Article on neural nets on orbital for python - #382
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EmilHvitfeldt
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Aug 18, 2026
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| Orbital's whole pitch is that a trained model becomes SQL, so a database can run predictions on its own, with no Python process anywhere near it. Until 0.6.0, "trained model" meant scikit-learn: pipelines, trees, linear models, all `.fit()` in Python and then turned into a `SELECT` statement. It never covered what a lot of teams are actually training now: PyTorch models, not scikit-learn pipelines. | ||
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| Orbital 0.6.0 closes that gap. A `torch.nn.Sequential` network, trained exactly the way you already train it, now compiles to the same kind of SQL a linear regression would. |
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Very good tying this back to linear regression!
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| Scikit-learn and PyTorch are both real and shipping today, which is enough on its own to call this "multiple frameworks." But the dependency story is already moving that direction: [issue #113](https://github.com/posit-dev/orbital/issues/113) proposes turning scikit-learn itself into an optional dependency, the same way PyTorch already is, so the core stops assuming any particular framework at all. | ||
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| There are more frameworks already in the works. I won't name them here, only that the architecture was built for exactly this. |
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I think this section is getting a little long, instead i think we would get a lot more bang for our buck if we move this to the end of the post as a "hope you like what you see, look forward to more expansions in the future"
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| The fix, `Optimizer.preserve_referenced_outputs()`, runs after every single node in the translation loop, for every translator, not just `MatMul` and `Add`, and checks how many times that node's output is actually referenced downstream. Referenced more than once, it gets materialized as a named column. Referenced once or not at all, it stays inlined, no extra column, no extra noise. | ||
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| None of this is new machinery either. Tree ensembles already lean on the same trick: `preserve()` materializes per-tree votes, or the whole ensemble's aggregated vote so it isn't re-emitted everywhere it's read, as real SQL columns. `preserve_referenced_outputs()` just applies that same idea automatically, after every step, instead of leaving it to each translator to do it by itself. |
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The same idea is used in a pipeline, instead of inlining everything we create the intermediary values
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@EmilHvitfeldt applied your feedback, let me know if it looks good to you |
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Article on the newly released Orbital for Python 0.6.0 with support for PyTorch neural networks.