1,085 items tagged “python”
The Python programming language.
2023
Llama from scratch (or how to implement a paper without crying) (via) Brian Kitano implemented the model described in the Llama paper against TinyShakespeare, from scratch, using Python and PyTorch. This write-up is fantastic—meticulous, detailed and deeply informative. It would take several hours to fully absorb and follow everything Brian does here but it would provide multiple valuable lessons in understanding how all of this stuff fits together.
Python cocktail: mix a context manager and an iterator in equal parts (via) Explanation of a neat trick used by the Tenacity Python library, which provides a mechanism for retrying a chunk of code automatically on errors up to three times using a mixture of an iterator and a context manager to work around Python’s lack of multi-line lambda functions.
Catching up on the weird world of LLMs
I gave a talk on Sunday at North Bay Python where I attempted to summarize the last few years of development in the space of LLMs—Large Language Models, the technology behind tools like ChatGPT, Google Bard and Llama 2.
[... 10,489 words]A Steering Council notice about PEP 703 (Making the Global Interpreter Lock Optional in CPython) (via) Huge news concerning the nogil research fork of Python: “It’s clear that the overall sentiment is positive, both for the general idea and for PEP 703 specifically. The Steering Council is also largely positive on both. We intend to accept PEP 703, although we’re still working on the acceptance details.”
textual-paint (via) Isaiah Odhner built a full working clone of MS Paint that runs entirely in the terminal, using Textual and Python. It’s worth trying this out to get a feel for quite how much you can get done with a CLI app these days—“pipx install textual-paint” worked for me.
Stamina: tutorial (via) Stamina is Hynek’s new Python library that implements an opinionated wrapper on top of Tenacity, providing a decorator for easily implementing exponential backoff retries. This tutorial includes a concise, clear explanation as to why this is such an important concept in building distributed systems.
Data analysis with SQLite and Python. I turned my 2hr45m workshop from PyCon into the latest official tutorial on the Datasette website. It includes an extensive handout which should be useful independently of the video itself.
Status of Python Versions (via) Very clear and useful page showing the exact status of different Python versions. 3.7 reaches end of life today (no more security updates), while 3.11 will continue to be supported until October 2027.
Building Search DSLs with Django (via) Neat tutorial by Dan Lamanna: how to build a GitHub-style search feature—supporting modifiers like “is:open author:danlamanna”—using PyParsing and the Django ORM.
Symbex: search Python code for functions and classes, then pipe them into a LLM
I just released a new Python CLI tool called Symbex. It’s a search tool, loosely inspired by ripgrep, which lets you search Python code for functions and classes by name or wildcard, then see just the source code of those matching entities.
[... 1,183 words]sqlean.py: Python’s sqlite3 with extensions. Anton Zhiyanov built a new Python package which bundles a fresh, compiled copy of SQLite with his SQLean family of C extensions built right in. Installing it gets you the latest SQLite—3.42.0—with nearly 200 additional functions, including things like define() and eval(), fileio_read() and fileio_write(), percentile_95() and uuid4() and many more. “import sqlean as sqlite3” works as a drop-in replacement for the module from the standard library.
simpleaichat (via) Max Woolf released his own Python package for building against the GPT-3.5 and GPT-4 APIs (and potentially other LLMs in the future).
It’s a very clean piece of API design with some useful additional features: there’s an AsyncAIChat subclass that works with Python asyncio, and the library includes a mechanism for registering custom functions that can then be called by the LLM as tools.
One trick I haven’t seen before: it uses a combination of max_tokens: 1 and a ChatGPT logit_bias to ensure that answers to one of its default prompts are restricted to just numerals between 0 and 9. This is described in the PROMPTS.md file.
pytest-icdiff (via) This is neat: “pip install pytest-icdiff” provides an instant usability upgrade to the output of failed tests in pytest, especially if the assertions involve comparing larger strings or nested JSON objects.
The Python Language Summit 2023: Making the Global Interpreter Lock Optional. Extremely informative update covering Sam Gross’s python-nogil proposal from this year’s language summit at PyCon.
Sam has been working hard on his fork for the past year, and now has it rebased for Python 3.12. If his PEP is accepted it could end up as an optional compile-time build in time for Python 3.13.
“The plan for nogil remains that it would be enabled via a compile-time flag, named --disable-gil. Third-party C extensions would need to provide separate wheels for GIL-disabled Python.”
Trogon (via) The latest project from the Textualize/Rich crew, Trogon provides a Python decorator—@tui—which, when applied to a Click CLI application, adds a new interactive TUI mode which introspects the available subcommands and their options and creates a full Text User Interface—with keyboard and mouse support—for assembling invocations of those various commands.
I just shipped sqlite-utils 3.32 with support for this—it uses an optional dependency, so you’ll need to run “sqlite-utils install trogon” and then “sqlite-utils tui” to try it out.
Writing Python like it’s Rust (via) Fascinating article by Jakub Beránek describing in detail patterns for using type annotations in Python inspired by working in Rust. I learned new tricks about both languages from reading this.
Real Multithreading is Coming to Python—Learn How You Can Use It Now (via) Martin Heinz provides a detailed tutorial on trying out the new Per-Interpreter GIL feature that’s landing in Python 3.12, which allows Python code to run concurrently in multiple threads by spawning separate sub-interpreters, each with their own dedicated GIL.
