然后你可以像这样使用它并且一旦你完成就不要留下猴子修补代码: from math import sqrt from joblib import Parallel, delayed with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) 我认为这很棒,它看起来类似于 ...
delayed from threading import Thread n = 0 def fun(_): global n n += 1 return _ ** 2 def main(): def parallel(): nonlocal result result = Parallel(n_jobs=-1, backend='threading')(delayed(fun)(_) for _ in range(1024 ** 2)) result = None progress_bar['maximum']...
returnsuper(ProgressParallel, self).__call__(*args, **kwargs) defprint_progress(self): ifself._totalisNone: self._pbar.total = self.n_dispatched_tasks self._pbar.n = self.n_completed_tasks self._pbar.refresh() 使用的时候:
joblib.Parallel执行过程的跟踪 、、、 是否有一种简单的方法来跟踪执行的总体进度? 我有一个由数千个作业组成的长期运行的执行过程,我想在数据库中跟踪和记录这些作业。但是,为了做到这一点,每当并行完成任务时,我都需要它执行回调,报告剩余的作业数量。我以前在Python multiprocessing.Pool中完成了类似的任务,启动...
progress bar given as argument"""classTqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):def__call__(self,*args,**kwargs):tqdm_object.update(n=self.batch_size)returnsuper().__call__(*args,**kwargs)old_batch_callback=joblib.parallel.BatchCompletionCallBackjoblib.parallel.Batch...
16_tqdm_progress_bar 17_folium_map_1_different_map_tiles 18_folium_map_2_custom_icon 19_folium_map_3_heatmap 20_python_basemap_background 21_folium_map_4_draw_lines 22_joblib .ipynb_checkpoints Python Module - Joblib, parallel for loop.ipynb README.md 23_folium_map_5_overlay_imag...
16_tqdm_progress_bar 17_folium_map_1_different_map_tiles 18_folium_map_2_custom_icon 19_folium_map_3_heatmap 20_python_basemap_background 21_folium_map_4_draw_lines 22_joblib .ipynb_checkpoints Python Module - Joblib, parallel for loop.ipynb README.md 23_folium_map_5_overlay...
)的可迭代性的解决方案并不能真正监视执行过程。相反，我建议对Parallel进行子类化并重写print_progress()...
修改nth的伟大答案，允许动态标志使用TQDM或不使用TQDM，并提前指定总量，这样状态栏才能正确填充。
This week, I found a nice python module to do quick parallel computing - joblib. I used to do parallel computing using python Multiprocessing module. But for a quick dirty way to parallel for loop, joblib is a very nice tool! Here's an example....