lru_cache是Python标准库functools中的一个函数装饰器,用于缓存函数的结果。当被装饰的函数再次被调用时,如果其参数与之前某次调用完全相同,则直接从缓存中返回结果,而不是重新执行函数体。这样可以显著提高程序的执行效率,特别是对于那些计算成本较高或IO密集型的函数。lru_cache使用最近最少使用(Least Recently Used,...
from functools import lru_cache不允许参数是list 1. 不允许参数是list
import logging import os import time from collections import defaultdict from functools import partial from pathlib import Path from typing import Callable import click import h5py import psutil import time import tqdm import yaml from collections import defaultdict from edges_io import io from functool...
lru_cache(maxsize=None) def _DEFAULT_CONFIG() -> str: path = _default_config_file_path("default_airflow.cfg") with open(path) as fh: return fh.read()@functools.lru_cache(maxsize=None) def _TEST_CONFIG() -> str: path = _default_config_file_path("default_test.cfg")...
@cache def web_lookup(url): return urllib.urlopen(url).read() Note: since python 3.2 there is a decorator for this in the standard library: functools.lru_cache. Factor-out temporary contexts # Saving the old, restoring the new old_context = getcontext().copy() getcontext().prec = 50...
brain_task_callback = functools.partial(self._run_tasks, sampler_idx=s_idx) if sampler.contains_partial_step: sampler_tasks += [asyncio.create_task( sampler.run_step(self.model) sampler.make_step( model=self.model, brain_task_callback=brain_task_callback, ) ) ] n_started += 1 elif ...