Oh I see, I missed that. You can specify at the stage level, nice. I think
you are more looking to break these operations into two stages. You can do
that with a persist or something - which has a cost but may work fine.
Does it actually help much with GPU utilization - in theory yes but
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
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
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
