joblib

问题 I'm using python 3.6 on on Anaconda Jupyter notebooks platform. My pc uses win 8.1 as OS. I was trying to import PCA from sklearn using the following lines: import sklearn from sklearn import decomposition from sklearn.decomposition import PCA the third line returns a Module error: ModuleNotFoundError: No module named 'sklearn.utils._joblib' Strangely, I couldn't find any record on this error online! I'd appreciate any help. I copied the complete error message below: -------------------------

问题 This is a more general question about how to run "embarassingly paralllel" problems with python "schedulers" in a science environment. I have a code that is a Python/Cython/C hybrid (for this example I'm using github.com/tardis-sn/tardis .. but I have more such problems for other codes) that is internally OpenMP parallalized. It provides a single function that takes a parameter dictionary and evaluates to an object within a few hundred seconds running on ~8 cores ( result=fun(paramset,

问题 I have a relatively simple linear regression lambda in AWS. Each instance the function is called the logs display the following: /opt/python/sklearn/externals/joblib/_multiprocessing_helpers.py:38: UserWarning: [Errno 38] Function not implemented. joblib will operate in serial mode warnings.warn('%s. joblib will operate in serial mode' % (e,)) I suspect this is due to sklearn running on a lambda (i.e. 'serverless') and trying to determine it's multi-processing capabilities as per this

问题 I have a very long list of tweets (2 millions) and I use regexes to search and replace text in these tweets. I run this using a joblib.Parallel map ( joblib is the parallel backend used by scikit-learn). My problem is that I can see in Windows' Task Manager that my script does not use 100% of each CPU. It doesn't use 100% of the RAM nor the disk. So I don't understand why it won't go faster. There is probably synchronization delays somewhere but I can't find what nor where. The code: # file

问题 I have a python script that uses sklearn joblib to load a persistent model and perform prediction. The script runs fine when I run it under my username and when some other user tries to run the same script they get the error "ImportError: No module named numpy_pickle" I also copied the script to the other user home directory and run it from there and still same error and I also ran it from python shell and nothing changed. Here is what I run in the Python shell: from sklearn.externals import

问题 My Parallel uses: sdf = Parallel(n_jobs=3, verbose=1, pre_dispatch='1.5*n_jobs')(delayed(char_etl)(x,k1,k2,k3) for x in X) X, which is a list .char_etl, is a string match function. Versions that I'm using are: python 2.7.11, centos 6.7, joblib 0.11 Every time was stuck,like this: [Parallel(n_jobs=3)]: Done 89 tasks | elapsed: 10.9s [Parallel(n_jobs=3)]: Done 258 tasks | elapsed: 46.8s [Parallel(n_jobs=3)]: Done 552 tasks | elapsed: 1.7min [Parallel(n_jobs=3)]: Done 902 tasks | elapsed: 2.7min

问题 I am looking for a way of building a decorator @memoize that I can use in functions as follows: @memoize my_function(a, b, c): # Do stuff # result may not always be the same for fixed (a,b,c) return result Then, if I do: result1 = my_function(a=1,b=2,c=3) # The function f runs (slow). We cache the result for later result2 = my_function(a=1, b=2, c=3) # The decorator reads the cache and returns the result (fast) Now say that I want to force a cache update : result3 = my_function(a=1, b=2, c=3,

问题 I created the following function in python: def cross_validate(algorithms, data, labels, cv=4, n_jobs=-1): print "Cross validation using: " for alg, predictors in algorithms: print alg print # Compute the accuracy score for all the cross validation folds. scores = cross_val_score(alg, data, labels, cv=cv, n_jobs=n_jobs) # Take the mean of the scores (because we have one for each fold) print scores print("Cross validation mean score = " + str(scores.mean())) name = re.split('\(', str(alg))

问题 I'm using Joblib to cache results of a computationally expensive function in my python script. The function's input arguments and return values are numpy arrays. The cache works fine for a single run of my python script. Now I want to spawn multiple runs of my python script in parallel for sweeping some parameter in an experiment. (The definition of the function remains same across all the runs). Is there a way to share the joblib cache among multiple python scripts running in parallel? This

问题 I want to run a function in parallel, and wait until all parallel nodes are done, using joblib. Like in the example: from math import sqrt from joblib import Parallel, delayed Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in range(10)) But, I want that the execution will be seen in a single progressbar like with tqdm , showing how many jobs has been completed. How would you do that? 回答1: If your problem consists of many parts, you could split the parts into k subgroups, run each subgroup in