Why joblib: project goals

What pipelines bring us

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 for understanding the code

• Tracking of data and computations: to be able to fully reproduce a computational experiment: requires tracking of the data and operation implemented.
• Inspecting data flow: Inspecting intermediate results helps debugging and understanding.

But pipeline frameworks can get in the way

We want our code to look like the underlying algorithm, not like a software framework.

Joblib’s approach

Functions are the simplest abstraction used by everyone. Our pipeline jobs (or tasks) are made of decorated functions.

Tracking of parameters in a meaningful way requires specification of data model. We give up on that and use hashing for performance and robustness.

Design choices

• No dependencies other than Python
• Robust, well-tested code, at the cost of functionnality
• 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