@<1524922424720625664:profile|TartLeopard58> @<1545216070686609408:profile|EnthusiasticCow4>
Notice that when you are spinning multiple agents on the same GPU, the Tasks should request the "correct" fractional GPU container, i.e. if they pick a "regular" no mem limit.
So something like
CLEARML_WORKER_NAME=host-gpu0a clearml-agent daemon --gpus 0 clearml/fractional-gpu:u22-cu12.3-2gb
CLEARML_WORKER_NAME=host-gpu0b clearml-agent daemon --gpus 0 clearml/fractional-gpu:u22-cu12.3-2gb
Also remeber to add --pid=host to your conf file extra_docker_arguments
None
That's great! I look forward to trying this out.
Is there currently a way to bind the same GPU to multiple queues? I believe the agent complains last time I tried (which was a bit ago).
@<1535069219354316800:profile|PerplexedRaccoon19>
is it in the OSS version too?
Yep, free of charge ❤
I’m also curious if it’s available to bind the same GPU to multiple queues.
@<1545216070686609408:profile|EnthusiasticCow4>
Is there currently a way to bind the same GPU to multiple queues? I believe the agent complains last time I tried (which was a bit ago)
run multiple agents on the same GPU,
CLEARML_WORKER_NAME=host-gpu0a clearml-agent daemon --gpus 0
CLEARML_WORKER_NAME=host-gpu0b clearml-agent daemon --gpus 0
How does it work with k8s?
You need to install the clearml-glue and them on the Task request the container, notice you need to preconfigure the clue with the correct Job YAML
How does it work with k8s? how can I request the two pods to sit on the same gpu?