This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism,as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing.
By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems.
This Learning Path includes content from the following Packt products:
Python High Performance - Second Edition by Gabriele Lanaro
Mastering Concurrency in Python by Quan Nguyen
Mastering Python Design Patterns by Sakis Kasampalis
Mastering Python by Dr. Gabriele Lanaro, Quan Nguyen, and Sakis Kasampalis
- Use NumPy and pandas to import and manipulate datasets
- Achieve native performance with Cython and Numba
- Write asynchronous code using asyncio and RxPy
- Design highly scalable programs with application scaffolding
- Explore abstract methods to maintain data consistency
- Clone objects using the prototype pattern
- Use the adapter pattern to make incompatible interfaces compatible
- Employ the strategy pattern to dynamically choose an algorithm
For optimal student experience, the following hardware requirements is recommended:
- Processor: Intel Core i5 or equivalent
- Memory: 4 GB RAM or higher
- Storage: 40 GB available space
- Python version 3.5 or higher
- Ubuntu version 16.04 (Note: Majority of the examples can also be run on the Windows and Mac OS X operating systems).
- SQLite 3.22.0 or higher
- RabbitMQ3.7.7
- Anaconda Distribution
- pip
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