لایب سان

• Title: High Performance Python, 2nd Edition
• Author(s): Micha Gorelick, Ian Ozsvald
• Release date: April 2020
• Publisher(s): O'Reilly Media, Inc.

Foreword Preface Who This Book Is For Who This Book Is Not For What You’ll 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 O’Reilly 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 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 Timing—print 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 Aren’t Python Lists Good Enough? Problems with Allocating Too Much Memory Fragmentation Understanding perf Making Decisions with perf’s 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 Pandas’s Internal Model Applying a Function to Many Rows of Data Building DataFrames and Series from Partial Results Rather than Concatenating There’s 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 CPU–I/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 Random Numbers in Parallel Systems Using numpy Finding Prime Numbers Queues of Work 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 Skype’s 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 NSQ for Robust Production Clustering Queues Pub/sub Distributed Prime Calculation Other Clustering Tools to Look At Docker Docker’s 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 Modeling More Text with Scikit-Learn’s FeatureHasher Introducing DictVectorizer and FeatureHasher Comparing DictVectorizer and FeatureHasher on a Real Problem SciPy’s 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 Lab’s Social Media Analytics (2014) Python at Adaptive Lab SoMA’s 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) Python’s 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) Python’s Role at Lanyrd Making the Task Queue Performant Reporting, Monitoring, Debugging, and Deployment Advice to a Fellow Developer

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