Which is the difference between 'multi-gpu' and 'parallel-gpu' in 'trainingOption' function of the DeepLearning Toolbox?
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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.
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Joss Knight
on 14 Jun 2024
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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