I am only using the basic joblib functionality: Parallel(n_jobs=-1)(delayed(function)(arg) for arg in arglist) I am frequently getting the warning: UserWarn
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.ex
I get the following UserWarning when trying to cache results using joblib: from tempfile import mkdtemp cachedir = mkdtemp() from joblib import Memory memory =
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
I've been finding that joblib.Memory.cache results in unreliable caching when using dataframes as inputs to the decorated functions. Playing around, I found tha
I've been finding that joblib.Memory.cache results in unreliable caching when using dataframes as inputs to the decorated functions. Playing around, I found tha
I'm trying to parallelize the subsetting of a Python dictionary. The code below creates a new dictionary, positions_sub, based on if the keys in positions dicti
I have a large list of sentences (~7 millions), and I want to extract the nouns from them. I used joblib library to parallelize the extracting process, like in