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.
import logging.config
import multiprocessing
import os
import pickle
import sys
from collections import Counter
from concurrent.futures import FIRST_COMPLETED, ProcessPoolExecutor, wait
from itertools import chain
from multiprocessing.managers import BaseProxy, SyncManager  # type: ignore
from multiprocessing.reduction import ForkingPickler
from pickle import PicklingError
from typing import Any, Dict, Iterable, Set

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

# see

class _SharedMemoryDataSet:
    """``_SharedMemoryDataSet`` a wrapper class for a shared MemoryDataSet in SyncManager.
    It is not inherited from AbstractDataSet class.

    def __init__(self, manager: SyncManager):
        """Creates a new instance of ``_SharedMemoryDataSet``,
        and creates shared memorydataset attribute.

            manager: An instance of multiprocessing manager for shared objects.

        self.shared_memory_dataset = manager.MemoryDataSet()  # type: ignore

    def __getattr__(self, name):
        # This if condition prevents recursive call when deserializing
        if name == "__setstate__":
            raise AttributeError()
        return getattr(self.shared_memory_dataset, name)

    def save(self, data: Any):
        """Calls save method of a shared MemoryDataSet in SyncManager."""
        except Exception as exc:  # pylint: disable=broad-except
            # Checks if the error is due to serialisation or not
            except Exception as exc:  # SKIP_IF_NO_SPARK
                raise DataSetError(
                    f"{str(data.__class__)} cannot be serialized. ParallelRunner "
                    "implicit memory datasets can only be used with serializable data"
                ) from exc
                raise exc

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

ParallelRunnerManager.register(  # pylint: disable=no-member
    "MemoryDataSet", MemoryDataSet

def _bootstrap_subprocess(package_name: str, conf_logging: Dict[str, Any]):
    # pylint: disable=import-outside-toplevel,cyclic-import
    from kedro.framework.project import configure_project


def _run_node_synchronization(  # pylint: disable=too-many-arguments
    node: Node,
    catalog: DataCatalog,
    is_async: bool = False,
    run_id: str = None,
    package_name: str = None,
    conf_logging: Dict[str, Any] = None,
) -> Node:
    """Run a single `Node` with inputs from and outputs to the `catalog`.
    `KedroSession` instance is activated in every subprocess because of Windows
    (and latest OSX with Python 3.8) limitation.
    Windows has no "fork", so every subprocess is a brand new process
    created via "spawn", hence the need to a) setup the logging, b) register
    the hooks, and c) activate `KedroSession` in every subprocess.

        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.
        run_id: The id of the pipeline run.
        package_name: The name of the project Python package.
        conf_logging: A dictionary containing logging configuration.

        The node argument.

    if multiprocessing.get_start_method() == "spawn" and package_name:  # type: ignore
        conf_logging = conf_logging or {}
        _bootstrap_subprocess(package_name, conf_logging)

    return run_node(node, catalog, is_async, run_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, is_async: bool = False): """ 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. Raises: ValueError: bad parameters passed """ super().__init__(is_async=is_async) self._manager = ParallelRunnerManager() self._manager.start() # pylint: disable=consider-using-with # 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): self._manager.shutdown()
[docs] def create_default_data_set( # type: ignore self, ds_name: str ) -> _SharedMemoryDataSet: """Factory method for creating the default data set for the runner. Args: ds_name: Name of the missing data set Returns: An instance of an implementation of _SharedMemoryDataSet to be used for all unregistered data sets. """ return _SharedMemoryDataSet(self._manager)
@classmethod def _validate_nodes(cls, nodes: Iterable[Node]): """Ensure all tasks are serializable.""" unserializable = [] for node in nodes: try: ForkingPickler.dumps(node) except (AttributeError, PicklingError): unserializable.append(node) if unserializable: raise AttributeError( f"The following nodes cannot be serialized: {sorted(unserializable)}\n" f"In order to utilize multiprocessing you need to make sure all nodes " f"are serializable, i.e. nodes should not include lambda " f"functions, nested functions, closures, etc.\nIf you " f"are using custom decorators ensure they are correctly using " f"functools.wraps()." ) @classmethod def _validate_catalog(cls, catalog: DataCatalog, pipeline: Pipeline): """Ensure that all data sets are serializable 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. """ data_sets = catalog._data_sets # pylint: disable=protected-access unserializable = [] for name, data_set in data_sets.items(): if getattr(data_set, "_SINGLE_PROCESS", False): # SKIP_IF_NO_SPARK unserializable.append(name) continue try: ForkingPickler.dumps(data_set) except (AttributeError, PicklingError): unserializable.append(name) if unserializable: raise AttributeError( f"The following data sets cannot be used with multiprocessing: " f"{sorted(unserializable)}\nIn order to utilize multiprocessing you " f"need to make sure all data sets are serializable, 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"using functools.wraps()." ) memory_data_sets = [] for name, data_set in data_sets.items(): if ( name in pipeline.all_outputs() and isinstance(data_set, MemoryDataSet) and not isinstance(data_set, BaseProxy) ): memory_data_sets.append(name) if memory_data_sets: raise AttributeError( f"The following data sets are memory data sets: " f"{sorted(memory_data_sets)}\n" f"ParallelRunner does not support output to externally created " f"MemoryDataSets" ) def _get_required_workers_count(self, pipeline: Pipeline): """ 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( # pylint: disable=too-many-locals,useless-suppression self, pipeline: Pipeline, catalog: DataCatalog, run_id: str = None ) -> None: """The abstract interface for running pipelines. Args: pipeline: The ``Pipeline`` to run. catalog: The ``DataCatalog`` from which to fetch data. run_id: The id of the run. 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. """ # pylint: disable=import-outside-toplevel,cyclic-import from kedro.framework.session.session import get_current_session nodes = pipeline.nodes self._validate_catalog(catalog, pipeline) self._validate_nodes(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() # type: Set[Node] futures = set() done = None max_workers = self._get_required_workers_count(pipeline) from kedro.framework.project import PACKAGE_NAME session = get_current_session(silent=True) # pylint: disable=protected-access conf_logging = session._get_logging_config() if session else None 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, run_id, package_name=PACKAGE_NAME, conf_logging=conf_logging, ) ) 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: try: node = future.result() except Exception: self._suggest_resume_scenario(pipeline, done_nodes) raise done_nodes.add(node) # decrement load counts and release any data sets we've finished with # this is particularly important for the shared datasets we create above for data_set in node.inputs: load_counts[data_set] -= 1 if ( load_counts[data_set] < 1 and data_set not in pipeline.inputs() ): catalog.release(data_set) for data_set in node.outputs: if ( load_counts[data_set] < 1 and data_set not in pipeline.outputs() ): catalog.release(data_set)