16 Jun 2024

Python Inception

At most companies I have worked for, there was some internal Python code base that relied on an old version of Python. But especially for data science, I'd often want to execute that code from an up-to-date Jupyter Notebook, to do some analysis on results.

When this happened last time, I decided to do something about it. Here's a Jupyter cell magic that executes the cell's code in a different Python, pipes out all of STDOUT and STDERR, and imports any newly created variables into the host Python. Use it like this:

%%py_magic /old/version/of/python
import this
truth = 42

When this cell executes, you will see the Zen of Python in your output, just as if you had import this in the host Python, and the variable truth will now be 42 in the host Python.

To get this magic, execute the following code in a preceding cell:

import subprocess
import sys
import pickle
import textwrap
from IPython.core.magic import needs_local_scope, register_cell_magic
 
@register_cell_magic
@needs_local_scope
def py_magic(line, cell, local_ns=None):
    proc = subprocess.Popen([line or 'python'],
                            stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
                            encoding='UTF8')
    # send a preamble to the client python, and remember all pre-existing local variable names:
    proc.stdin.write(textwrap.dedent("""
        import pickle as _pickle
        import types as _types
        _names_before = [k for k, v in locals().items()] + ['_f', '_names_before']
        try:
    """))
    # send the cell's contents, indented to run in the try:
    for line in cell.splitlines():
        proc.stdin.write("    " + line + "\n")  # indent!
    # send a postamble that pickles all new variables or thrown exceptions:
    proc.stdin.write(textwrap.dedent("""
        # save results to result.pickle
        except Exception as exc:
            with open('result.pickle', 'wb') as _f:
                _pickle.dump({'type':'error', 'value': exc}, _f)
        else:
            with open('result.pickle', 'wb') as _f:
                _values = {k:v for k, v in locals().items()
                               if not isinstance(v, _types.ModuleType) 
                                  and not k in _names_before}
                _safe_values = {}  # skip any unpickleable variables
                for k, v in _values.items():
                    try:
                        _pickle.dumps(v)
                    except Exception as _exc:
                        print(f'skipping dumping {k} because {_exc}')
                    else:
                        _safe_values[k] = v
                _pickle.dump({'type':'result', 'value': _safe_values}, _f)
        finally:
            quit()
    """))
    # print any captured stdout or stderr:
    stdout, stderr = proc.communicate()
    if stdout:
        print(stdout, file=sys.stdout)
    if stderr:
        print(stderr, file=sys.stderr)

    # load new local variables or throw error:
    try:
        with open('result.pickle', 'rb') as f:
            result = pickle.load(f)
        if result['type'] == 'error':
            raise result['value']
        elif result['type'] == 'result':
            for key, value in result['value'].items():
                try:
                    local_ns[key] = value
                except Exception as exc:
                    print(f"skipping loading {key} because {exc}")
    finally:
        pathlib.Path('result.pickle').unlink()  # remove temporary file
  
del py_magic  # otherwise the function overwrites the magic

I love how this sort of trickery is relatively easy in Python. Also, this is the first time I've used a try with except, else, and finally.

Tags: computers python

09 May 2024

AI Predictions

Meta just invested 30 Billion Dollars into AI accelerators¹. That's roughly equivalent to one Manhattan Project worth of money. Meta must expect a comparable return on that investment. But with that kind of money, that return must be disruptive.

And yet, AI does not feel disruptive. In my life, I have witnessed a few consumer technology disruptions: portable computers, portable telephones, internet-connected "smart" telephones, always-available GPS. Perhaps even tablet computers, smart watches, and electric cars? They all felt exciting! They all felt immediately obviously useful, if perhaps not always to me. But AI does not excite me. So if it's not for me, where is that $30B market that Meta is envisioning?

The best I can think of is a "command line for the common man". But the power of the command line comes from unflinchingly powerful commands, and behaving deterministically. Both of these characteristics are the antithesis of current AI technology.

We will not give an AI assistant the power to execute "format my hard drive", or "delete my Google account", even though a command line interface clearly would. Yet without that power, the AI assistant is toothless, and less useful. Even if we did, how could we trust the AI assistant to actually do what we want, and not misunderstand us? When interacting with LLMs, I am reminded of Terry Pratchett's gods of the Discworld, who wield absolute power, but you don't want to ask them for help as they're too likely to do what they think you wanted instead of what you actually asked.

But without power, and without deterministic behavior, you can't have a command line experience.

I keep coming back to that question: What is the disruptive use case of AI? Sure, we'll outsource some tasks to AI that we'd previously outsource overseas. This will result in rampant tragedy, and is already known to be difficult to pull off successfully. We'll enhance many tasks with short snippets of AI, to speed up menial programming tasks, and writing tasks, translation, image generation. But that's a feature-addition to existing software, not a disruption, let alone a $30B disruption.

Perhaps I'm wrong. Only time will tell. But I hereby predict that disruptive AI is a bubble.