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Lex Fridman Podcast
#341 – Guido van Rossum: Python and the Future of Programming
Nov 26, 2022
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03:22:36
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Quick takeaways
- Python 3.11 introduces significant performance improvements, enhancing the overall execution speed of Python programs.
- The balance between simplicity and performance is an ongoing consideration in Python's development.
- Python optimization efforts have primarily focused on improving the efficiency of the interpreter.
- Python's continuous efforts to improve performance demonstrate the language's commitment to delivering high-quality and efficient capabilities.
- Python's approach to parallelism and concurrency involves the use of locks, semaphores, and the asyncio module.
- The future possibilities of introducing multiple sub-interpreters and transitioning to Python 4.0 are discussed in the podcast.
Deep dives
Python 3.11: Faster Performance
Python 3.11 is set to be released soon and brings with it significant performance improvements. The focus on improving the performance of the interpreter itself has led to faster execution times for Python code. Efforts were made to optimize the interpreter, which is responsible for compiling Python code into bytecode that can be executed by the Python interpreter. By making the interpreter more efficient, Python programs can be executed more quickly, improving overall performance. This improvement demonstrates the ongoing efforts to enhance Python's efficiency, ensuring that it remains a reliable and high-performing programming language.
Simplicity versus Performance in Programming
The balance between simplicity and performance is an ongoing consideration in programming. While simple code is often easier to understand and reason about, it may not always be the most performant. The design choices made in the early stages of Python's development aimed for simplicity, allowing for a quick and useful programming language. However, as Python grew in popularity and the demand for performance increased, optimizations were necessary. Different algorithms or coding techniques were explored to increase the efficiency of Python programs. Sometimes, a more complex solution was required to achieve the desired performance improvements. The challenge lies in finding the right trade-off between simplicity and performance, ensuring that code remains maintainable and readable while also meeting performance requirements.
Algorithmic Optimization in Python
Python optimization efforts have focused on areas where there were low-hanging fruits, such as the interpreter itself. Python's interpreter combines interpretation and compilation. While it compiles Python code into bytecode, it's still responsible for interpreting and executing that bytecode. By optimizing the interpreter, the execution of Python programs becomes more efficient, leading to better performance. This optimization work primarily targeted the interpreter, making it more efficient and improving the overall execution speed of Python programs. It's important to note that Python did not adopt a just-in-time (JIT) compiler approach, but rather improved the efficiency of the existing interpreter.
The Constant Pursuit of Python's Performance
Python's continuous efforts to improve performance demonstrate the language's commitment to providing a high-quality and efficient platform for developers. The optimizations in Python 3.11 showcase the dedication to enhancing Python's speed and execution capabilities. By refining the interpreter and considering algorithmic optimizations, Python strives to deliver faster performance for a wide range of applications. These ongoing efforts ensure that Python remains a competitive and reliable language for various programming needs.
Python's approach to parallelism and concurrency
Python's approach to parallelism and concurrency involves the use of locks, semaphores, and asynchronous IO. The parallelism in Python refers to the use of multiple CPUs or cores to execute tasks simultaneously. Concurrency, on the other hand, involves the illusion of simultaneous execution by switching between tasks. Python's approach to parallelism and concurrency can be seen in the asyncio module, which provides mechanisms for handling asynchronous IO and networking tasks. The asyncio module allows for task-based programming, where each task has its own logic and can be executed concurrently. This approach aims to improve efficiency and handle multiple IO tasks effectively.
The challenge of implementing synchronization primitives
Implementing synchronization primitives, such as locks and semaphores, can be challenging due to the complexity of managing multiple tasks or threads. It is difficult for programmers to keep track of multiple variables and ensure the correct ordering of operations. This can lead to bugs and errors in the code. The complexity is further increased when dealing with asynchronous IO, where different tasks may require different types of synchronization. Python's standard library provides mechanisms for implementing synchronization primitives, but careful design and attention to detail are required to ensure their proper use and effectiveness.
