# Compmake keeps calm and carries on: a fault-tolerant “make” for parallelizable batch Python computations

A few years ago, at a SLAM summer school in Oxford,
I attended a talk by Frank Dellaert about a scientist’s “secret weapons”.
I believe he was channeling his advisor Herb Simon
in saying that every scientist must have a secret weapon that give them an
edge against the others. (For the record, Frank’s secret weapon is OCaml.)
In this age where most science is highly computational and data-based, the secret weapons are often software tools. Compmake has become one of my secret weapons.

Compmake is a nonobtrusive module that provides Makefile--like facilities to Python programs, including familiar commands such as make and clean, zero-effort parallelization, caching of results (the program can be interrupted and restarted), a console interface, and many other goodies. I have been relying on it daily since a couple of years and recently I polished it enough that it can be used by others. Here's a quick introduction; see the webpage for the full documentation.

Compmake has been designed primarily for handling long computational-intensive batch processes that can be decomposed in smaller granular jobs. To use Compmake, you have to minimally modify your Python program, such that it can understand the processing layout. Basically, each function call of the kind y = f(x) becomes y = comp(f, x), like the following figure shows. Compmake takes care of the rest. This simple modification is enough to solve most of the frustrating problems that I personally encountered in software development.

To install Compmake, use pip install compmake. You can try the following examples using the demo example.py. One way to use Compmake is to use the compmake executable. If you write:

$compmake example -c make # runs serially  the module example will be imported and the jobs defined there using comp will be loaded in the DB. The command make passed with the -c switch executes the jobs serially. Parallel execution: To run jobs in parallel, use the parmake command: $ compmake example -c "parmake n=6"   # runs at most 6 in parallel


There are all sorts of configuration options for being nice to other users of the machine; for example, Compmake can be instructed not to start other jobs if the CPU or memory usage is already above a certain percentage:

$compmake --max_cpu_load=50 --max_mem_load=50 example -c "clean; parmake"  Console: A console is displayed if you just run: $ compmake example


Some useful commands are ls, make, clean, and parmake, which all do what you think they do. Write help for a list of all commands.

Selective remake: You can selectively remake part of the computations. For example, suppose that you modify the draw() function, and you want to rerun only the last step. You can achieve that by::

$compmake example -c "remake draw*"  Compmake will reuse part of the computations (func1 and func2) but it will redo the last step. Tolerance to failures: If some of the jobs fail (e.g., they throw an exception), compmake will go forward with the rest. To see this behavior, download the file example_fail.py, which defines jobs that fail. If you run:: $ compmake example_fail -c "parmake n=4"


you will see how Compmake completes all jobs that can be completed.

Give it a go, and let me know how it goes.