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"""Parallel processing functionality for Annif""" |
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import multiprocessing |
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import multiprocessing.dummy |
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from collections import defaultdict |
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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): |
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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__(self, registry, project_ids, backend_params, limit, threshold): |
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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): |
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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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hits = project.suggest([doc.text], self.backend_params)[0] |
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filtered_hits[project_id] = hits.filter( |
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project.subjects, self.limit, self.threshold |
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) |
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return (filtered_hits, doc.subject_set) |
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def suggest_batch(self, batch): |
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filtered_hit_sets = defaultdict(list) |
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texts, subject_sets = zip(*[(doc.text, 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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hit_sets = project.suggest(texts, self.backend_params) |
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for hits in hit_sets: |
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filtered_hit_sets[project_id].append( |
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hits.filter(project.subjects, 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): |
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"""return a suitable multiprocessing pool class, and the correct jobs |
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argument for its constructor, 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_class = 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_class = multiprocessing.dummy.Pool |
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else: |
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pool_class = ctx.Pool |
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return n_jobs, pool_class |
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NatLibFi /
Annif
