High Performance Python. Practical Performant Programming for Humans. 2nd Edition

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Spis treści książki

• Foreword
• Preface
  • Who This Book Is For
  • Who This Book Is Not For
  • What Youll Learn
  • Python 3
  • Changes from Python 2.7
  • License
  • How to Make an Attribution
  • Errata and Feedback
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Understanding Performant Python
  • The Fundamental Computer System
    • Computing Units
    • Memory Units
    • Communications Layers
  • Putting the Fundamental Elements Together
    • Idealized Computing Versus the Python Virtual Machine
      • Idealized computing
      • Pythons virtual machine
  • So Why Use Python?
  • How to Be a Highly Performant Programmer
    • Good Working Practices
    • Some Thoughts on Good Notebook Practice
    • Getting the Joy Back into Your Work
• 2. Profiling to Find Bottlenecks
  • Profiling Efficiently
  • Introducing the Julia Set
  • Calculating the Full Julia Set
  • Simple Approaches to Timingprint and a Decorator
  • Simple Timing Using the Unix time Command
  • Using the cProfile Module
  • Visualizing cProfile Output with SnakeViz
  • Using line_profiler for Line-by-Line Measurements
  • Using memory_profiler to Diagnose Memory Usage
  • Introspecting an Existing Process with PySpy
  • Bytecode: Under the Hood
    • Using the dis Module to Examine CPython Bytecode
    • Different Approaches, Different Complexity
  • Unit Testing During Optimization to Maintain Correctness
    • No-op @profile Decorator
  • Strategies to Profile Your Code Successfully
  • Wrap-Up
• 3. Lists and Tuples
  • A More Efficient Search
  • Lists Versus Tuples
    • Lists as Dynamic Arrays
    • Tuples as Static Arrays
  • Wrap-Up
• 4. Dictionaries and Sets
  • How Do Dictionaries and Sets Work?
    • Inserting and Retrieving
    • Deletion
    • Resizing
    • Hash Functions and Entropy
  • Dictionaries and Namespaces
  • Wrap-Up
• 5. Iterators and Generators
  • Iterators for Infinite Series
  • Lazy Generator Evaluation
  • Wrap-Up
• 6. Matrix and Vector Computation
  • Introduction to the Problem
  • Arent Python Lists Good Enough?
    • Problems with Allocating Too Much
  • Memory Fragmentation
    • Understanding perf
    • Making Decisions with perfs Output
    • Enter numpy
  • Applying numpy to the Diffusion Problem
    • Memory Allocations and In-Place Operations
    • Selective Optimizations: Finding What Needs to Be Fixed
  • numexpr: Making In-Place Operations Faster and Easier
  • A Cautionary Tale: Verify Optimizations (scipy)
  • Lessons from Matrix Optimizations
  • Pandas
    • Pandass Internal Model
    • Applying a Function to Many Rows of Data
      • Which OLS implementation should we use?
      • Applying lstsq to our rows of data
    • Building DataFrames and Series from Partial Results Rather than Concatenating
    • Theres More Than One (and Possibly a Faster) Way to Do a Job
    • Advice for Effective Pandas Development
  • Wrap-Up
• 7. Compiling to C
  • What Sort of Speed Gains Are Possible?
  • JIT Versus AOT Compilers
  • Why Does Type Information Help the Code Run Faster?
  • Using a C Compiler
  • Reviewing the Julia Set Example
  • Cython
    • Compiling a Pure Python Version Using Cython
  • pyximport
    • Cython Annotations to Analyze a Block of Code
    • Adding Some Type Annotations
  • Cython and numpy
    • Parallelizing the Solution with OpenMP on One Machine
  • Numba
    • Numba to Compile NumPy for Pandas
  • PyPy
    • Garbage Collection Differences
    • Running PyPy and Installing Modules
  • A Summary of Speed Improvements
  • When to Use Each Technology
    • Other Upcoming Projects
  • Graphics Processing Units (GPUs)
    • Dynamic Graphs: PyTorch
    • Basic GPU Profiling
    • Performance Considerations of GPUs
    • When to Use GPUs
  • Foreign Function Interfaces
    • ctypes
    • cffi
    • f2py
    • CPython Module
  • Wrap-Up
• 8. Asynchronous I/O
  • Introduction to Asynchronous Programming
  • How Does async/await Work?
    • Serial Crawler
    • Gevent
    • tornado
    • aiohttp
  • Shared CPUI/O Workload
    • Serial
    • Batched Results
    • Full Async
  • Wrap-Up
• 9. The multiprocessing Module
  • An Overview of the multiprocessing Module
  • Estimating Pi Using the Monte Carlo Method
