Google Brain's Jax and Flax

Google's AI division, Google Brain, has two main products for deep learning: TensorFlow and Jax. While TensorFlow is best known, Jax can be thought of as a higher-level language for specifying deep learning algorithms while automatically eliding code that doesn't need to run as part of the model.

Jax evolved from Autograd, and is a combination of Autograd and XLA. Autograd "can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization."

Flax is then built on top of Jax, and allows for easier customization of existing models.

What do you see as the future of domain specific languages for AI?

Transfer of pywer

Guido van Rossum is "resigning" from being the Python BDFL: "I'm basically giving myself a permanent vacation from being BDFL, and you all will be on
your own." Apparently running a language can be tiring... It will be interesting to see what happens next.

Coroutines with async and await syntax (Python 3.5)

With Python 3.5 released, the thing that drew my attention is the support for asynchronous programming through PEP 0492 -- Coroutines with async and await syntax, which added awaitable objects, coroutine functions, asynchronous iteration, and asynchronous context managers. I found the mailing list discussions (look both above and below) particularly helpful in understanding what exactly is going on.

Ancient use of generators

Guido van Rossum reminisces a bit about early discussions of generators in the Python community (read the other messages in the thread as well). I think we talked about the articles he mentions way back when. Earlier still, and beyond the discussion by Guido here, was Icon, a clever little language that I have a soft spot for. i don't think we ever fully assessed its influence on Python and other languages.

Python and Scientific Computing

This interesting blog post argues that in recent years Python has gained libraries making it the choice language for scientific computing (over MATLAB and R primarily).

I find the details discussed in the post interesting. Two small points that occur to me are that in several domains Mathematica is still the tool of choice. From what I could see nothing free, let alone open source, is even in the same ballpark in these cases. Second, I find it interesting that several of the people commenting mentioned IPython. It seems to be gaining ground as the primary environment many people use.

Project Sikuli

Picture or screenshot driven programming from the MIT.

From the Sikuli project page:

Sikuli is a visual technology to search and automate graphical user interfaces (GUI) using images (screenshots). The first release of Sikuli contains Sikuli Script, a visual scripting API for Jython, and Sikuli IDE, an integrated development environment for writing visual scripts with screenshots easily.

The Development of Sage

Sage is a project to create a viable free open source alternative to Magma, Maple, Mathematica and Matlab. The lead developer/manager William Stein has recently written Mathematical Software and Me: A Very Personal Recollection, a rather enjoyable story of his experience with mathematical software, especially Magma, and how Sage came to be.

One of the difficulties of writing broadly useful math software is the sheer size and scope of such a project. It is easily outside the abilities of even the most prodigious lone developer. So the focus of Sage, at least up until recently, has been on creating Python-based interfaces to existing mathematical software. For example, for symbolic calculation the Sage distribution includes Maxima (written in Common Lisp), a fork of Macsyma dating back to the early 1980s, and released as open-source software by the US Department of Energy approximately 10 years ago. In addition to Maxima, Sage includes the ability to call out to Magma, Mathematica, and Maple.

There are some interesting PLT-related snippets, for example, Magma's language is frequently criticized, although its algorithms are frequently praised. In conversations with others, OCaml and Haskell were brought up, but William Stein chose Python because he felt that it was more accessible. Also, Axiom, which includes the dependently-typed language Aldor, was rejected in favor of Maxima because Maxima was less esoteric and much more widely used.

Unladen Swallow: LLVM based Python compiler

The second release of Unladen Swallow was made available yesterday. The project plan describes the goals as follows:

We want to make Python faster, but we also want to make it easy for large, well-established applications to switch to Unladen Swallow.

  1. Produce a version of Python at least 5x faster than CPython.
  2. Python application performance should be stable.
  3. Maintain source-level compatibility with CPython applications.
  4. Maintain source-level compatibility with CPython extension modules.
  5. We do not want to maintain a Python implementation forever; we view our work as a branch, not a fork.

Of course, Google is known for writing a lot of their software in Python, and now they are trying to speed it up, just like the V8 project has done for JavaScript.

Skipping C - SPE and synthetic programming in Python

Expression and Loop Libraries for High-Performance Code Synthesis. Christopher Mueller and Andrew Lumsdaine. LCPC2006.

To simultaneously provide rapid application development and high performance, developers of scientific and multimedia applications often mix languages, using scripting languages to glue together high-performance components written in compiled languages. While this can be a useful development strategy, it distances application developers from the optimization process while adding complexity to the development tool chain. Recently, we introduced the Synthetic Programming Environment (SPE) for Python, a library for generating and executing machine instructions at run-time.

The authors didn't show much interest yet in supporting the most widespread ISAs, those for Intel processors. Instead they focus on PowerPC but also Cell. Have fun with Python and PS3 hacking.

Edit: You might also checkout the site of Christopher Mueller containing related material.

Google Python Sprint 2007

Joining this event (August 22-25) or following the action from afar may be a good way to keep up with Python 3000.

The first alpha release (3.0a1) should be just around the corner.

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