Selecting the right Python version is a primary decision point when setting up an environment. While Python 3 is the standard, specific minor versions (like 3.9, 3.10, 3.11, 3.12) can have implications for compatibility with the rapidly evolving LLM ecosystem libraries.
Why Python Version Matters
The Python version you choose impacts several aspects of your development workflow:
- Library Compatibility: This is often the most significant factor. Major LLM libraries like LangChain, LlamaIndex, Transformers (from Hugging Face), TensorFlow, and PyTorch specify the Python versions they support. Using an unsupported version can lead to installation failures or unexpected runtime errors. Often, libraries require a minimum version (e.g., Python 3.8+) and may not yet support the absolute latest release immediately.
- Language Features: Newer Python versions introduce useful language features. For instance, structural pattern matching (
match statements, introduced in 3.10) or improvements in type hinting can make your code cleaner and more maintainable. While not strictly necessary for LLM workflows, leveraging modern features can enhance development.
- Performance: Python's core developers continuously work on performance improvements. Newer versions often execute code faster than older ones, which can be relevant for computationally intensive tasks sometimes found in data preprocessing or model interaction loops.
- Security and Maintenance: Older Python versions eventually reach their End-of-Life (EOL) and stop receiving security updates. Sticking to actively maintained versions is important for security and long-term project stability.
Checking Your Version and Making a Choice
Before proceeding, you can check your currently installed default Python version by opening your terminal or command prompt and running:
python --version
# or sometimes
python3 --version
If you have multiple Python installations, ensure you're checking the one you intend to use for your LLM projects.
So, which version should you pick? Here's a practical approach:
- Consult Library Documentation: Check the documentation for the primary libraries you plan to use (e.g., LangChain, LlamaIndex, OpenAI's Python client). They usually state their supported Python versions clearly.
- Target Recent, Stable Releases: As a general rule, aim for one of the more recent, stable Python versions that are widely supported by the LLM ecosystem. As of late 2023 / early 2024, versions like Python 3.9, 3.10, and 3.11 are often safe bets, offering a good balance of modern features and broad library compatibility. Python 3.12 is becoming increasingly supported.
- Avoid EOL Versions: Do not use versions that are no longer receiving security updates (e.g., Python 3.7 and earlier).
- Be Cautious with Bleeding Edge: While tempting, immediately jumping to the absolute newest Python release (e.g., a recent .0 release or beta versions) might lead to compatibility issues as libraries take time to adapt. It's often wiser to wait a few months after a major Python release for the ecosystem to catch up.
The diagram below illustrates the typical status progression for Python versions.
Python version lifecycle stages. Aim for versions in the "Stable Release" or early "Security Fixes Only" phases for LLM development.
For this course, we recommend using Python 3.9 or newer, ensuring compatibility with the examples and libraries we'll be working with. If you already have a slightly different version installed (e.g., 3.10, 3.11), it will likely work perfectly fine. The most important step is consistency, which we'll address next when discussing virtual environments.
