I have access to a large CPU cluster that does not have GPUs. Is it possible to speed up YOLO training by parallelizing between multiple CPU nodes?
The docs say that device parameter specifies the computational device(s) for training: a single GPU (device=0), multiple GPUs (device=0,1), CPU (device=cpu), or MPS for Apple silicon (device=mps). What about multiple CPUs?

I have access to a large CPU cluster that does not have GPUs. Is it possible to speed up YOLO training by parallelizing between multiple CPU nodes?
The docs say that device parameter specifies the computational device(s) for training: a single GPU (device=0), multiple GPUs (device=0,1), CPU (device=cpu), or MPS for Apple silicon (device=mps). What about multiple CPUs?

• machine-learning
• deep-learning
• pytorch
• cluster-computing
• yolo

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Sorted by: Reset to default 0

You can use torch.set_num_threads(int) (docs) to control how many CPU processes pytorch uses to execute operations.