Executive Development Programme in Advanced Python Internals for Developers

This course is designed for experienced Python developers seeking to deepen their understanding of Python internals. Participants will gain comprehensive knowledge of Python's C API, memory management, and the Python interpreter. The curriculum includes advanced topics such as writing efficient C extensions, understanding the Python garbage collector, and optimizing performance bottlenecks.

By the end, attendees will be equipped to develop high-performance C extensions, debug complex issues, and contribute to open-source Python projects with confidence.

1. 1. Introduction to Python Internals: Learners will explore the structure of Python code and its execution flow, understanding how Python interprets and compiles code. They will gain foundational knowledge about Python’s data model and the role of the Python runtime environment.
2. 2. Python Compiler and Bytecode: This module focuses on Python’s bytecode and the compilation process. Learners will study the CPython compiler and how Python bytecode is generated and executed. They will create and interpret bytecode to understand Python’s internal mechanisms.
3. 3. Python Memory Management: Learners will delve into Python’s memory management system, including object lifecycle, garbage collection, and reference counting. They will implement basic memory management strategies to optimize their Python applications.
4. 4. Python C API: This module introduces learners to the Python C API, enabling them to write Python extensions and integrate C code with Python. They will develop simple C extensions and understand how to interface between Python and C.
5. 5. Python Internals: The Python Virtual Machine (PVM): Learners will study the Python Virtual Machine (PVM), understanding how it manages threads, executes bytecode, and handles exceptions. They will create and run custom PVM bytecode to customize Python’s internal behavior.
6. 6. Advanced Python Performance Optimization: This module covers techniques for optimizing Python code performance, including profiling and benchmarking tools. Learners will apply these techniques to real-world Python applications, enhancing their ability to write efficient and performant code.
7. 7. Extending Python with JIT Compilation: Learners will explore Just-In-Time (JIT) compilation in Python, focusing on tools like Numba and PyPy. They will implement JIT compilation for Python functions and understand its impact on performance.
8. 8. Python Debugging and Tracing: This module teaches learners how to debug and trace Python code using tools like pdb, cProfile, and other advanced debugging techniques. They will develop skills to identify and resolve complex bugs in their Python applications.
9. 9. Python Internals: The Python Standard Library: Learners will investigate the Python Standard Library, understanding its architecture and the role of modules like `sys`, `os`, and `collections`. They will extend and customize these modules for their own projects.
10. 10. Building a Custom Python Interpreter: In this final module, learners will design and build a simple Python interpreter from scratch. They will apply their knowledge of Python internals to create a working interpreter, gaining a deep understanding of Python’s core concepts and structures.