merlin.systems.dag.ops.workflow.TransformWorkflow#
- class merlin.systems.dag.ops.workflow.TransformWorkflow(workflow=None, sparse_max: Optional[dict] = None, max_batch_size: Optional[int] = None, label_columns: Optional[List[str]] = None, cats: Optional[List[str]] = None, conts: Optional[List[str]] = None)[source]#
Bases:
InferenceOperator
This operator takes a workflow and turns it into a ensemble operator so that we can execute feature engineering during ensemble on tritonserver.
- __init__(workflow=None, sparse_max: Optional[dict] = None, max_batch_size: Optional[int] = None, label_columns: Optional[List[str]] = None, cats: Optional[List[str]] = None, conts: Optional[List[str]] = None)[source]#
Creates a Transform Workflow operator for a target workflow.
- Parameters:
workflow (Nvtabular.Workflow) – The workflow to transform data in ensemble.
sparse_max (dict, optional) – Dictionary representing key(name)/val(max value) pairs of max sparsity, by default None
max_batch_size (int, optional) – Maximum batch size, by default None
label_columns (List[str], optional) – List of strings identifying the label columns, by default None
cats (List[str], optional) – List of strings identifying categorical columns, by default None
conts (List[str], optional) – List of string identifying continuous columns, by default None
Methods
__init__
([workflow, sparse_max, ...])Creates a Transform Workflow operator for a target workflow.
column_mapping
(col_selector)Compute which output columns depend on which input columns
compute_column_schema
(col_name, input_schema)compute_input_schema
(root_schema, ...)Given the schemas coming from upstream sources and a column selector for the input columns, returns a set of schemas for the input columns this operator will use
compute_output_schema
(input_schema, col_selector)Returns output schema of operator
compute_selector
(input_schema, selector[, ...])Provides a hook method for sub-classes to override to implement custom column selection logic.
create_node
(selector)_summary_
export
(path, input_schema, output_schema[, ...])Export the class object as a config and all related files to the user defined path.
from_model_registry
(registry, **kwargs)Loads the InferenceOperator from the provided ModelRegistry.
from_path
(path, **kwargs)Loads the InferenceOperator from the path where it was exported after training.
load_artifacts
(artifact_path)Load artifacts from disk required for operator function.
output_column_names
(col_selector)Given a set of columns names returns the names of the transformed columns this operator will produce
save_artifacts
(artifact_path)Save artifacts required to be reload operator state from disk
set_nvt_model_name
(nvt_model_name)transform
(col_selector, transformable)Run nvtabular workflow transformations.
validate_schemas
(parents_schema, ...[, ...])Provides a hook method that sub-classes can override to implement schema validation logic.
Attributes
dependencies
Defines an optional list of column dependencies for this operator.
dynamic_dtypes
export_name
Provides a clear common english identifier for this operator.
is_subgraph
label
output_dtype
output_properties
output_tags
scalar_shape
supported_formats
supports
Returns what kind of data representation this operator supports
- property nvt_model_name#
- compute_output_schema(input_schema: Schema, col_selector: ColumnSelector, prev_output_schema: Optional[Schema] = None) Schema [source]#
Returns output schema of operator
- transform(col_selector: ColumnSelector, transformable: Transformable) Transformable [source]#
Run nvtabular workflow transformations.
- Parameters:
col_selector (ColumnSelector) – Unused ColumunSelector input
transformable (Transformable) – Input features to model
- Returns:
workflow transform
- Return type:
Transformable