joblib

来源： https://stackoverflow.com/questions/43200878/python-add-keyvalue-to-dictionary-in-parallelised-loop

来源： https://stackoverflow.com/questions/38483874/intermediate-results-from-joblib

来源： https://stackoverflow.com/questions/64107457/how-to-optimize-this-python-function-using-joblib-or-other-parallel-computation

问题 I'm currently trying to run a parallel process in python 3.5 using the joblib library with the multiprocessing backend. However, every time it runs I get this error: Process ForkServerPoolWorker-5: Traceback (most recent call last): File "/opt/anaconda3/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap self.run() File "/opt/anaconda3/lib/python3.5/multiprocessing/process.py", line 93, in run self._target(*self._args, **self._kwargs) File "/opt/anaconda3/lib/python3.5

问题 Is there a simple way to track the overall progress of a joblib.Parallel execution? I have a long-running execution composed of thousands of jobs, which I want to track and record in a database. However, to do that, whenever Parallel finishes a task, I need it to execute a callback, reporting how many remaining jobs are left. I've accomplished a similar task before with Python's stdlib multiprocessing.Pool, by launching a thread that records the number of pending jobs in Pool's job list.

问题 I am looking at the joblib examples but I can't figure out how to do a parallel for loop over a matrix. I am computing a pairwise distance metric between the rows of a matrix. So I was doing: N, _ = data.shape upper_triangle = [(i, j) for i in range(N) for j in range(i + 1, N)] dist_mat = np.zeros((N,N)) for (i, j) in upper_triangle: dist_mat[i,j] = dist_fun(data[i], data[j]) dist_mat[j,i] = dist_mat[i,j] where dist_fun takes two vectors and computes a distance. How can I make this loop

问题 I am looking at the joblib examples but I can't figure out how to do a parallel for loop over a matrix. I am computing a pairwise distance metric between the rows of a matrix. So I was doing: N, _ = data.shape upper_triangle = [(i, j) for i in range(N) for j in range(i + 1, N)] dist_mat = np.zeros((N,N)) for (i, j) in upper_triangle: dist_mat[i,j] = dist_fun(data[i], data[j]) dist_mat[j,i] = dist_mat[i,j] where dist_fun takes two vectors and computes a distance. How can I make this loop

问题 I am trying to load my saved model from s3 using joblib import pandas as pd import numpy as np import json import subprocess import sqlalchemy from sklearn.externals import joblib ENV = 'dev' model_d2v = load_d2v('model_d2v_version_002', ENV) def load_d2v(fname, env): model_name = fname if env == 'dev': try: model=joblib.load(model_name) except: s3_base_path='s3://sd-flikku/datalake/doc2vec_model' path = s3_base_path+'/'+model_name command = "aws s3 cp {} {}".format(path,model_name).split()

问题 I am trying to pickle a sklearn machine-learning model, and load it in another project. The model is wrapped in pipeline that does feature encoding, scaling etc. The problem starts when i want to use self-written transformers in the pipeline for more advanced tasks. Let's say I have 2 projects: train_project: it has the custom transformers in src.feature_extraction.transformers.py use_project: it has other things in src, or has no src catalog at all If in "train_project" I save the pipeline

问题 I’ve a function: def func(something): for j in tqdm(something): ... which is called by: joblib.Parallel(n_jobs=4)((joblib.delayed)(s) for s in something_else) Now, this creates 4 overlapping tqdm progress bars. Is it possible to get 4 separate ones that update independently? 回答1: EDIT: I was sent this discussion by a friend in which a much cleaner solution is provided. I wrote a quick performance test to make sure that the lock does not cause the threads to block each other. There was no