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Why joblib: project goals
=========================
Benefits of pipelines
---------------------
Pipeline processing systems can provide a set of useful features:
Data-flow programming for performance
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* **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
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* **Tracking of data and computations:** This enables the reproducibility of a
computational experiment.
* **Inspecting data flow:** Inspecting intermediate results helps
debugging and understanding.
.. topic:: But pipeline frameworks can get in the way
:class: warning
Joblib's philosophy is to keep the underlying algorithm code unchanged,
avoiding framework-style modifications.
Joblib's approach
-----------------
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.
Design choices
--------------
* 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**
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