For what it's worth, I'm interested in getting bohrium[1] (sucessor of distnumpy with GPU & cluster support) and sklearn to play along nicely to speedup some of the regression algorithms (especially gaussian process) because we have a decently sized cluster and can't compromise on accuracy. The documentation is a bit... lacking, but it shouldn't be too hard to do this.
[1] -- http://bohrium.bitbucket.org/ 2014-05-07 1:49 GMT-05:00 Andy <t3k...@gmail.com>: > Hi Michal. > Please direct all such questions to the sklearn mailing list or > stackoverflow. > I doubt there will be any integration of GPU computation into numpy in > the near future (or probably ever). > There is also no plan to integrate GPU acceleration into scikit-learn, > mostly because it introduces a lot of dependencies. > > If your problem is kernel SVMs, GPUs don't really help much anyhow. If > your dataset is large, I would suggest using the kernel approximation > module. > This example illustrates the use and the gains in speed: > http://scikit-learn.org/dev/auto_examples/plot_kernel_approximation.html#example-plot-kernel-approximation-py > > Best, > Andy > > > On 05/05/2014 11:20 PM, Michal Sourek wrote: >> Hi Andreas, >> with regards to your long track of Scikit-learn development, >> let me raise one issue from code-execution point of view. >> >> Scikit has many marvelous tools. >> >> My primary concern is related to Supervised Learning of SVM-based >> Classifier. >> >> In the initial coarse optimisation scan over a non-convex problem, >> a GridSearchCV() use on the real-world dataset >> has rather long code-execution times, >> that even off-peak shifts scheduled onto available CPU/Cores >> resource-pool, >> available over weekend DataCenter shifts, >> cannot handle. >> >> Would indeed appreciate your comments, ideas or directions about the >> possible scikit-learn acceleration strategies available or >> possible-in-principle in this context. >> >> Are there, upon your knowledge, any R&D activities running in this >> direction? >> >> Have not found any promising candidates anywhere near the Scikit-learn >> framework. Also not aware about any industry-group spin-off aiming >> onto the gap between the raw, low level GPU/CUDA resources to bridge >> the very distance up to the Scikit-learn tools. >> >> Thought of a primitive mezzanine-approach for allowing minimum >> Scikit-learn integration efforts, >> i.e. to add the GPU-enabling layer into the Numpy, supposing all the >> Scikit-learn code uses vectorised Numpy services, >> to allow Numpy to call transparently GPU-services in case >> GPU-resources are detected on the <localhost> >> and to bypass the calls to CPU on such <localhost> that has no GPU >> resources available, which seems to be already available in iPython, >> if I remember well, >> but >> some GPU-grid approach is a principally better architecture for this. >> >> Appreciate your time, you´ve spent Andreas on reading this -- thank you. >> >> Remaining with respect, >> Michal Sourek > > > ------------------------------------------------------------------------------ > Is your legacy SCM system holding you back? Join Perforce May 7 to find out: > • 3 signs your SCM is hindering your productivity > • Requirements for releasing software faster > • Expert tips and advice for migrating your SCM now > http://p.sf.net/sfu/perforce > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Is your legacy SCM system holding you back? Join Perforce May 7 to find out: • 3 signs your SCM is hindering your productivity • Requirements for releasing software faster • Expert tips and advice for migrating your SCM now http://p.sf.net/sfu/perforce _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
