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>
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""Which nodes make up which flow.
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Membership is this list, not the file a function sits in: `fit` is declared in
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`train.py` and used by both flows below, once as itself and once rewired and
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reconfigured. Wiring is not membership either — nodes connect because one
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provides a message another requires, never because one imported the other.
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fluksio sync examples/myresearch
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fluksio run train --lr 0.05 --wait
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"""
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from __future__ import annotations
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from fluksio import Flow, Port, use
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from myresearch.data import augment, make_rows, prepare
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from myresearch.evaluate import evaluate, score
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from myresearch.train import fit, train_curve
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train = Flow(
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"train",
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title="Train",
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nodes=[prepare, fit, evaluate],
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inputs=[Port("lr", "float", initial=0.01)],
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outputs=["score", "final_loss"],
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)
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finetune = Flow(
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"finetune",
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title="Finetune",
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nodes=[
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prepare,
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augment,
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# The same function, reading `augmented` instead of `dataset` and with
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# a shorter schedule. `train` is unaffected.
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use(fit, wire={"dataset": "augmented"}, epochs=3),
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evaluate,
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],
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inputs=[Port("lr", "float", initial=0.0001)],
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outputs=["score"],
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)
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if __name__ == "__main__":
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# Run the research here, with no engine involved. Not the node bodies: they
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# save and load artifacts, and there is nothing to save to out here. The
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# functions they wrap are ordinary Python, which is why they are worth
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# keeping separate — the arithmetic stays yours to run by hand.
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data = make_rows(limit=64)
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print("rows:", len(data["rows"]))
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print("losses:", [round(loss, 4) for loss in train_curve(0.05, 5)])
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print("score:", score(0.05, 5))
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