A set of tools to use Python long-running function as pipeline jobs; output-value caching and execution logging.
Project description
Joblib provides a set of tools for long running Python functions as pipeline jobs:
transparent disk caching of the output values and lazy re-evaluation
logging of the execution
The original focus was on scientific-computing scripts, but any long-running succession of functions can profit from the tools provided by joblib.
Joblib came out of long-running data-analysis Python scripts. The long term vision is to provide tools for scientists to achieve better reproducibility when running jobs. However, Joblib can also be used to provide a light-weight make replacement.
The main problems identified are:
Rerunning over and over the same script as it is tuned, but commenting out steps, or uncommenting steps, as they are needed, as they take long to run.
Not ideal persistence model, too often hand-implemented by the scientist, which leads to people having a hard time resuming the job, eg after a crash.
The approach take by Joblib to address these problems is not to build a heavy framework and coerce user into using it. It strives to build a set of easy-to-use, light-weight tools that fit with the users’s mind of running a script, and not developing a library.
The tools that have been identified and developped so far are:
A make-like functionality. The goal is to separate a script in a set of steps, with well-defined inputs and outputs, that can be saved and reran only if necessary.
The functionalities described above will progressively acquire better logging mechanism to help track what has been ran, and capture I/O easily. In addition, Joblib will provide a few I/O primitives, to easily define define logging and display streams, and maybe provide a way of compiling a report, probably with some graphics captured from pylab plots, or anything else (here arises to need to define an easy API for a visualization mechanism in addition to the one defined for persistence). In the long run, we would like to be able to quickly inspect what has been run, and visualize the results to be able to compare multiple runs. This would try to achieve a virtual lab-book. Moreover, combined with the persistence model, the lab-book would also have the data stored.
As stated on the project page, currently the project is in alpha quality. I am testing heavily all the features, as I care more about robustness than having plenty of features. On the other side, I expect to be playing with the API and features for a while before I can figure out what is the right set of functionalities to expose.
The code is hosted on launchpad for the good reason that branching the project and publishing it along-side my branch is dead-easy. I suspect that some of the existing functionality (such as the make decorator) can already be useful.
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