Source code for kedro.runner.parallel_runner

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

import multiprocessing
import os
import sys
from collections import Counter
from concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait
from itertools import chain
from multiprocessing.managers import BaseProxy, SyncManager
from multiprocessing.reduction import ForkingPickler
from pickle import PicklingError
from typing import Any, Iterable

from pluggy import PluginManager

from kedro.framework.hooks.manager import (
from kedro.framework.project import settings
from import (
from kedro.pipeline import Pipeline
from kedro.pipeline.node import Node
from kedro.runner.runner import AbstractRunner, run_node

# see

class ParallelRunnerManager(SyncManager):
    """``ParallelRunnerManager`` is used to create shared ``MemoryDataset``
    objects as default data sets in a pipeline.

ParallelRunnerManager.register("MemoryDataset", MemoryDataset)

def _bootstrap_subprocess(
    package_name: str, logging_config: dict[str, Any] | None = None
) -> None:
    from kedro.framework.project import configure_logging, configure_project

    if logging_config:

def _run_node_synchronization(  # noqa: PLR0913
    node: Node,
    catalog: DataCatalog,
    is_async: bool = False,
    session_id: str | None = None,
    package_name: str | None = None,
    logging_config: dict[str, Any] | None = None,
) -> Node:
    """Run a single `Node` with inputs from and outputs to the `catalog`.

    A ``PluginManager`` instance is created in each subprocess because the
    ``PluginManager`` can't be serialised.

        node: The ``Node`` to run.
        catalog: A ``DataCatalog`` containing the node's inputs and outputs.
        is_async: If True, the node inputs and outputs are loaded and saved
            asynchronously with threads. Defaults to False.
        session_id: The session id of the pipeline run.
        package_name: The name of the project Python package.
        logging_config: A dictionary containing logging configuration.

        The node argument.

    if multiprocessing.get_start_method() == "spawn" and package_name:
        _bootstrap_subprocess(package_name, logging_config)

    hook_manager = _create_hook_manager()
    _register_hooks(hook_manager, settings.HOOKS)
    _register_hooks_entry_points(hook_manager, settings.DISABLE_HOOKS_FOR_PLUGINS)

    return run_node(node, catalog, hook_manager, is_async, session_id)

[docs] class ParallelRunner(AbstractRunner): """``ParallelRunner`` is an ``AbstractRunner`` implementation. It can be used to run the ``Pipeline`` in parallel groups formed by toposort. Please note that this `runner` implementation validates dataset using the ``_validate_catalog`` method, which checks if any of the datasets are single process only using the `_SINGLE_PROCESS` dataset attribute. """
[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 by creating a Manager. Args: max_workers: Number of worker processes to spawn. If not set, calculated automatically based on the pipeline configuration and CPU core count. On windows machines, the max_workers value cannot be larger than 61 and will be set to min(61, max_workers). is_async: If True, the node inputs and outputs are loaded and saved 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 SharedMemoryDataset for `ParallelRunner`. Raises: ValueError: bad parameters passed """ default_dataset_pattern = {"{default}": {"type": "SharedMemoryDataset"}} self._extra_dataset_patterns = extra_dataset_patterns or default_dataset_pattern super().__init__( is_async=is_async, extra_dataset_patterns=self._extra_dataset_patterns ) self._manager = ParallelRunnerManager() self._manager.start() # This code comes from the concurrent.futures library # if max_workers is None: # NOTE: `os.cpu_count` might return None in some weird cases. # max_workers = os.cpu_count() or 1 if sys.platform == "win32": max_workers = min(_MAX_WINDOWS_WORKERS, max_workers) self._max_workers = max_workers
def __del__(self) -> None: self._manager.shutdown() @classmethod def _validate_nodes(cls, nodes: Iterable[Node]) -> None: """Ensure all tasks are serialisable.""" unserialisable = [] for node in nodes: try: ForkingPickler.dumps(node) except (AttributeError, PicklingError): unserialisable.append(node) if unserialisable: raise AttributeError( f"The following nodes cannot be serialised: {sorted(unserialisable)}\n" f"In order to utilize multiprocessing you need to make sure all nodes " f"are serialisable, i.e. nodes should not include lambda " f"functions, nested functions, closures, etc.\nIf you " f"are using custom decorators ensure they are correctly decorated using " f"functools.wraps()." ) @classmethod def _validate_catalog(cls, catalog: DataCatalog, pipeline: Pipeline) -> None: """Ensure that all data sets are serialisable and that we do not have any non proxied memory data sets being used as outputs as their content will not be synchronized across threads. """ datasets = catalog._datasets unserialisable = [] for name, dataset in datasets.items(): if getattr(dataset, "_SINGLE_PROCESS", False): # SKIP_IF_NO_SPARK unserialisable.append(name) continue try: ForkingPickler.dumps(dataset) except (AttributeError, PicklingError): unserialisable.append(name) if unserialisable: raise AttributeError( f"The following data sets cannot be used with multiprocessing: " f"{sorted(unserialisable)}\nIn order to utilize multiprocessing you " f"need to make sure all data sets are serialisable, i.e. data sets " f"should not make use of lambda functions, nested functions, closures " f"etc.\nIf you are using custom decorators ensure they are correctly " f"decorated using functools.wraps()." ) memory_datasets = [] for name, dataset in datasets.items(): if ( name in pipeline.all_outputs() and isinstance(dataset, MemoryDataset) and not isinstance(dataset, BaseProxy) ): memory_datasets.append(name) if memory_datasets: raise AttributeError( f"The following data sets are memory data sets: " f"{sorted(memory_datasets)}\n" f"ParallelRunner does not support output to externally created " f"MemoryDatasets" ) def _set_manager_datasets(self, catalog: DataCatalog, pipeline: Pipeline) -> None: for dataset in pipeline.datasets(): try: catalog.exists(dataset) except DatasetNotFoundError: pass for name, ds in catalog._datasets.items(): if isinstance(ds, SharedMemoryDataset): ds.set_manager(self._manager) def _get_required_workers_count(self, pipeline: Pipeline) -> int: """ Calculate the max number of processes required for the pipeline, limit to the number of CPU cores. """ # 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_processes = len(pipeline.nodes) - len(pipeline.grouped_nodes) + 1 return min(required_processes, self._max_workers) 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: AttributeError: When the provided pipeline is not suitable for parallel execution. RuntimeError: If the runner is unable to schedule the execution of all pipeline nodes. Exception: In case of any downstream node failure. """ if not self._is_async: "Using synchronous mode for loading and saving data. Use the --async flag " "for potential performance gains." ) nodes = pipeline.nodes self._validate_catalog(catalog, pipeline) self._validate_nodes(nodes) self._set_manager_datasets(catalog, pipeline) 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) from kedro.framework.project import LOGGING, PACKAGE_NAME with ProcessPoolExecutor(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_synchronization, node, catalog, self._is_async, session_id, package_name=PACKAGE_NAME, logging_config=LOGGING, # type: ignore[arg-type] ) ) if not futures: if todo_nodes: debug_data = { "todo_nodes": todo_nodes, "done_nodes": done_nodes, "ready_nodes": ready, "done_futures": done, } debug_data_str = "\n".join( f"{k} = {v}" for k, v in debug_data.items() ) raise RuntimeError( f"Unable to schedule new tasks although some nodes " f"have not been run:\n{debug_data_str}" ) break # pragma: no cover done, futures = wait(futures, return_when=FIRST_COMPLETED) for future in done: node = future.result() done_nodes.add(node) # Decrement load counts, and release any datasets we # have finished with. This is particularly important # for the shared, default datasets we created above. 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)