The evolution of asyncio and the challenge of the Global Interpreter Lock (GIL)
Python's asyncio module has evolved since its introduction in Python 3.4. The module was designed to address the challenges of asynchronous IO and concurrency, providing a task-based approach for handling parallel IO tasks. However, the presence of the Global Interpreter Lock (GIL), which allows only one thread to execute Python bytecode at a time, presents a limitation for achieving true parallelism in multi-threaded applications. The GIL ensures thread safety but restricts the effective utilization of multiple CPU cores. This limitation has led to discussions and debates on the best approaches for achieving parallelism and concurrency in Python, with ongoing efforts to find solutions and optimizations.
Python's journey towards efficient parallelism and concurrency
Python has been on a journey towards efficient parallelism and concurrency, with the introduction of libraries like asyncio and ongoing discussions surrounding the GIL. The asyncio module provides a task-based approach for handling asynchronous IO tasks and has gained popularity in the Python web development community. However, achieving true parallelism in Python remains a challenge due to the GIL. Efforts are being made to explore potential solutions and optimizations, with the goal of improving the efficiency of parallel and concurrent programming in Python. This journey highlights the evolving nature of Python's approach to parallelism and the ongoing advancements in the field.
Future of Python: Multiple Sub-interpreters and No-GIL Interpreter
The podcast discusses the future possibilities for Python, including the idea of introducing multiple sub-interpreters. This would involve running independent Python programs that communicate with each other, but may require more time for inter-program communication. Another potential future is the development of a 'No-GIL' interpreter, currently being explored by a Facebook employee. This interpreter removes the Global Interpreter Lock (GIL) and optimizes performance for single-threaded and multi-threaded cases. However, there are concerns about maintaining and managing the additional complexity and overhead. Despite differing opinions, the sub-interpreters appear to be a safer bet for Python 3.12.
Transition to Python 4.0: Balancing Compatibility and Innovation
The possibility of a Python 4.0 release is discussed, accompanied by the lessons learned from the Python 3 transition. The shift to Python 3 caused significant pain for users, leading to the decision that Python 4.0 would require a different approach to managing the transition. One potential process mentioned involves releasing versions that gradually introduce alternative features and support for 'No-GIL Python'. Extension developers would have the opportunity to experiment with the new API and adapt their modules accordingly. Python 4.0 would likely ensure compatibility with Python 3 and mainly focus on changes in extension modules. The transition process would be carefully planned and accompanied by a significant heads-up period for third-party extension developers.
Python's Dominance in Data Science and Machine Learning
The podcast explores why Python has become the primary language for the machine learning, data science, and AI communities. Python's dominance can be attributed to factors such as its compatibility and the availability of powerful libraries and frameworks like PyTorch, TensorFlow, scikit-learn, Pandas, and Matplotlib. The open-source nature of Python and its focus on developer culture have also played a pivotal role. Python's position as the language of choice for these domains is a result of its accessibility, extensive package ecosystem, compatibility with existing tools, and its ability to facilitate fast prototyping and experimentation.
1.
Introduction
2min
2.
I Love Taking Naps. I'm Going to Pass Out for 20, 25, 30 Minutes
3min
3.
The All-in-One Daily Drink to Support Better Health and Peak Performance
2min
4.
CPython Is the Last Python Implementation Standing
2min
5.
Is There a Difference Between L and R in Chinese?
2min
6.
I Love You to a Person That Matters a Lot
2min
7.
Programming Languages - The Difference Between a Cookbook Recipe and a Computer Program
3min
8.
The Indentation Style of Python
3min
9.
Python Code - The Space Between Space and Indentation
2min
10.
Python Indentation Is a Recommendation
2min
11.
Is Space of Indentation Better Than Braces in Python?
3min
12.
Indentation in Python
3min
13.
The Dollar Sign Before Variable
5min
14.
Resilience in Computer Programming
2min
15.
The Number of Bugs Per Line of Code
2min
16.
Stack Overflow Is Currently Offline for Maintenance?
2min
17.
Can You Believe That Number of Bugs?
3min
18.
Using a Kinesis Keyboard?
2min
19.
Is the Kinesis Keyboard a Comfort Space?
2min
20.
JavaScript Frameworks and the Future of the Web?
3min
21.
Learning Programming When You're Young, Right?
2min
22.
Is C++ the Right Tool for Machine Learning?
