Automated python arguments with Google Fire
Spend less time documenting and more time programming. We look at a couple simple apps that use the Python Fire library to create automated command line interfaces.
It’s the hottest thing to drop from Google for the open source community since TensorFlow .
Err well, it’s not quite as popular…
What’s interesting to me about the deep learning library TensorFlow is there were already a few popular deep learning libraries on the market (namely, Theano, Caffe and Torch) when it was released. Even still, TensorFlow immediately and persistently garnered interest, check out the stackoverflow activity:
Fire is similar in the sense that a bunch of perfectly good tools for automating command line interfaces (CLIs) already exist for python programmers . It will be interesting to see if Fire is adopted by a significant portion of the user base.
Update: After using Fire for a few weeks and comparing with other options such as Click, I’ve realized that Fire is more suited for rapid development as it lacks many features of other CLIs. However, new features are being developed. The initial release carried IPython around as a dependency, which lead to long load times for even simple scripts, but this is now an optional dependency!
A simple app with Fire
Fire can be installed using pip:
pip install fire
The documentation is on github. It contained an insightful note:
We can do this by calling
my_object can be a function, class or other object.
Now time to make the app. It will be a random sample generator. The
random_sample function will be computed via
Fire. As we’ll see, the values returned by the function are printed.
Just show me the code.
random_sample function is fed into the
Fire method, which automatically creates command line arguments for the function parameters. Instead of writing usage instructions for the script, the user can be instructed to run
python main.py -- --help which results in this output:
Here is an example of the script’s usage:
>>> python main.py 'Bohr, Schrodinger, Einstein, Dirac' --num-samples 4 --seed 1905
Choices: ('Bohr', 'Schrodinger', 'Einstein', 'Dirac')
Genreating 4 samples
Random seed: 1905
By the way, if you want to run the script as an executable (i.e.
python main.py) then add a line like this to the top:
and give the script permission to run with the command
chmod +x main.py
My favorite feature of Fire is that it can throw you into an IPython REPL environment. All you have to do it add
-- --interactive to the end of your script call. For example:
>>> python main.py 'Bohr, Schrodinger, Einstein, Dirac' --num-samples 4
1. Running main.py
2. Print output from main.py
3. Notice the modules and object that have been loaded?
4. Launching the IPython interpreter
5. Printing the results
The data returned from the function call has now been stored in memory, along with the function itself. Below we get the docstring and then run a new sampling computation.
The choice arguments are passed as a tuple, whereas they would be input as a string using the command line interface:
>>> python main.py 'Bohr, Schrodinger' --num-samples 2 --seed 3
Firing a class
You can feed in any object to the
Fire module and have the library build a CLI. Here is a an example where we use a class.
WikiPage class requests a wikipedia page and the
get_html_element module will try and find an html element on the page (note: always use a parsing library like BeautifulSoup to find html elements — not regex like me!).
Let’s look at the usage:
Passing a page argument results in another helpful usage message:
For example we can get the status code:
>>> python main.py --page https://en.wikipedia.org/wiki/History_of_quantum_mechanics status-code
Or generate a random page and check the URL:
>>> python main.py url
We can also call modules directly from the command line:
>>> python main.py --page https://en.wikipedia.org/wiki/History_of_quantum_mechanics get-html-element title
History of quantum mechanics - Wikipedia
Thanks for reading. You can find me on twitter @agalea91
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