  • Estimating Pi Using Processes and Threads
    • Using Python Objects
    • Replacing multiprocessing with Joblib
      • Intelligent caching of function call results
    • Random Numbers in Parallel Systems
    • Using numpy
  • Finding Prime Numbers
    • Queues of Work
      • Asynchronously adding jobs to the Queue
  • Verifying Primes Using Interprocess Communication
    • Serial Solution
    • Naive Pool Solution
    • A Less Naive Pool Solution
    • Using Manager.Value as a Flag
    • Using Redis as a Flag
    • Using RawValue as a Flag
    • Using mmap as a Flag
    • Using mmap as a Flag Redux
  • Sharing numpy Data with multiprocessing
  • Synchronizing File and Variable Access
    • File Locking
    • Locking a Value
  • Wrap-Up
• 10. Clusters and Job Queues
  • Benefits of Clustering
  • Drawbacks of Clustering
    • $462 Million Wall Street Loss Through Poor Cluster Upgrade Strategy
    • Skypes 24-Hour Global Outage
  • Common Cluster Designs
  • How to Start a Clustered Solution
  • Ways to Avoid Pain When Using Clusters
  • Two Clustering Solutions
    • Using IPython Parallel to Support Research
    • Parallel Pandas with Dask
      • Parallelized apply with Swifter on Dask
      • Vaex for bigger-than-RAM DataFrames
  • NSQ for Robust Production Clustering
    • Queues
    • Pub/sub
    • Distributed Prime Calculation
  • Other Clustering Tools to Look At
  • Docker
    • Dockers Performance
    • Advantages of Docker
  • Wrap-Up
• 11. Using Less RAM
  • Objects for Primitives Are Expensive
    • The array Module Stores Many Primitive Objects Cheaply
    • Using Less RAM in NumPy with NumExpr
  • Understanding the RAM Used in a Collection
  • Bytes Versus Unicode
  • Efficiently Storing Lots of Text in RAM
    • Trying These Approaches on 11 Million Tokens
      • list
      • set
      • More efficient tree structures
      • Directed acyclic word graph
      • Marisa trie
      • Using tries (and DAWGs) in production systems
  • Modeling More Text with Scikit-Learns FeatureHasher
  • Introducing DictVectorizer and FeatureHasher
    • Comparing DictVectorizer and FeatureHasher on a Real Problem
  • SciPys Sparse Matrices
  • Tips for Using Less RAM
  • Probabilistic Data Structures
    • Very Approximate Counting with a 1-Byte Morris Counter
    • K-Minimum Values
    • Bloom Filters
    • LogLog Counter
    • Real-World Example
• 12. Lessons from the Field
  • Streamlining Feature Engineering Pipelines with Feature-engine
    • Feature Engineering for Machine Learning
    • The Hard Task of Deploying Feature Engineering Pipelines
    • Leveraging the Power of Open Source Python Libraries
    • Feature-engine Smooths Building and Deployment of Feature Engineering Pipelines
    • Helping with the Adoption of a New Open Source Package
    • Developing, Maintaining, and Encouraging Contribution to Open Source Libraries
  • Highly Performant Data Science Teams
    • How Long Will It Take?
    • Discovery and Planning
    • Managing Expectations and Delivery
  • Numba
    • A Simple Example
    • Best Practices and Recommendations
    • Getting Help
  • Optimizing Versus Thinking
  • Adaptive Labs Social Media Analytics (2014)
    • Python at Adaptive Lab
    • SoMAs Design
    • Our Development Methodology
    • Maintaining SoMA
    • Advice for Fellow Engineers
  • Making Deep Learning Fly with RadimRehurek.com (2014)
    • The Sweet Spot
    • Lessons in Optimizing
    • Conclusion
  • Large-Scale Productionized Machine Learning at Lyst.com (2014)
    • Cluster Design
    • Code Evolution in a Fast-Moving Start-Up
    • Building the Recommendation Engine
    • Reporting and Monitoring
    • Some Advice
  • Large-Scale Social Media Analysis at Smesh (2014)
    • Pythons Role at Smesh
    • The Platform
    • High Performance Real-Time String Matching
    • Reporting, Monitoring, Debugging, and Deployment
  • PyPy for Successful Web and Data Processing Systems (2014)
    • Prerequisites
    • The Database
    • The Web Application
    • OCR and Translation
    • Task Distribution and Workers
    • Conclusion
  • Task Queues at Lanyrd.com (2014)
    • Pythons Role at Lanyrd
    • Making the Task Queue Performant
    • Reporting, Monitoring, Debugging, and Deployment
    • Advice to a Fellow Developer
• Index

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