Hi Andreas, I know that NVIDIA team is a wonderful team to reach out to, they respond quite quickly and help you along the way.
I am not quite sure about SPARK community leaders will be willing to allow the overall SPARK community to build native integrations with Deep Learning systems. ray.io being headed by Berkely labs will gain a wider adoption if they stop the SPARK community from building the native integrations and thus we end up using Ray for such integrations thus massively increasing the adoption of Ray. But I think that SPARK should be building the native integrations with Deep Learning libraries and not forced to depend on horovod, Ray, or other solutions. NVIDIA is trying to lead its efforts on this integration I think, and we should all congratulate them :) Regards, Gourav On Sun, Aug 1, 2021 at 6:28 PM Andreas Kunft <andreas.ku...@gmail.com> wrote: > Hi, > > @Sean: Since Spark 3.x, stage level resource scheduling is available: > https://databricks.com/session_na21/stage-level-scheduling-improving-big-data-and-ai-integration > > @Gourav: I'm using the latest version of Spark 3.1.2. I want to split the > two maps on different executors, as both the GPU function and the CPU > function take quite some time, > so it would be great to have element n being processed in the GPU function > while n + 1 is already computed in the CPU function. As a workaround, I > write the results of the > CPU task to a queue which is consumed by another job that executes the CPU > task. > > Do you have any idea, if resource assignment based scheduling for > functions is a planned feature for the future? > > Best > Andreas > > > On Sun, Aug 1, 2021 at 6:53 PM Gourav Sengupta <gourav.sengu...@gmail.com> > wrote: > >> Hi Andreas, >> >> just to understand the question first, what is it you want to achieve by >> breaking the map operations across the GPU and CPU? >> >> Also it will be wonderful to understand the version of SPARK you are >> using, and your GPU details a bit more. >> >> >> Regards, >> Gourav >> >> On Sat, Jul 31, 2021 at 9:57 AM Andreas Kunft <andreas.ku...@gmail.com> >> wrote: >> >>> I have a setup with two work intensive tasks, one map using GPU followed >>> by a map using only CPU. >>> >>> Using stage level resource scheduling, I request a GPU node, but would >>> also like to execute the consecutive CPU map on a different executor so >>> that the GPU node is not blocked. >>> >>> However, spark will always combine the two maps due to the narrow >>> dependency, and thus, I can not define two different resource requirements. >>> >>> So the question is: can I force the two map functions on different >>> executors without shuffling or even better is there a plan to enable this >>> by assigning different resource requirements. >>> >>> Best >>> >>
