Python high performance build robust application by implementing concurrent and distributed processing techniques
Learn how to use Python to create efficient applications About This Book Identify the bottlenecks in your applications and solve them using the best profiling techniques Write efficient numerical code in NumPy, Cython, and Pandas Adapt your programs to run on multiple processors and machines with pa...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt
2017.
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630203906719 |
Tabla de Contenidos:
- 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.
