vipr.plugins.inference.interfaces package

Submodules

vipr.plugins.inference.interfaces.data_loader module

class vipr.plugins.inference.interfaces.data_loader.DataLoaderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'data_loader'

load_data(**kwargs) → DataSet

Loads data from a specified path.

Parameters:

• data_path – Optional path to data file

• **kwargs – Additional parameters for data loading

Returns:

DataSet containing loaded data with x, y, errors, and metadata

vipr.plugins.inference.interfaces.model_builder module

class vipr.plugins.inference.interfaces.model_builder.ModelBuilderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'model_builder'

abstract build(package_dir, package)

Build and return an executable model from a model package directory.

Parameters:

• package_dir – Path to the unpacked model package directory

• package – Parsed ModelPackage object

Returns:

Executable model object

classmethod manifest_contract() → dict

Optional manifest contract metadata for bundle helpers.

vipr.plugins.inference.interfaces.model_loader module

class vipr.plugins.inference.interfaces.model_loader.ModelLoaderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'model_loader'

load_model(**kwargs)

Loads a model of the given type.

Parameters:

**kwargs – Additional parameters for model initialization

Returns:

The loaded model

vipr.plugins.inference.interfaces.postprocessor module

Postprocessor interface.

This file defines the interface for all postprocessor implementations.

class vipr.plugins.inference.interfaces.postprocessor.PostprocessorInterface(**kw: Any)

Bases: Interface

Interface for postprocessor implementations.

Postprocessors take prediction results and apply additional processing to refine or enhance the results.

class Meta

Bases: object

interface = 'postprocessor'

abstract postprocess(data: Any) → Any

Postprocess the prediction results.

Parameters:

data – Input data to postprocess (typically prediction results)

Returns:

Postprocessed results

Return type:

Any

vipr.plugins.inference.interfaces.predictor module

class vipr.plugins.inference.interfaces.predictor.PredictorInterface(**kw: Any)

Bases: Interface

Interface for predictor handlers with unified DataSet support.

class Meta

Bases: object

interface = 'predictor'

abstract predict(dataset, model: Any, params: dict[str, Any] | None = None) → dict[str, Any]

Make predictions using the model with unified DataSet interface.

Parameters:

• dataset – DataSet with batch-first format (batch_size, n_points)

• model – Model to use for prediction

• params – Additional parameters for prediction control

Returns:

dict with prediction results in batch format

Module contents

Inference-specific interface contracts.

class vipr.plugins.inference.interfaces.DataLoaderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'data_loader'

load_data(**kwargs) → DataSet

Loads data from a specified path.

Parameters:

• data_path – Optional path to data file

• **kwargs – Additional parameters for data loading

Returns:

DataSet containing loaded data with x, y, errors, and metadata

class vipr.plugins.inference.interfaces.ModelBuilderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'model_builder'

abstract build(package_dir, package)

Build and return an executable model from a model package directory.

Parameters:

• package_dir – Path to the unpacked model package directory

• package – Parsed ModelPackage object

Returns:

Executable model object

classmethod manifest_contract() → dict

Optional manifest contract metadata for bundle helpers.

class vipr.plugins.inference.interfaces.ModelLoaderInterface(**kw: Any)

Bases: Interface

class Meta

Bases: object

interface = 'model_loader'

load_model(**kwargs)

Loads a model of the given type.

Parameters:

**kwargs – Additional parameters for model initialization

Returns:

The loaded model

class vipr.plugins.inference.interfaces.PostprocessorInterface(**kw: Any)

Bases: Interface

Interface for postprocessor implementations.

Postprocessors take prediction results and apply additional processing to refine or enhance the results.

class Meta

Bases: object

interface = 'postprocessor'

abstract postprocess(data: Any) → Any

Postprocess the prediction results.

Parameters:

data – Input data to postprocess (typically prediction results)

Returns:

Postprocessed results

Return type:

Any

class vipr.plugins.inference.interfaces.PredictorInterface(**kw: Any)

Bases: Interface

Interface for predictor handlers with unified DataSet support.

class Meta

Bases: object

interface = 'predictor'

abstract predict(dataset, model: Any, params: dict[str, Any] | None = None) → dict[str, Any]

Make predictions using the model with unified DataSet interface.

Parameters:

• dataset – DataSet with batch-first format (batch_size, n_points)

• model – Model to use for prediction

• params – Additional parameters for prediction control

Returns:

dict with prediction results in batch format