3min
23.
Do You Switch Back-End to Python?
4min
24.
Is There a Better Community in Machine Learning?
2min
25.
C++ and Python V3 - What Are Some Ideas That Make It Faster?
4min
26.
Is It Divisible by Four?
3min
27.
The Simplified Algorithms Are the Most Efficient
2min
28.
Python Interpreter - What's the Low Hanging Fruit?
2min
29.
Adaptive Specifying Interpreter in Python
5min
30.
How to Add Two Integers?
2min
31.
Python Optimization
5min
32.
Adding Two Integers Not Two Generic Types
3min
33.
Python 3.5 - Type Hints
3min
34.
Is There a Way to Enforce Type Hints in Python?
2min
35.
The Future of Static Typing in Python
2min
36.
Is There a Split in the Community Over Types?
2min
37.
JavaScript - TypeScript or JavaScript?
2min
38.
TypeScript or JavaScript?
1min
39.
JavaScript vs Python
2min
40.
My Pi Is a Static Type Checker for Python
3min
41.
How to Write a Static Type Checker
4min
42.
Is There a Future for My Pie?
2min
43.
Python and It's Annotation Syntax
2min
44.
Python Programming - Type Hindering
2min
45.
Pycharm vs Code Vim or Emax?
3min
46.
How to Get a Custom Just Like With Touch Typing
2min
47.
Vs Code Is the Spiritual Successor of Emacs
4min
48.
Pi Charm
2min
49.
Parallelism and the Fishing Rods
3min
50.
The Difference Between Parallelism and Concurrency in the Computer World
4min
51.
Using a Lock to Get Multiple Ovens in Use
2min
52.
Python 3.4 Synchronous IO
4min
53.
Python Web Clients and Parallel IO
5min
54.
Python and Threading
6min
55.
Python Core Development - Is There a Future to Remove the Global Interpreter Lock?
4min
56.
Python 4.0 - Is There a 4.0?
2min
57.
Python 2 7 Officially End of Life Did You Shed a Single Tear?
2min
58.
Python 4.0
3min
59.
No-Gill Python 4.0
2min
60.
Pipi Packages - Do You Have Any?
3min
61.
Python and Machine Learning
4min
62.
Using Python to Solve Complex Problems
3min
63.
Python Is a Great Example of Open Source Programming Languages
3min
64.
Python Developers - What Are Your Thoughts and Lessons Learned From That?
4min
65.
Dropbox vs Microsoft - Differences in Culture
3min
66.
Microsoft and Google
2min
67.
I've Never Learned How to Use Excel
2min
68.
What's the Role of Leadership in Such a Pivotal?
2min
69.
Entrepreneurship Is Not a Nine to Five Job
3min
70.
How to Learn Python in 10 Days or Less
3min
71.
Python Code Generation - The 10 Years Is About the Experience
2min
72.
Co-Pilot Is a Great Assistant
2min
73.
The Future of Python
2min
74.
Machine Learning and It's a Giant Beautiful Higher
2min
75.
Eventually Machine Learning Will Build Higher and Higher Layers
2min
76.
The Importance of Dna and Proteins
2min
77.
The Evolution of the Human Condition
2min
Guido van Rossum is the creator of Python programming language. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
Guido’s Twitter: https://twitter.com/gvanrossum
Guido’s Website: https://gvanrossum.github.io/
Python’s Website: https://python.org
PODCAST INFO:
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SUPPORT & CONNECT:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(07:26) – CPython
(12:38) – Code readability
(17:00) – Indentation
(33:36) – Bugs
(45:04) – Programming fads
(1:00:15) – Speed of Python 3.11
(1:25:09) – Type hinting
(1:30:27) – mypy
(1:35:43) – TypeScript vs JavaScript
(1:51:42) – Best IDE for Python
(2:01:43) – Parallelism
(2:19:36) – Global Interpreter Lock (GIL)
(2:29:14) – Python 4.0
(2:41:31) – Machine learning
(2:51:13) – Benevolent Dictator for Life (BDFL)
(3:02:49) – Advice for beginners
(3:09:21) – GitHub Copilot
(3:12:47) – Future of Python
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