stroblmeandClaude Opus 5
8f71b638b6
It claimed to run the pipeline with no engine involved, and could not: the node bodies it called save and load artifacts, which raise outside a node by design. Each node is now a thin wrapper over a plain function — make_rows, train_curve, score — and __main__ calls those, which is the split the sandbox already demonstrates and the one worth copying. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
25 lines
723 B
Python
25 lines
723 B
Python
"""Scoring what was trained."""
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from __future__ import annotations
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import json
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import fluksio
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from fluksio import Port, node
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def score(lr: float, epochs: int) -> float:
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"""How good the model came out, with no Fluksio in it."""
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return round(1 - lr / (1 + epochs), 4)
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@node(requires=["weights"], provides=Port("score", "float"))
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def evaluate(weights):
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"""Score the model, returning the number rather than a dict.
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`provides=Port(...)` — one port rather than a list of them — is the opt-in
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to a bare return: the generated node wraps it in the message it belongs to.
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"""
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trained = json.loads(open(fluksio.load_artifact(weights)).read())
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return score(trained["lr"], trained["epochs"])
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