Source code for towhee.engine.pipeline

# Copyright 2021 Zilliz. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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from towhee.dag.graph_repr import GraphRepr
from towhee.dataframe import DataFrame
from towhee.errors import NoSchedulerError, EmptyInputError
from towhee.engine.graph_context import GraphContext
from towhee.engine.task_scheduler import TaskScheduler

[docs]class Pipeline: """ The runtime pipeline context, include graph context, all dataframes. Args: graph_repr: (`str` or `towhee.dag.GraphRepr`) The graph representation either as a YAML-formatted string, or directly as an instance of `GraphRepr`. parallelism: (`int`) The parallelism parameter dictates how many copies of the graph context we create. This is likely a low number (1-4) for local engines, but may be much higher for cloud instances. """
[docs] def __init__(self, graph_repr: GraphRepr, parallelism: int = 1) -> None: self._parallelism = parallelism self.on_graph_finish_handlers = [] self._scheduler = None self._graph_count = 0 if isinstance(graph_repr, str): self._graph_repr = GraphRepr.from_yaml(graph_repr) else: self._graph_repr = graph_repr
@property def pipeline_type(self) -> int: return self._graph_repr.graph_type @property def parallelism(self) -> int: return self._parallelism @property def graph_repr(self) -> GraphRepr: return self._graph_repr @parallelism.setter def parallelism(self, val): if isinstance(val, int) and val > 0: self._parallelism = val else: raise ValueError('Parallelism value must be a positive integer') def register(self, scheduler: TaskScheduler): self._scheduler = scheduler
[docs] def __repr__(self) -> str: if self._graph_repr._ir is not None: return self._graph_repr._ir else: return object.__repr__(self)
[docs] def __call__(self, inputs: DataFrame) -> DataFrame: """ Process an input `DataFrame`. This function instantiates an output `DataFrame`; upon completion, individual `GraphContext` outputs are merged into this dataframe. Inputs are weaved through the input `DataFrame` for each `GraphContext` as follows (`parallelism = 3`): data[0] -> ctx[0] data[1] -> ctx[1] data[2] -> ctx[2] data[3] -> ctx[0] data[4] -> ctx[1] Args: inputs (`towhee.dataframe.DataFrame`): Input `DataFrame` (with potentially multiple rows) to process. Returns: (`towhee.dataframe.DataFrame`) Output `DataFrame` with ordering matching the input `DataFrame`. """ if not self._scheduler: raise NoSchedulerError('Pipeline not registered to a Scheduler.') assert self._parallelism == 1 graph_ctx = GraphContext(self._graph_count, self._graph_repr) self._graph_count += 1 self._scheduler.register(graph_ctx) input_data = inputs.get(0, 1) if input_data is None: raise EmptyInputError('Input data is empty') graph_ctx(input_data[0]) graph_ctx.join() return graph_ctx.result()