Why joblib: project goals¶
Benefits of pipelines¶
Pipeline processing systems can provide a set of useful features:
Data-flow programming for performance¶
On-demand computing: in pipeline systems such as labView or VTK, calculations are performed as needed by the outputs and only when inputs change.
Transparent parallelization: a pipeline topology can be inspected to deduce which operations can be run in parallel (it is equivalent to purely functional programming).
Provenance tracking to understand the code¶
Tracking of data and computations: This enables the reproducibility of a computational experiment.
Inspecting data flow: Inspecting intermediate results helps debugging and understanding.
But pipeline frameworks can get in the way
Joblib’s philosophy is to keep the underlying algorithm code unchanged, avoiding framework-style modifications.
Functions are the simplest abstraction used by everyone. Pipeline jobs (or tasks) in Joblib are made of decorated functions.
Tracking of parameters in a meaningful way requires specification of data model. Joblib gives up on that and uses hashing for performance and robustness.
No dependencies other than Python
Robust, well-tested code, at the cost of functionality
Fast and suitable for scientific computing on big dataset without changing the original code
Only local imports: embed joblib in your code by copying it