The Article Recommendation Tool depends on scikit-learn, which has not been packaged for Python 3 in jessie. It is available in sid, though. To roll out ART in prod, python3-sklearn, python3-sklearn-lib, and python3-sklearn-pandas would need to be backported for jessie. This requires the assistance of someone in ops with packaging experience.
Description
Description
| Status | Subtype | Assigned | Task | ||
|---|---|---|---|---|---|
| Resolved | leila | T112321 [Epic] Article recommendation productization/integration | |||
| Declined | None | T133362 Backport python3-sklearn and python3-sklearn-lib from sid |
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I took a quick look at this, the source packages we'd need are: scikit-learn, joblib (dependency of the former) and sklearn-pandas. I think it'd make sense to upload them to jessie-backports once the packages and their deps are all in testing as per backports policy.
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Why is packaging necessary? With ORES, we are using sklearn via a wheel rather than a Deb package. Could that work for deployments here too?
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Per discussions in backlog grooming, there is no dependency on sklearn at the moment, however, for future experiments and developments, we may need this.
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I also think deb packaging for this is going town a long, unrecoverable rabbit hole, and would recommend a wheels setup similar to what we have for ORES.
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Per discussions in backlog grooming, there is no dependency on sklearn at the moment, however, for future experiments and developments, we may need this.
I'll remove the Blocked-on-Operations tag for now, then.