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