High Performance Python. 2nd Edition Micha Gorelick, Ian Ozsvald

High Performance Python. 2nd Edition Micha Gorelick, Ian Ozsvald - okladka książki

Autorzy:: Micha Gorelick, Ian Ozsvald
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 468
Dostępne formaty:: ePub

Mobi

Ebook 203,15 zł najniższa cena z 30 dni

~~249,00 zł~~ (-15%)
211,65 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

203,15 zł najniższa cena z 30 dni

Poleć tę książkę znajomemu Poleć tę książkę znajomemu!!

Przenieś na półkę

Do przechowalni

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation.

How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.

Get a better grasp of NumPy, Cython, and profilers
Learn how Python abstracts the underlying computer architecture
Use profiling to find bottlenecks in CPU time and memory usage
Write efficient programs by choosing appropriate data structures
Speed up matrix and vector computations
Use tools to compile Python down to machine code
Manage multiple I/O and computational operations concurrently
Convert multiprocessing code to run on local or remote clusters
Deploy code faster using tools like Docker

Wybrane bestsellery

O autorach książki

Micha Gorelick to laureat nagrody Nobla z roku 2046 za swój wkład w podróże w czasie. Cofnął się w czasie do pierwszej dekady XXI wieku, aby studiować astrofizykę, przetwarzać dane w serwisie bitly i zostać współzałożycielem firmy Fast Forward Labs jako obywatel Szalony Naukowiec, zajmując się różnymi zagadnieniami, począwszy od uczenia maszynowego, a skończywszy na wydajnych algorytmach strumieni.

Ian Ozsvald to analityk danych i nauczyciel w firmie ModelInsight.io z ponad 10-letnim doświadczeniem z zakresu języka Python. Ian prowadził wykłady poświęcone kodowi Python o dużej wydajności na konferencjach PyCon i PyData, a także od wielu lat oferuje w Wielkiej Brytanii usługi konsultingowe związane z analizą danych i obliczeniami o dużej wydajności.

Ebooka "High Performance Python. 2nd Edition" przeczytasz na:

czytnikach Inkbook, Kindle, Pocketbook, Onyx Boox i innych
systemach Windows, MacOS i innych

systemach Windows, Android, iOS, HarmonyOS
na dowolnych urządzeniach i aplikacjach obsługujących formaty: PDF, EPub, Mobi

Masz pytania? Zajrzyj do zakładki Pomoc »

Audiobooka "High Performance Python. 2nd Edition" posłuchasz:

w aplikacji Ebookpoint na Android, iOS, HarmonyOs
na systemach Windows, MacOS i innych

na dowolonych urządzeniach
i aplikacjach obsługujących format MP3
(pliki spakowane w ZIP)

Masz pytania? Zajrzyj do zakładki Pomoc »

Kurs Video "High Performance Python. 2nd Edition" zobaczysz:

Oceny i opinie klientów: High Performance Python. 2nd Edition Micha Gorelick, Ian Ozsvald (0)

Szczegóły książki

ISBN Ebooka:: 978-14-920-5497-9, 9781492054979
Data wydania ebooka :: 2020-04-30 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: 1
Rozmiar pliku ePub:: 6.5MB
Rozmiar pliku Mobi:: 18.3MB

Zgłoś erratę

Kategorie

Kliknij, aby zgłosić błędnie przypisaną kategorię »

Informatyka » Programowanie » Python

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