Model backends

Looking for usage documentation? Check out Relational predictions.

`TabPFNRelModel`

Shared tabpfn-rel implementation; the registered variants pin the backend.

`TabPFNRelModel.fit`

Build DFS features, apply the enabled extras, then fit the TFM. The explicitly constructed CacheConfig determines whether DFS reads, fills, or computes privately; this model does not override that policy.

TabPFNRelModel.fit(
    task: EntityTask,
    db: Database,
    train_table: Table,
    val_table: Table | None,
    *,
    seed: int,
    time_limit: float | None = None,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`val_table`	`Table \| None`	Required	—
`seed`	`int`	Required	—
`time_limit`	`float \| None`	`None`	—

Returns

Type	Description
`None`	—

`TabPFNRelModel.predict`

View source

Build + extend DFS features for table and return TFM predictions.

TabPFNRelModel.predict(
    task: EntityTask,
    db: Database,
    table: Table,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`table`	`Table`	Required	—

Returns

Type	Description
`np.ndarray`	—

`TabPFNRelModel.warm_cache`

View source

Populate the feature cache for this config’s fit + predict, without the TFM. Runs exactly the build_dfs_features + feature-pipeline calls fit and predict make (same cache keys), so a later eval reads them instead of recomputing. Construct the model with an explicit fill config; needs only CPU. The shared public command is relarena_core.featurization.warm_cache.

TabPFNRelModel.warm_cache(
    task: EntityTask,
    db: Database,
    train_table: Table,
    eval_table: Table,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`eval_table`	`Table`	Required	—

Returns

Type	Description
`None`	—

`TabPFNRelClientModel`

View source

tabpfn-rel through the hosted TabPFN API, with raw anchor-table text.

`TabPFNRelClientModel.fit`

View source

TabPFNRelClientModel.fit(
    task: EntityTask,
    db: Database,
    train_table: Table,
    val_table: Table | None,
    *,
    seed: int,
    time_limit: float | None = None,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`val_table`	`Table \| None`	Required	—
`seed`	`int`	Required	—
`time_limit`	`float \| None`	`None`	—

Returns

Type	Description
`None`	—

`TabPFNRelClientModel.predict`

View source

Build + extend DFS features for table and return TFM predictions.

TabPFNRelClientModel.predict(
    task: EntityTask,
    db: Database,
    table: Table,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`table`	`Table`	Required	—

Returns

Type	Description
`np.ndarray`	—

`TabPFNRelClientModel.warm_cache`

View source

TabPFNRelClientModel.warm_cache(
    task: EntityTask,
    db: Database,
    train_table: Table,
    eval_table: Table,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`eval_table`	`Table`	Required	—

Returns

Type	Description
`None`	—

`TabPFNRelLocalModel`

View source

tabpfn-rel on the local TabPFN v3, without text features.

`TabPFNRelLocalModel.fit`

View source

TabPFNRelLocalModel.fit(
    task: EntityTask,
    db: Database,
    train_table: Table,
    val_table: Table | None,
    *,
    seed: int,
    time_limit: float | None = None,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`val_table`	`Table \| None`	Required	—
`seed`	`int`	Required	—
`time_limit`	`float \| None`	`None`	—

Returns

Type	Description
`None`	—

`TabPFNRelLocalModel.predict`

View source

Build + extend DFS features for table and return TFM predictions.

TabPFNRelLocalModel.predict(
    task: EntityTask,
    db: Database,
    table: Table,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`table`	`Table`	Required	—

Returns

Type	Description
`np.ndarray`	—

`TabPFNRelLocalModel.warm_cache`

View source

TabPFNRelLocalModel.warm_cache(
    task: EntityTask,
    db: Database,
    train_table: Table,
    eval_table: Table,
) -> None

Parameters

Parameter	Type	Default	Description
`task`	`EntityTask`	Required	—
`db`	`Database`	Required	—
`train_table`	`Table`	Required	—
`eval_table`	`Table`	Required	—

Returns

Type	Description
`None`	—

Reference

tabpfn

tabpfn-client

tabpfn-extensions

tabpfn-rel

REST API

`TabPFNRelModel`

`TabPFNRelModel.fit`

`TabPFNRelModel.predict`

`TabPFNRelModel.warm_cache`

`TabPFNRelClientModel`

`TabPFNRelClientModel.fit`

`TabPFNRelClientModel.predict`

`TabPFNRelClientModel.warm_cache`

`TabPFNRelLocalModel`

`TabPFNRelLocalModel.fit`

`TabPFNRelLocalModel.predict`

`TabPFNRelLocalModel.warm_cache`

​TabPFNRelModel

​TabPFNRelModel.fit

​TabPFNRelModel.predict

​TabPFNRelModel.warm_cache

​TabPFNRelClientModel

​TabPFNRelClientModel.fit

​TabPFNRelClientModel.predict

​TabPFNRelClientModel.warm_cache

​TabPFNRelLocalModel

​TabPFNRelLocalModel.fit

​TabPFNRelLocalModel.predict

​TabPFNRelLocalModel.warm_cache

`TabPFNRelModel`

`TabPFNRelModel.fit`

`TabPFNRelModel.predict`

`TabPFNRelModel.warm_cache`

`TabPFNRelClientModel`

`TabPFNRelClientModel.fit`

`TabPFNRelClientModel.predict`

`TabPFNRelClientModel.warm_cache`

`TabPFNRelLocalModel`

`TabPFNRelLocalModel.fit`

`TabPFNRelLocalModel.predict`

`TabPFNRelLocalModel.warm_cache`