0
$\begingroup$

Say I have one server with 10 GPUs. I have a python program which detects available GPU and use all of them.

I have a couple of users who will run python (Machine learning or data mining) programs and use GPU.

I initially thought to use Hadoop, as I find Yarn is good at managing resources, including GPU, and YARN has certain scheduling strategies, like fair, FIFO, capacity.

I don't like hard-coded rules, eg. user1 can only use gpu1, user2 can only use gpu2.

I later find Hadoop seems to require the program written in map-reduce pattern, but my requirement is to run unmodified code as we run on Windows or local desktop, or modify as little as possible.

Which knowledge should I look at for running and scheduling python programs on a machine with multiple GPUs?

$\endgroup$
0

1 Answer 1

1
$\begingroup$

A popular solution used for job management on GPU environments is SLURM.

SLURM allows specifying the resources needed by a job (e.g. 2 CPUs, 2Gb of RAM, 4 GPUs) and it will be scheduled for execution when the needed resources are available.

A job can be any program or script.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.