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