keras + scikit-learn wrapper, appears to hang when GridSearchCV with n_jobs >1

user_1783398 0
Viewed 3778

UPDATE: I have to re-write this question as after some investigation I realise that this is a different problem.

Context: running keras in a gridsearch setting using the kerasclassifier wrapper with scikit learn. Sys: Ubuntu 16.04, libraries: anaconda distribution 5.1, keras 2.0.9, scikitlearn 0.19.1, tensorflow 1.3.0 or theano 0.9.0, using CPUs only.

Code: I simply used the code here for testing: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/, the second example 'Grid Search Deep Learning Model Parameters'. Pay attention to line 35, which reads:

grid = GridSearchCV(estimator=model, param_grid=param_grid)

Symptoms: When grid search uses more than 1 jobs (means cpus?), e.g.,, setting 'n_jobs' on the above line A to '2', line below:

grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=2)

will cause the code to hang indefinitely, either with tensorflow or theano, and there is no cpu usage (see attached screenshot, where 5 python processes were created but none is using cpu).

enter image description here

By debugging, it appears to be the following line with 'sklearn.model_selection._search' that causes problems:

line 648: for parameters, (train, test) in product(candidate_params,
                                               cv.split(X, y, groups)))

, on which the program hangs and cannot continue.

I would really appreciate some insights as to what this means and why this could happen.

Thanks in advance

neuraxle theano scikit-learn keras tensorflow
3 Answers

TL;DR Answer: You can't because your Keras model can't be serialized, and serialization is needed for parallelizing in Python with joblib.

This problem is much detailed here: https://www.neuraxle.org/stable/scikit-learn_problems_solutions.html#problem-you-can-t-parallelize-nor-save-pipelines-using-steps-that-can-t-be-serialized-as-is-by-joblib

The solution to parallelize your code is to make your Keras estimator serializable. This can be done using savers as described at the link above.

If you're lucky enough to be using TensorFlow v2's prebuilt Keras module, the following practical code sample will reveal to be useful to you as you'd practically just need to take the code and modify it with yours:

In this example, all the saving and loading code is all pre-written for you using Neuraxle-TensorFlow, and this makes it parallelizeable if you use Neuraxle's AutoML methods (e.g.: Neuraxle's grid search and Neuraxle's own parallelism things).

user_24769200
Related