماشین و رایانش ابری: رویکرد الگو محور
مشخصات کتاب
Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach
ویرایش: [1 ed.]
نویسندگان: Dmitry Vostokov
سری:
ISBN (شابک) : 148429744X, 9781484297445
ناشر: Apress
سال نشر: 2024
تعداد صفحات: 254
[244]
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 7 Mb
قیمت کتاب (تومان) : 37,000
در صورت تبدیل فایل کتاب Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب اشکال زدایی پایتون برای هوش مصنوعی، یادگیری ماشین و رایانش ابری: رویکرد الگو محور نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب اشکال زدایی پایتون برای هوش مصنوعی، یادگیری ماشین و رایانش ابری: رویکرد الگو محور
توضیحاتی درمورد کتاب به خارجی
This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you’ll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You’ll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications. What You Will Learn Employ a pattern-oriented approach to Python debugging that starts with diagnostics of common software problems Use tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debugging Understand Python internals for interfacing with operating systems and external modules Perform Python memory dump analysis, tracing, and logging Who This Book Is For Software developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.
فهرست مطالب
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