It’s not an easy feature to play with yet! First you need to compile Python yourself, and then use APIs that are generally only available to C code (but should hopefully become available to Python code itself in Python 3.13).
Martin’s workaround for this is ingenious: it turns out the Python test.support package provides utility functions to help write tests against interpreters, and Martin shows how to abuse this module to launch, run and cleanup interpreters using regular Python code.
He also demonstrates test.support.interpreters.create_channel(), which can be used to create channels with receiver and sender ends, somewhat similar to Go.
Implement DNS in a weekend (via) Fantastically clear and useful guide to implementing DNS lookups, from scratch, using Python’s struct, socket and dataclass modules—Julia Evans plans to follow this up with one for TLS which I am very much looking forward to.
Mojo may be the biggest programming advance in decades (via) Jeremy Howard makes a very convincing argument for why the new programming language Mojo is a big deal.
Mojo is a superset of Python designed by a team lead by Chris Lattner, who previously created LLVM, Clang and and Swift.
Existing Python code should work unmodified, but it also adds features that enable performant low-level programming—like “fn” for creating typed, compiled functions and “struct” for memory-optimized alternatives to classes.
It’s worth watching Jeremy’s video where he uses these features to get more than a 2000x speed up implementing matrix multiplication, while still keeping the code readable and easy to follow.
Mojo isn’t available yet outside of a playground preview environment, but it does look like an intriguing new project.
urllib3 v2.0.0 is now generally available. urllib3 is 12 years old now, and is a common low-level dependency for packages like requests and httpx. The biggest new feature in v2 is a higher-level API: resp = urllib3.request(“GET”, “https://example.com”)—a very welcome addition to the library.
Rye.
Armin Ronacher's take on a Python packaging tool. There are a lot of interesting ideas in this one - it's written in Rust, configured using pyproject.toml and has some very strong opinions, including completely hiding pip from view and insisting you use rye add package instead. Notably, it doesn't use the system Python at all: instead, it downloads a pre-compiled standalone Python from Gregory Szorc's python-build-standalone project - the same approach I used for the Datasette Desktop Electron app.
Armin warns that this is just an exploration, with no guarantees of future maintenance - and even has an issue open titled Should Rye exist?
Introducing PyPI Organizations. Launched at PyCon US today: Organizations allow packages on the Python Package Index to be owned by a group, not an individual user account. “We’re making organizations available to community projects for free, forever, and to corporate projects for a small fee.”—this is the first revenue generating PyPI feature.
codespaces-jupyter (via) This is really neat. Click “Use this template” -> “Open in a codespace” and you get a full in-browser VS Code interface where you can open existing notebook files (or create new ones) and start playing with them straight away.
Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha
Today I wanted to understand the performance difference between two Python implementations of a mechanism to detect changes to a SQLite database schema. I rendered the difference between the two as this chart:
[... 2,939 words]My strong hunch is that the GIL does not need removing, if a) subinterpreters have their own GILs and b) an efficient way is provided to pass (some) data between subinterpreters lock free and c) we find good patterns to make working with subinterpreters work.
Several libraries let you declare objects with type-hinted members and automatically derive validation rules and serialization/deserialization from the type hints – Pydantic is the most popular, but alternatives like msgspec are out there too. There’s also a whole new generation of web frameworks like FastAPI and Starlite which use type hints at runtime to do not just input validation and serialization/deserialization but also things like dependency injection.
Personally, I’ve seen more significant gains in productivity from those runtime usages of Python’s type hints than from any static ahead-of-time type checking, which mostly is only useful to me as documentation.
The different uses of Python type hints (via) Luke Plant describes five different categories for how Python optional types are being used today: IDE assistants, type checking, runtime behavior changes via introspection (e.g. Pydantic), code documentation, compiler instructions (ala mypyc)—and a bonus sixth, dependency injection.
textual-mandelbrot (via) I love this: run “pipx install textual-mandelbrot” and then “mandelexp” to get an interactive Mandelbrot fractal exploration interface right there in your terminal, built on top of Textual. The code for this is only 250 lines of Python and delightfully easy to follow.
A simple Python implementation of the ReAct pattern for LLMs. I implemented the ReAct pattern (for Reason+Act) described in this paper. It's a pattern where you implement additional actions that an LLM can take - searching Wikipedia or running calculations for example - and then teach it how to request that those actions are run, then feed their results back into the LLM.
djngo.com: Portable Django (via) “A 20mb executable zip file with Python 3.6 and Django 2.2. Works on Windows, Linux, MacOSX with x86_64 and aarch64 (yes, Apple M1 and Raspberry Pi).” The latest wizardry from the ecosystem surrounding the Cosmopolitan project, which provides a should-be-impossible mechanism for running the same executable on a bunch of different platforms. This utility by Ariel Núñez bundles Python and Django and SQLite, such that a Django application can become a portable executable ready to run on multiple platforms. It’s currently limited to Python 3.6 and Django 2.2 since those are the versions that run under Cosmopolitan, but I expect we’ll see more recent versions of those dependencies in the future.