Hi Petr, to clarify a bit:
pylearn2 specifically comes with a script to convert a model trained on a GPU into a version that runs on the CPU. This doesn't work very well though and the documentation points that out too. According to the dev commens that is down to how Theano, the framework pylearn2 is based on, handles its shared variables und the CUDA variables need to be converted to Theano tensor variables. Just wanted to advise caution that this process working flawlessly isn't necessarily given in every NN lib. If Caffe does this well, then David shouldn't have any problem of course and this warning doesn't apply here. -Michael On Wed, Mar 2, 2016 at 8:57 AM, Petr Baudis <pa...@ucw.cz> wrote: > On Tue, Mar 01, 2016 at 02:51:03PM -0800, David Fotland wrote: >> > Also, if you are training on a GPU you can probably avoid a lot of >> > hassle if you expect to run it on a GPU as well. I don't know how other >> > NN implementations handle it, but the GPU-to-CPU conversion script that >> > comes with the Theano-based pylearn2 kit doesn't work very reliably. >> I'll keep it in mind. I'm using caffe, which has a compile-time flag, so >> I'm not sure it will work with GPU enabled on a machine without a GPU. > > I'm not sure about Michael's specific problem, but in my experience, > there is no trouble at all transferring stuff between CPU and GPU - your > model is, after all, just the weight matrices. In Caffe, you should > be able to switch between GPU and CPU completely freely. > > Petr Baudis > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
