Python High Performance

✍ Scribed by Gabriele Lanaro

• Publisher: Packt Publishing
• Year: 2017
• Tongue: English
• Leaves: 264
• Edition: 2
• Category: Library

✦ Synopsis

Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Benchmarking and Profiling -- Designing your application -- Writing tests and benchmarks -- Timing your benchmark -- Better tests and benchmarks with pytest-benchmark -- Finding bottlenecks with cProfile -- Profile line by line with line_profiler -- Optimizing our code -- The dis module -- Profiling memory usage with memory_profiler -- Summary -- Chapter 2: Pure Python Optimizations -- Useful algorithms and data structures -- Lists and deques -- Dictionaries -- Building an in-memory search index using a hash map -- Sets -- Heaps -- Tries -- Caching and memoization -- Joblib -- Comprehensions and generators -- Summary -- Chapter 3: Fast Array Operations with NumPy and Pandas -- Getting started with NumPy -- Creating arrays -- Accessing arrays -- Broadcasting -- Mathematical operations -- Calculating the norm -- Rewriting the particle simulator in NumPy -- Reaching optimal performance with numexpr -- Pandas -- Pandas fundamentals -- Indexing Series and DataFrame objects -- Database-style operations with Pandas -- Mapping -- Grouping, aggregations, and transforms -- Joining -- Summary -- Chapter 4: C Performance with Cython -- Compiling Cython extensions -- Adding static types -- Variables -- Functions -- Classes -- Sharing declarations -- Working with arrays -- C arrays and pointers -- NumPy arrays -- Typed memoryviews -- Particle simulator in Cython -- Profiling Cython -- Using Cython with Jupyter -- Summary -- Chapter 5: Exploring Compilers -- Numba -- First steps with Numba -- Type specializations -- Object mode versus native mode -- Numba and NumPy -- Universal functions with Numba -- Generalized universal functions -- JIT classes -- Limitations in Numba -- The PyPy project

Setting up PyPy -- Running a particle simulator in PyPy -- Other interesting projects -- Summary -- Chapter 6: Implementing Concurrency -- Asynchronous programming -- Waiting for I/O -- Concurrency -- Callbacks -- Futures -- Event loops -- The asyncio framework -- Coroutines -- Converting blocking code into non-blocking code -- Reactive programming -- Observables -- Useful operators -- Hot and cold observables -- Building a CPU monitor -- Summary -- Chapter 7: Parallel Processing -- Introduction to parallel programming -- Graphic processing units -- Using multiple processes -- The Process and Pool classes -- The Executor interface -- Monte Carlo approximation of pi -- Synchronization and locks -- Parallel Cython with OpenMP -- Automatic parallelism -- Getting started with Theano -- Profiling Theano -- Tensorflow -- Running code on a GPU -- Summary -- Chapter 8: Distributed Processing -- Introduction to distributed computing -- An introduction to MapReduce -- Dask -- Directed Acyclic Graphs -- Dask arrays -- Dask Bag and DataFrame -- Dask distributed -- Manual cluster setup -- Using PySpark -- Setting up Spark and PySpark -- Spark architecture -- Resilient Distributed Datasets -- Spark DataFrame -- Scientific computing with mpi4py -- Summary -- Chapter 9: Designing for High Performance -- Choosing a suitable strategy -- Generic applications -- Numerical code -- Big data -- Organizing your source code -- Isolation, virtual environments, and containers -- Using conda environments -- Virtualization and Containers -- Creating docker images -- Continuous integration -- Summary -- Index  Read more...

Abstract: Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Benchmarking and Profiling -- Designing your application -- Writing tests and benchmarks -- Timing your benchmark -- Better tests and benchmarks with pytest-benchmark -- Finding bottlenecks with cProfile -- Profile line by line with line_profiler -- Optimizing our code -- The dis module -- Profiling memory usage with memory_profiler -- Summary -- Chapter 2: Pure Python Optimizations -- Useful algorithms and data structures -- Lists and deques -- Dictionaries -- Building an in-memory search index using a hash map -- Sets -- Heaps -- Tries -- Caching and memoization -- Joblib -- Comprehensions and generators -- Summary -- Chapter 3: Fast Array Operations with NumPy and Pandas -- Getting started with NumPy -- Creating arrays -- Accessing arrays -- Broadcasting -- Mathematical operations -- Calculating the norm -- Rewriting the particle simulator in NumPy -- Reaching optimal performance with numexpr -- Pandas -- Pandas fundamentals -- Indexing Series and DataFrame objects -- Database-style operations with Pandas -- Mapping -- Grouping, aggregations, and transforms -- Joining -- Summary -- Chapter 4: C Performance with Cython -- Compiling Cython extensions -- Adding static types -- Variables -- Functions -- Classes -- Sharing declarations -- Working with arrays -- C arrays and pointers -- NumPy arrays -- Typed memoryviews -- Particle simulator in Cython -- Profiling Cython -- Using Cython with Jupyter -- Summary -- Chapter 5: Exploring Compilers -- Numba -- First steps with Numba -- Type specializations -- Object mode versus native mode -- Numba and NumPy -- Universal functions with Numba -- Generalized universal functions -- JIT classes -- Limitations in Numba -- The PyPy project

Setting up PyPy -- Running a particle simulator in PyPy -- Other interesting projects -- Summary -- Chapter 6: Implementing Concurrency -- Asynchronous programming -- Waiting for I/O -- Concurrency -- Callbacks -- Futures -- Event loops -- The asyncio framework -- Coroutines -- Converting blocking code into non-blocking code -- Reactive programming -- Observables -- Useful operators -- Hot and cold observables -- Building a CPU monitor -- Summary -- Chapter 7: Parallel Processing -- Introduction to parallel programming -- Graphic processing units -- Using multiple processes -- The Process and Pool classes -- The Executor interface -- Monte Carlo approximation of pi -- Synchronization and locks -- Parallel Cython with OpenMP -- Automatic parallelism -- Getting started with Theano -- Profiling Theano -- Tensorflow -- Running code on a GPU -- Summary -- Chapter 8: Distributed Processing -- Introduction to distributed computing -- An introduction to MapReduce -- Dask -- Directed Acyclic Graphs -- Dask arrays -- Dask Bag and DataFrame -- Dask distributed -- Manual cluster setup -- Using PySpark -- Setting up Spark and PySpark -- Spark architecture -- Resilient Distributed Datasets -- Spark DataFrame -- Scientific computing with mpi4py -- Summary -- Chapter 9: Designing for High Performance -- Choosing a suitable strategy -- Generic applications -- Numerical code -- Big data -- Organizing your source code -- Isolation, virtual environments, and containers -- Using conda environments -- Virtualization and Containers -- Creating docker images -- Continuous integration -- Summary -- Index