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Which is the difference between 'multi-gpu' and 'parallel-gpu' in 'trainingOption' function of the DeepLearning Toolbox?

7 Ansichten (letzte 30 Tage)
Hi everyone,
I have two NVIDIA RTX 3060 installed on my local computer and I want to train a neural network in parallel on both GPUs. I am worried about which is the best strategy between 'multi-gpu' and 'parallel-gpu'. Does anyone know how they work and which is the difference between 'multi-gpu' and 'parallel-gpu'?
Thank you.

Akzeptierte Antwort

Matt J
Matt J am 22 Mai 2024
Bearbeitet: Matt J am 22 Mai 2024
According to the doc, 'parallel-gpu' has the additional capability of being able to use remote GPUs. Since that doesn't apply to the hardware environment you describe, you can probably use either one.

Weitere Antworten (1)

Joss Knight
Joss Knight am 14 Jun. 2024 um 23:30
The purpose of 'multi-gpu' is effectively to try to ensure you are using a local pool with numGpus workers, rather than needing to understand anything about configuring a cluster. So either can work, but multi-gpu will give you helpful errors if you're doing something you didn't intend.

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