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
