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"""Parallel processing functionality for Annif""" |
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from __future__ import annotations |
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import multiprocessing |
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import multiprocessing.dummy |
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from typing import TYPE_CHECKING, Any |
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if TYPE_CHECKING: |
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from collections import defaultdict |
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from collections.abc import Iterator |
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from typing import Callable |
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from annif.corpus import Document, SubjectSet |
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from annif.registry import AnnifRegistry |
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from annif.suggestion import SuggestionBatch, SuggestionResult |
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# Start method for processes created by the multiprocessing module. |
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# A value of None means using the platform-specific default. |
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# Intended to be overridden in unit tests. |
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MP_START_METHOD = None |
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class BaseWorker: |
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"""Base class for workers that implement tasks executed via |
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multiprocessing. The init method can be used to store data objects that |
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are necessary for the operation. They will be stored in a class |
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attribute that is accessible to the static worker method. The storage |
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solution is inspired by this blog post: |
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https://thelaziestprogrammer.com/python/multiprocessing-pool-a-global-solution # noqa |
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""" |
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args = None |
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@classmethod |
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def init(cls, args) -> None: |
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cls.args = args # pragma: no cover |
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class ProjectSuggestMap: |
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"""A utility class that can be used to wrap one or more projects and |
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provide a mapping method that converts Document objects to suggestions. |
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Intended to be used with the multiprocessing module.""" |
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def __init__( |
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self, |
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registry: AnnifRegistry, |
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project_ids: list[str], |
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backend_params: defaultdict[str, Any] | None, |
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limit: int | None, |
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threshold: float, |
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) -> None: |
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self.registry = registry |
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self.project_ids = project_ids |
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self.backend_params = backend_params |
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self.limit = limit |
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self.threshold = threshold |
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def suggest(self, doc: Document) -> tuple[dict[str, SuggestionResult], SubjectSet]: |
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filtered_hits = {} |
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for project_id in self.project_ids: |
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project = self.registry.get_project(project_id) |
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batch = project.suggest([doc], self.backend_params) |
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filtered_hits[project_id] = batch.filter(self.limit, self.threshold)[0] |
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return (filtered_hits, doc.subject_set) |
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def suggest_batch( |
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self, batch |
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) -> tuple[dict[str, SuggestionBatch], Iterator[SubjectSet]]: |
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filtered_hit_sets = {} |
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subject_sets = [doc.subject_set for doc in batch] |
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for project_id in self.project_ids: |
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project = self.registry.get_project(project_id) |
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suggestion_batch = project.suggest(batch, self.backend_params) |
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filtered_hit_sets[project_id] = suggestion_batch.filter( |
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self.limit, self.threshold |
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) |
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return (filtered_hit_sets, subject_sets) |
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def get_pool(n_jobs: int) -> tuple[int | None, Callable]: |
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"""return a suitable constructor for multiprocessing pool class, and the correct |
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jobs argument for it, for the given amount of parallel jobs""" |
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ctx = multiprocessing.get_context(MP_START_METHOD) |
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if n_jobs < 1: |
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n_jobs = None |
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pool_constructor: Callable = ctx.Pool |
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elif n_jobs == 1: |
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# use the dummy wrapper around threading to avoid subprocess overhead |
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pool_constructor = multiprocessing.dummy.Pool |
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else: |
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pool_constructor = ctx.Pool |
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return n_jobs, pool_constructor |
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NatLibFi /
Annif
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