relflow

relflow builds PyTorch/Lightning models from nested records. Typed fields represent values; branches combine them into local contexts; decoders learn to predict selected fields from the available context.

Install

Python 3.12 or newer:

uv add relflow

Add relflow[text] for Hugging Face text encoders or relflow[serving] for the HTTP runtime.

Describe One Record

line_items:
  - sku: A12
    quantity: 2
    price: 19.99
  - sku: B07
    quantity: 1
    price: 45.50
returned: false

YAML makes the shape visible. Applications supply Arrow tables or eager Polars DataFrames with this structure.

import relflow as rf

model = rf.Model(
    name="order",
    d_model=64,
    n_layers=2,
    n_heads=4,
    batch_size=128,
    line_items=rf.Branch(
        length=32,
        sku=rf.Category(size=4096),
        quantity=rf.Number,
        price=rf.Number,
    ),
    returned=rf.Boolean(mask=True),
)

Parent keywords name each field. mask=True makes returned a supervised target whose value never enters the encoder. The branch builds line-item context before its reduced representation reaches the order root.

Train And Predict

train_table, validation_table, and request_table below are application-supplied Arrow tables. Prediction requests omit returned.

import lightning.pytorch as lit

model.optimizer = rf.adamw(learning_rate=1e-3)
data = rf.ArrowDataModule(
    model=model, train=train_table, validate=validation_table
)
trainer = lit.Trainer(max_epochs=30)
trainer.fit(model=model, datamodule=data)
predictions = model.predict(request_table)

For eager Polars DataFrames, use rf.PolarsDataModule with the same split arguments. predict returns an Arrow table; decoded results live under its predictions column at addresses such as order/returned.

Documentation

Docs use static examples and Typst model diagrams. Build them with make render; run make check-docs to validate the render in a temporary directory. Package checks use uv run pytest; synthetic learning checks use make proofs. See CONTRIBUTING.md for development conventions.

Community · Apache 2.0 license

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