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>
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
"""Where the data comes from."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
|
|
import fluksio
|
|
from fluksio import Port, node
|
|
|
|
|
|
def make_rows(source: str = "builtin", limit: int = 512) -> dict:
|
|
"""The training set itself, with no Fluksio in it."""
|
|
return {
|
|
"source": source,
|
|
"rows": [{"x": index / limit, "y": (index % 7) / 7} for index in range(limit)],
|
|
}
|
|
|
|
|
|
@node(provides=[Port("dataset", "artifact"), Port("rows", "int")])
|
|
def prepare(source: str = "builtin", limit: int = 512):
|
|
"""Make the training set and store it, returning a reference to it.
|
|
|
|
`source` and `limit` have defaults and are not ports, so they become the
|
|
node's settings: the canvas can tune them without touching this file.
|
|
"""
|
|
payload = make_rows(source, limit)
|
|
blob = json.dumps(payload).encode()
|
|
return {
|
|
"dataset": fluksio.save_artifact(blob, "dataset.json", "application/json"),
|
|
"rows": len(payload["rows"]),
|
|
}
|
|
|
|
|
|
@node(requires=["dataset"], provides=[Port("augmented", "artifact")])
|
|
def augment(dataset, factor: int = 2):
|
|
"""Grow the training set, so `finetune` has something of its own."""
|
|
payload = json.loads(open(fluksio.load_artifact(dataset)).read())
|
|
payload["rows"] = payload["rows"] * factor
|
|
blob = json.dumps(payload).encode()
|
|
return {
|
|
"augmented": fluksio.save_artifact(blob, "augmented.json", "application/json")
|
|
}
|