Re: [HTCondor-users] Multi GPUs on multiple nodes

[Benedikt Riedel <briedel@xxxxxxxxxxxxxxxx>]

Are they doing data or model parallelism? This has a big effect on how this should be setup.

Benedikt

On Tue, Nov 21, 2023 at 11:19âAM Dudu Handelman <duduhandelman@xxxxxxxxxxx> wrote:

ThanksÂBenedikt.Â

They are currently using 8 gpus on a single server.Â

They are considering to expand for the model.Â

ThanksÂ

David

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From: HTCondor-users <htcondor-users-bounces@xxxxxxxxxxx> on behalf of Benedikt Riedel <briedel@xxxxxxxxxxxxxxxx>
Sent: Tuesday, November 21, 2023 6:27:46 PM
To: HTCondor-Users Mail List <htcondor-users@xxxxxxxxxxx>
Subject: Re: [HTCondor-users] Multi GPUs on multiple nodes

Few questions for clarification:

* Do they need more GPUs for their application than is on a single node, i.e. more than 4 or 8?

* Are they using model or data parallelism in their training?

Out of the box pytorch only uses the GPUs on a single machine. For cross-node you will need to use something like like pytorch distribute: https://pytorch.org/tutorials/beginner/dist_overview.html

In HTCondor this would require the parallel universe. I am not sure what the status of that with GPUs is.Â

Benedikt

On Tue, Nov 21, 2023 at 10:04âAM Dudu Handelman <duduhandelman@xxxxxxxxxxx> wrote:

Hi All,

My users using pytorch and considering using multi GPUs on multiple physical servers.

I think that pytorch is able to do that out of the box using tcp as a workers.

I wonder if anyone doing that on top of HTCondor?

Thanks

David.

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Benedikt Riedel

Global Computing Coordinator IceCube Neutrino Observatory

Technical Coordinator IceCube Neutrino Observatory

Computing Manager Wisconsin IceCube Particle Astrophysics Center

University of Wisconsin-Madison

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Benedikt Riedel

Global Computing Coordinator IceCube Neutrino Observatory

Technical Coordinator IceCube Neutrino Observatory

Computing Manager Wisconsin IceCube Particle Astrophysics Center

University of Wisconsin-Madison