最新章節
- Summary
- Continuous integration
- Creating docker images
- Virtualization and Containers
- Using conda environments
- Isolation virtual environments and containers
品牌：中圖公司
上架時間：2021-07-09 18:34:21
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Summary 更新時間：2021-07-09 21:02:19
- Continuous integration
- Creating docker images
- Virtualization and Containers
- Using conda environments
- Isolation virtual environments and containers
- Organizing your source code
- Big data
- Numerical code
- Generic applications
- Choosing a suitable strategy
- Designing for High Performance
- Summary
- Scientific computing with mpi4py
- Spark DataFrame
- Resilient Distributed Datasets
- Spark architecture
- Setting up Spark and PySpark
- Using PySpark
- Manual cluster setup
- Dask distributed
- Dask Bag and DataFrame
- Dask arrays
- Directed Acyclic Graphs
- Dask
- An introduction to MapReduce
- Introduction to distributed computing
- Distributed Processing
- Summary
- Running code on a GPU
- Tensorflow
- Profiling Theano
- Getting started with Theano
- Automatic parallelism
- Parallel Cython with OpenMP
- Synchronization and locks
- Monte Carlo approximation of pi
- The Executor interface
- The Process and Pool classes
- Using multiple processes
- Graphic processing units
- Introduction to parallel programming
- Parallel Processing
- Summary
- Building a CPU monitor
- Hot and cold observables
- Useful operators
- Observables
- Reactive programming
- Converting blocking code into non-blocking code
- Coroutines
- The asyncio framework
- Event loops
- Futures
- Callbacks
- Concurrency
- Waiting for I/O
- Asynchronous programming
- Implementing Concurrency
- Summary
- Other interesting projects
- Running a particle simulator in PyPy
- Setting up PyPy
- The PyPy project
- Limitations in Numba
- JIT classes
- Generalized universal functions
- Universal functions with Numba
- Numba and NumPy
- Object mode versus native mode
- Type specializations
- First steps with Numba
- Numba
- Exploring Compilers
- Summary
- Using Cython with Jupyter
- Profiling Cython
- Particle simulator in Cython
- Typed memoryviews
- NumPy arrays
- C arrays and pointers
- Working with arrays
- Sharing declarations
- Classes
- Functions
- Variables
- Adding static types
- Compiling Cython extensions
- C Performance with Cython
- Summary
- Joining
- Grouping aggregations and transforms
- Mapping
- Database-style operations with Pandas
- Indexing Series and DataFrame objects
- Pandas fundamentals
- Pandas
- Reaching optimal performance with numexpr
- Rewriting the particle simulator in NumPy
- Calculating the norm
- Mathematical operations
- Broadcasting
- Accessing arrays
- Creating arrays
- Getting started with NumPy
- Fast Array Operations with NumPy and Pandas
- Summary
- Comprehensions and generators
- Joblib
- Caching and memoization
- Tries
- Heaps
- Sets
- Building an in-memory search index using a hash map
- Dictionaries
- Lists and deques
- Useful algorithms and data structures
- Pure Python Optimizations
- Summary
- Profiling memory usage with memory_profiler
- The dis module
- Optimizing our code
- Profile line by line with line_profiler
- Finding bottlenecks with cProfile
- Better tests and benchmarks with pytest-benchmark
- Timing your benchmark
- Writing tests and benchmarks
- Designing your application
- Benchmarking and Profiling
- Questions
- Piracy
- Errata
- Downloading the color images of this book
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- www.PacktPub.com
- About the Reviewer
- About the Author
- Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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 更新時間：2021-07-09 21:02:19
