stroblmeandClaude Fable 5
a38e2745eb
A data scientist keeps their code where it is and decorates it: `@node` declares a function's ports beside the function, `Flow(name, nodes=[...])` says which of them make a flow, and `use(fn, wire=..., **settings)` rebinds one for a single flow. `fluksio sync` uploads the document plus a generated import shim per node, so the store still holds a complete, runnable, git-versioned definition while the code it imports stays theirs. `fluksio login|run|runs` and `flow.submit().wait()` are the client half, over the run endpoints that already existed. Runs record the user repository's commit beside the store's, so "what code produced this number" is answerable on the side that now holds the code. - `fluksio/sdk/`: ports, decorators, the flow builder and its checks, the shim generator, an HTTP client and sync. Standard library only at import, so `from fluksio import node` in a training script pulls in no engine. - `FlowDef.origin` marks a flow code-defined; `Run.origin_commit` carries the repository's commit; `POST /modules/refresh` retires the workers without an install, which every sync calls — a worker holds the imported package in memory, so an edit to it is invisible until the process goes. - The canvas shows a generated body read-only and names the repository to edit instead; a body edited there stops the next sync rather than being discarded. - The worker's reporter carries inert `Port`, `node`, `use` and `Flow`, since the shim imports a module whose first line declares them. - `examples/myresearch` is the worked example, `make sync-example` uploads it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""The part that would be on the GPU."""
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from __future__ import annotations
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import json
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import math
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import fluksio
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from fluksio import Port, node
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@node(
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requires=["dataset", Port("lr", "float")],
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provides=[
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Port("loss", "float", stream=True),
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Port("weights", "artifact"),
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Port("final_loss", "float"),
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],
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device="gpu",
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device_policy="prefer",
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timeout=600,
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)
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def fit(dataset, lr, epochs=25):
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"""Train, reporting the loss as it goes.
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Yielding is the reporting: each one publishes on the `loss` port the
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instant it happens, and the run keeps every value as a series — which is
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why there is no `log_metric()` to call. `device="gpu"` with
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`device_policy="prefer"` sends this to a worker carrying that label when
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one is attached, and runs it here when none is.
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"""
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rows = json.loads(open(fluksio.load_artifact(dataset)).read())["rows"]
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loss = 1.0
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for epoch in range(epochs):
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loss = math.exp(-lr * epoch * 10) * (1 + 0.05 * (epoch % 3)) / (1 + lr)
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yield {"loss": loss}
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weights = json.dumps({"lr": lr, "epochs": epochs, "n": len(rows)}).encode()
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return {
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"weights": fluksio.save_artifact(weights, "weights.json", "application/json"),
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"final_loss": loss,
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}
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