Hi, The short answer is that I suggest you to continue to use multiple process for now. The GIL still cause problems. Using multiple threads or just one with asynchronous execution won't work well in many cases for now.
If you are ready to work with us inside their to make this work tell us! Out of curiosity, what other optimization, speed up did you expected? Fred Le jeu. 12 janv. 2017 21:02, Adam Stooke <[email protected]> a écrit : > Hi, > > I have a concept of how to implement data parallelism to utilize > multiple GPUs, and I'd appreciate any feedback before I start on this. > > > First, some background: > --I'm working within an established, fairly complex code base. It builds > NNs using Lasagne and computes gradients and other things to perform > somewhat sophisticated optimization. To the extent possible, I'd like to > not modify this, but only wrap it. (And for this reason I'm under the > impression that Platoon is not the solution?) > --All Theano functions are expectation values over big data batches, so > I'd like to split up the data and do the same computation on multiple GPUs > in parallel, and average the results (even with multiple GPUs the data will > probably be bigger than can fit, so using inputs instead of shareds for > this, to cycle through the data). > --I already have a version running with multiprocessing, and it performs > well for CPU-only parallelism, and for GPU parallelism. But the new GPU > backend and the prospect of running in only one python process, seems to > offer a much cleaner solution, which would be easier to maintain and more > immediately portable to other specific cases which I haven't yet > parallelized, and this is what I hope to achieve. > > > What I think I have to do is: go through the full construction process of > the NN and all related inference / training functions, once for each GPU > (context). That is, > > 1. Build a separate copy of the network, each with a different GPU as the ` > *target`* of the shared variables comprising the network parameters (I > think this can be done cleanly by providing Lasagne with an initialization > function for the weights, with the target already set in it.) > > 2. Build a separate copy of the rest of the graphs, with e.g. > `.transfer('dev0')` appended to each of the input variables, and re-build > functions with those inputs and NN of corresponding GPU, and while we're > here, also must put the corresponding `.transfer('dev0')` on all Theano > function outputs, so the functions can be run concurrently. > > 3. All functions must be compiled separately for each GPU. (Even if they > are all grouped together into one Theano function.) > > 4. Figure out how to use the collectives so that I can all_reduce whatever > outputs I need directly on the GPUs. (Possibly outputs need to be > re-factored as updates to GPU-bound shared variables.) > > > This is certainly do-able, although slightly painful. Is there another > way that I am missing?? Maybe some little trick using `givens` (I haven't > been able to get it). *If there was some way to take a Theano function > (pre-compilation) or graph and make a copy of it but assign a different > context, this would be helpful!* > > > The only part I'm nervous about, performance-wise, is when some part of a > computation ends up on the CPU for whatever reason... would Theano only > service one of those at a time, and potentially slow down? As opposed to > the multiprocessing approach where everything is definitely concurrent (and > I can pin the processes to non-overlapping subsets of the CPU cores to make > sure they don't interfere). > > > > Thanks, > Adam > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
