My guess is that your model evaluations are too fast, and that you are
not getting the benefits of distributed computing as the overhead is
hiding them.
Anyhow, I don't think that this is ready for prime-time usage. It
probably requires tweeking and understanding the tradeoffs.
G
On Sun, Aug 07, 2016 at 09:25:47PM +0000, Vlad Ionescu wrote:
> I copy pasted the example in the link you gave, only made the search take a
> longer time. I used dask-ssh to setup worker nodes and a scheduler, then
> connected to the scheduler in my code.
> Tweaking the n_jobs parameters for the randomized search does not get any
> performance benefits. The connection to the scheduler seems to work, but
> nothing gets assigned to the workers, because the code doesn't scale.
> I am using scikit-learn 0.18.dev0
> Any ideas?
> Code and results are below. Only the n_jobs value was changed between
> executions. I printed an Executor assigned to my scheduler, and it reported
> 240
> cores.
> import distributed.joblib
> from joblib import Parallel, parallel_backend
> from sklearn.datasets import load_digits
> from sklearn.grid_search import RandomizedSearchCV
> from sklearn.svm import SVC
> import numpy as np
> digits = load_digits()
> param_space = {
> 'C': np.logspace(-6, 6, 100),
> 'gamma': np.logspace(-8, 8, 100),
> 'tol': np.logspace(-4, -1, 100),
> 'class_weight': [None, 'balanced'],
> }
> model = SVC(kernel='rbf')
> search = RandomizedSearchCV(model, param_space, cv=3, n_iter=1000, verbose=1,
> n_jobs=200)
> with parallel_backend('distributed', scheduler_host='my_scheduler:8786'):
> search.fit(digits.data, digits.target)
> Fitting 3 folds for each of 1000 candidates, totalling 3000 fits
> [Parallel(n_jobs=200)]: Done 4 tasks | elapsed: 0.5s
> [Parallel(n_jobs=200)]: Done 292 tasks | elapsed: 6.9s
> [Parallel(n_jobs=200)]: Done 800 tasks | elapsed: 16.1s
> [Parallel(n_jobs=200)]: Done 1250 tasks | elapsed: 24.8s
> [Parallel(n_jobs=200)]: Done 1800 tasks | elapsed: 36.0s
> [Parallel(n_jobs=200)]: Done 2450 tasks | elapsed: 49.0s
> [Parallel(n_jobs=200)]: Done 3000 out of 3000 | elapsed: 1.0min finished
> -------------------------------------
> Fitting 3 folds for each of 1000 candidates, totalling 3000 fits
> [Parallel(n_jobs=20)]: Done 10 tasks | elapsed: 0.5s
> [Parallel(n_jobs=20)]: Done 160 tasks | elapsed: 3.7s
> [Parallel(n_jobs=20)]: Done 410 tasks | elapsed: 8.6s
> [Parallel(n_jobs=20)]: Done 760 tasks | elapsed: 16.2s
> [Parallel(n_jobs=20)]: Done 1210 tasks | elapsed: 25.0s
> [Parallel(n_jobs=20)]: Done 1760 tasks | elapsed: 36.2s
> [Parallel(n_jobs=20)]: Done 2410 tasks | elapsed: 48.8s
> [Parallel(n_jobs=20)]: Done 3000 out of 3000 | elapsed: 1.0min finished
>
> On Sun, Aug 7, 2016 at 8:31 PM Gael Varoquaux <gael.varoqu...@normalesup.org>
> wrote:
> Parallel computing in scikit-learn is built upon on joblib. In the
> development version of scikit-learn, the included joblib can be extended
> with a distributed backend:
> http://distributed.readthedocs.io/en/latest/joblib.html
> that can distribute code on a cluster.
> This is still bleeding edge, but this is probably a direction that will
> see more development.
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--
Gael Varoquaux
Researcher, INRIA Parietal
NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
Phone: ++ 33-1-69-08-79-68
http://gael-varoquaux.info http://twitter.com/GaelVaroquaux
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