Source code for kedro.runner.thread_runner

"""``ThreadRunner`` is an ``AbstractRunner`` implementation. It can
be used to run the ``Pipeline`` in parallel groups formed by toposort
using threads.
from __future__ import annotations

import warnings
from collections import Counter
from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from itertools import chain
from typing import Any

from pluggy import PluginManager

from import DataCatalog
from kedro.pipeline import Pipeline
from kedro.pipeline.node import Node
from kedro.runner.runner import AbstractRunner, run_node

[docs] class ThreadRunner(AbstractRunner): """``ThreadRunner`` is an ``AbstractRunner`` implementation. It can be used to run the ``Pipeline`` in parallel groups formed by toposort using threads. """
[docs] def __init__( self, max_workers: int | None = None, is_async: bool = False, extra_dataset_patterns: dict[str, dict[str, Any]] | None = None, ): """ Instantiates the runner. Args: max_workers: Number of worker processes to spawn. If not set, calculated automatically based on the pipeline configuration and CPU core count. is_async: If True, set to False, because `ThreadRunner` doesn't support loading and saving the node inputs and outputs asynchronously with threads. Defaults to False. extra_dataset_patterns: Extra dataset factory patterns to be added to the DataCatalog during the run. This is used to set the default datasets to MemoryDataset for `ThreadRunner`. Raises: ValueError: bad parameters passed """ if is_async: warnings.warn( "'ThreadRunner' doesn't support loading and saving the " "node inputs and outputs asynchronously with threads. " "Setting 'is_async' to False." ) default_dataset_pattern = {"{default}": {"type": "MemoryDataset"}} self._extra_dataset_patterns = extra_dataset_patterns or default_dataset_pattern super().__init__( is_async=False, extra_dataset_patterns=self._extra_dataset_patterns ) if max_workers is not None and max_workers <= 0: raise ValueError("max_workers should be positive") self._max_workers = max_workers
def _get_required_workers_count(self, pipeline: Pipeline) -> int: """ Calculate the max number of processes required for the pipeline """ # Number of nodes is a safe upper-bound estimate. # It's also safe to reduce it by the number of layers minus one, # because each layer means some nodes depend on other nodes # and they can not run in parallel. # It might be not a perfect solution, but good enough and simple. required_threads = len(pipeline.nodes) - len(pipeline.grouped_nodes) + 1 return ( min(required_threads, self._max_workers) if self._max_workers else required_threads ) def _run( self, pipeline: Pipeline, catalog: DataCatalog, hook_manager: PluginManager, session_id: str | None = None, ) -> None: """The abstract interface for running pipelines. Args: pipeline: The ``Pipeline`` to run. catalog: The ``DataCatalog`` from which to fetch data. hook_manager: The ``PluginManager`` to activate hooks. session_id: The id of the session. Raises: Exception: in case of any downstream node failure. """ nodes = pipeline.nodes load_counts = Counter(chain.from_iterable(n.inputs for n in nodes)) node_dependencies = pipeline.node_dependencies todo_nodes = set(node_dependencies.keys()) done_nodes: set[Node] = set() futures = set() done = None max_workers = self._get_required_workers_count(pipeline) with ThreadPoolExecutor(max_workers=max_workers) as pool: while True: ready = {n for n in todo_nodes if node_dependencies[n] <= done_nodes} todo_nodes -= ready for node in ready: futures.add( pool.submit( run_node, node, catalog, hook_manager, self._is_async, session_id, ) ) if not futures: assert not todo_nodes, (todo_nodes, done_nodes, ready, done) # noqa: S101 break done, futures = wait(futures, return_when=FIRST_COMPLETED) for future in done: try: node = future.result() except Exception: self._suggest_resume_scenario(pipeline, done_nodes, catalog) raise done_nodes.add(node)"Completed node: %s", "Completed %d out of %d tasks", len(done_nodes), len(nodes) ) # Decrement load counts, and release any datasets we # have finished with. for dataset in node.inputs: load_counts[dataset] -= 1 if ( load_counts[dataset] < 1 and dataset not in pipeline.inputs() ): catalog.release(dataset) for dataset in node.outputs: if ( load_counts[dataset] < 1 and dataset not in pipeline.outputs() ): catalog.release(dataset)